top_25_pandas_tricks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data School: My top 25 pandas tricks ([video](https://www.youtube.com/watch?v=RlIiVeig3hc&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=35))\n",
    "\n",
    "**See also:** \"21 more pandas tricks\" [video](https://www.youtube.com/watch?v=tWFQqaRtSQA&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=36) and [notebook](https://nbviewer.org/github/justmarkham/pandas-videos/blob/master/21_more_pandas_tricks.ipynb)\n",
    "\n",
    "- Watch the [complete pandas video series](https://www.dataschool.io/easier-data-analysis-with-pandas/)\n",
    "- Connect on [Twitter](https://twitter.com/justmarkham), [Facebook](https://www.facebook.com/DataScienceSchool/), and [LinkedIn](https://www.linkedin.com/in/justmarkham/)\n",
    "- Subscribe on [YouTube](https://www.youtube.com/dataschool?sub_confirmation=1)\n",
    "- Join the [email newsletter](https://www.dataschool.io/subscribe/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "1. <a href=\"#1.-Show-installed-versions\">Show installed versions</a>\n",
    "2. <a href=\"#2.-Create-an-example-DataFrame\">Create an example DataFrame</a>\n",
    "3. <a href=\"#3.-Rename-columns\">Rename columns</a>\n",
    "4. <a href=\"#4.-Reverse-row-order\">Reverse row order</a>\n",
    "5. <a href=\"#5.-Reverse-column-order\">Reverse column order</a>\n",
    "6. <a href=\"#6.-Select-columns-by-data-type\">Select columns by data type</a>\n",
    "7. <a href=\"#7.-Convert-strings-to-numbers\">Convert strings to numbers</a>\n",
    "8. <a href=\"#8.-Reduce-DataFrame-size\">Reduce DataFrame size</a>\n",
    "9. <a href=\"#9.-Build-a-DataFrame-from-multiple-files-(row-wise)\">Build a DataFrame from multiple files (row-wise)</a>\n",
    "10. <a href=\"#10.-Build-a-DataFrame-from-multiple-files-(column-wise)\">Build a DataFrame from multiple files (column-wise)</a>\n",
    "11. <a href=\"#11.-Create-a-DataFrame-from-the-clipboard\">Create a DataFrame from the clipboard</a>\n",
    "12. <a href=\"#12.-Split-a-DataFrame-into-two-random-subsets\">Split a DataFrame into two random subsets</a>\n",
    "13. <a href=\"#13.-Filter-a-DataFrame-by-multiple-categories\">Filter a DataFrame by multiple categories</a>\n",
    "14. <a href=\"#14.-Filter-a-DataFrame-by-largest-categories\">Filter a DataFrame by largest categories</a>\n",
    "15. <a href=\"#15.-Handle-missing-values\">Handle missing values</a>\n",
    "16. <a href=\"#16.-Split-a-string-into-multiple-columns\">Split a string into multiple columns</a>\n",
    "17. <a href=\"#17.-Expand-a-Series-of-lists-into-a-DataFrame\">Expand a Series of lists into a DataFrame</a>\n",
    "18. <a href=\"#18.-Aggregate-by-multiple-functions\">Aggregate by multiple functions</a>\n",
    "19. <a href=\"#19.-Combine-the-output-of-an-aggregation-with-a-DataFrame\">Combine the output of an aggregation with a DataFrame</a>\n",
    "20. <a href=\"#20.-Select-a-slice-of-rows-and-columns\">Select a slice of rows and columns</a>\n",
    "21. <a href=\"#21.-Reshape-a-MultiIndexed-Series\">Reshape a MultiIndexed Series</a>\n",
    "22. <a href=\"#22.-Create-a-pivot-table\">Create a pivot table</a>\n",
    "23. <a href=\"#23.-Convert-continuous-data-into-categorical-data\">Convert continuous data into categorical data</a>\n",
    "24. <a href=\"#24.-Change-display-options\">Change display options</a>\n",
    "25. <a href=\"#25.-Style-a-DataFrame\">Style a DataFrame</a>\n",
    "26. <a href=\"#Bonus:-Profile-a-DataFrame\">Bonus trick: Profile a DataFrame</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load example datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n",
    "movies = pd.read_csv('http://bit.ly/imdbratings')\n",
    "orders = pd.read_csv('http://bit.ly/chiporders', sep='\\t')\n",
    "orders['item_price'] = orders.item_price.str.replace('$', '').astype('float')\n",
    "stocks = pd.read_csv('http://bit.ly/smallstocks', parse_dates=['Date'])\n",
    "titanic = pd.read_csv('http://bit.ly/kaggletrain')\n",
    "ufo = pd.read_csv('http://bit.ly/uforeports', parse_dates=['Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Show installed versions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you need to know the pandas version you're using, especially when reading the pandas documentation. You can show the pandas version by typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.24.2'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But if you also need to know the versions of pandas' dependencies, you can use the `show_versions()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "INSTALLED VERSIONS\n",
      "------------------\n",
      "commit: None\n",
      "python: 3.7.3.final.0\n",
      "python-bits: 64\n",
      "OS: Darwin\n",
      "OS-release: 18.6.0\n",
      "machine: x86_64\n",
      "processor: i386\n",
      "byteorder: little\n",
      "LC_ALL: None\n",
      "LANG: en_US.UTF-8\n",
      "LOCALE: en_US.UTF-8\n",
      "\n",
      "pandas: 0.24.2\n",
      "pytest: None\n",
      "pip: 19.1.1\n",
      "setuptools: 41.0.1\n",
      "Cython: None\n",
      "numpy: 1.16.4\n",
      "scipy: None\n",
      "pyarrow: None\n",
      "xarray: None\n",
      "IPython: 7.5.0\n",
      "sphinx: None\n",
      "patsy: None\n",
      "dateutil: 2.8.0\n",
      "pytz: 2019.1\n",
      "blosc: None\n",
      "bottleneck: None\n",
      "tables: None\n",
      "numexpr: None\n",
      "feather: None\n",
      "matplotlib: 3.1.0\n",
      "openpyxl: None\n",
      "xlrd: None\n",
      "xlwt: None\n",
      "xlsxwriter: None\n",
      "lxml.etree: None\n",
      "bs4: None\n",
      "html5lib: None\n",
      "sqlalchemy: None\n",
      "pymysql: None\n",
      "psycopg2: None\n",
      "jinja2: 2.10.1\n",
      "s3fs: None\n",
      "fastparquet: None\n",
      "pandas_gbq: None\n",
      "pandas_datareader: None\n",
      "gcsfs: None\n"
     ]
    }
   ],
   "source": [
    "pd.show_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the versions of Python, pandas, NumPy, matplotlib, and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create an example DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that you want to demonstrate some pandas code. You need an example DataFrame to work with.\n",
    "\n",
    "There are many ways to do this, but my favorite way is to pass a dictionary to the DataFrame constructor, in which the dictionary keys are the column names and the dictionary values are lists of column values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   col one  col two\n",
       "0      100      300\n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame({'col one':[100, 200], 'col two':[300, 400]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you need a much larger DataFrame, the above method will require way too much typing. In that case, you can use NumPy's `random.rand()` function, tell it the number of rows and columns, and pass that to the DataFrame constructor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>0.422632</td>\n",
       "      <td>0.098952</td>\n",
       "      <td>0.471007</td>\n",
       "      <td>0.307562</td>\n",
       "      <td>0.503040</td>\n",
       "      <td>0.317663</td>\n",
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       "          0         1         2         3         4         5         6  \\\n",
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       "1  0.526786  0.258871  0.032577  0.635255  0.008315  0.827765  0.574318   \n",
       "2  0.114055  0.795156  0.144248  0.161738  0.624836  0.223252  0.492255   \n",
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       "\n",
       "          7  \n",
       "0  0.154178  \n",
       "1  0.781200  \n",
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.rand(4, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's pretty good, but if you also want non-numeric column names, you can coerce a string of letters to a list and then pass that list to the columns parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.rand(4, 8), columns=list('abcdefgh'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you might guess, your string will need to have the same number of characters as there are columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Rename columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the example DataFrame we created in the last trick:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col one</th>\n",
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      "text/plain": [
       "   col one  col two\n",
       "0      100      300\n",
       "1      200      400"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I prefer to use dot notation to select pandas columns, but that won't work since the column names have spaces. Let's fix this.\n",
    "\n",
    "The most flexible method for renaming columns is the `rename()` method. You pass it a dictionary in which the keys are the old names and the values are the new names, and you also specify the axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename({'col one':'col_one', 'col two':'col_two'}, axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best thing about this method is that you can use it to rename any number of columns, whether it be just one column or all columns.\n",
    "\n",
    "Now if you're going to rename all of the columns at once, a simpler method is just to overwrite the columns attribute of the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['col_one', 'col_two']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if the only thing you're doing is replacing spaces with underscores, an even better method is to use the `str.replace()` method, since you don't have to type out all of the column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three of these methods have the same result, which is to rename the columns so that they don't have any spaces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_one</th>\n",
       "      <th>col_two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_one  col_two\n",
       "0      100      300\n",
       "1      200      400"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, if you just need to add a prefix or suffix to all of your column names, you can use the `add_prefix()` method..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_col_one</th>\n",
       "      <th>X_col_two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X_col_one  X_col_two\n",
       "0        100        300\n",
       "1        200        400"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.add_prefix('X_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or the `add_suffix()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_one_Y</th>\n",
       "      <th>col_two_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_one_Y  col_two_Y\n",
       "0        100        300\n",
       "1        200        400"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.add_suffix('_Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Reverse row order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the drinks DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a dataset of average alcohol consumption by country. What if you wanted to reverse the order of the rows?\n",
    "\n",
    "The most straightforward method is to use the `loc` accessor and pass it `::-1`, which is the same slicing notation used to reverse a Python list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>333</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "192   Zimbabwe             64               18              4   \n",
       "191     Zambia             32               19              4   \n",
       "190      Yemen              6                0              0   \n",
       "189    Vietnam            111                2              1   \n",
       "188  Venezuela            333              100              3   \n",
       "\n",
       "     total_litres_of_pure_alcohol      continent  \n",
       "192                           4.7         Africa  \n",
       "191                           2.5         Africa  \n",
       "190                           0.1           Asia  \n",
       "189                           2.0           Asia  \n",
       "188                           7.7  South America  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc[::-1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if you also wanted to reset the index so that it starts at zero?\n",
    "\n",
    "You would use the `reset_index()` method and tell it to drop the old index entirely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>333</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0   Zimbabwe             64               18              4   \n",
       "1     Zambia             32               19              4   \n",
       "2      Yemen              6                0              0   \n",
       "3    Vietnam            111                2              1   \n",
       "4  Venezuela            333              100              3   \n",
       "\n",
       "   total_litres_of_pure_alcohol      continent  \n",
       "0                           4.7         Africa  \n",
       "1                           2.5         Africa  \n",
       "2                           0.1           Asia  \n",
       "3                           2.0           Asia  \n",
       "4                           7.7  South America  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc[::-1].reset_index(drop=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the rows are in reverse order but the index has been reset to the default integer index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Reverse column order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the previous trick, you can also use `loc` to reverse the left-to-right order of your columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>4.9</td>\n",
       "      <td>54</td>\n",
       "      <td>132</td>\n",
       "      <td>89</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>0.7</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>12.4</td>\n",
       "      <td>312</td>\n",
       "      <td>138</td>\n",
       "      <td>245</td>\n",
       "      <td>Andorra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>5.9</td>\n",
       "      <td>45</td>\n",
       "      <td>57</td>\n",
       "      <td>217</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continent  total_litres_of_pure_alcohol  wine_servings  spirit_servings  \\\n",
       "0      Asia                           0.0              0                0   \n",
       "1    Europe                           4.9             54              132   \n",
       "2    Africa                           0.7             14                0   \n",
       "3    Europe                          12.4            312              138   \n",
       "4    Africa                           5.9             45               57   \n",
       "\n",
       "   beer_servings      country  \n",
       "0              0  Afghanistan  \n",
       "1             89      Albania  \n",
       "2             25      Algeria  \n",
       "3            245      Andorra  \n",
       "4            217       Angola  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc[:, ::-1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colon before the comma means \"select all rows\", and the `::-1` after the comma means \"reverse the columns\", which is why \"country\" is now on the right side."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Select columns by data type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the data types of the drinks DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                     int64\n",
       "spirit_servings                   int64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say you need to select only the numeric columns. You can use the `select_dtypes()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beer_servings  spirit_servings  wine_servings  total_litres_of_pure_alcohol\n",
       "0              0                0              0                           0.0\n",
       "1             89              132             54                           4.9\n",
       "2             25                0             14                           0.7\n",
       "3            245              138            312                          12.4\n",
       "4            217               57             45                           5.9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.select_dtypes(include='number').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This includes both int and float columns.\n",
    "\n",
    "You could also use this method to select just the object columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent\n",
       "0  Afghanistan      Asia\n",
       "1      Albania    Europe\n",
       "2      Algeria    Africa\n",
       "3      Andorra    Europe\n",
       "4       Angola    Africa"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.select_dtypes(include='object').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can tell it to include multiple data types by passing a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.select_dtypes(include=['number', 'object', 'category', 'datetime']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also tell it to exclude certain data types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent\n",
       "0  Afghanistan      Asia\n",
       "1      Albania    Europe\n",
       "2      Algeria    Africa\n",
       "3      Andorra    Europe\n",
       "4       Angola    Africa"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.select_dtypes(exclude='number').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Convert strings to numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create another example DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_one</th>\n",
       "      <th>col_two</th>\n",
       "      <th>col_three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col_one col_two col_three\n",
       "0     1.1     4.4       7.7\n",
       "1     2.2     5.5       8.8\n",
       "2     3.3     6.6         -"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'col_one':['1.1', '2.2', '3.3'],\n",
    "                   'col_two':['4.4', '5.5', '6.6'],\n",
    "                   'col_three':['7.7', '8.8', '-']})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These numbers are actually stored as strings, which results in object columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "col_one      object\n",
       "col_two      object\n",
       "col_three    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to do mathematical operations on these columns, we need to convert the data types to numeric. You can use the `astype()` method on the first two columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "col_one      float64\n",
       "col_two      float64\n",
       "col_three     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype({'col_one':'float', 'col_two':'float'}).dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this would have resulted in an error if you tried to use it on the third column, because that column contains a dash to represent zero and pandas doesn't understand how to handle it.\n",
    "\n",
    "Instead, you can use the `to_numeric()` function on the third column and tell it to convert any invalid input into `NaN` values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.7\n",
       "1    8.8\n",
       "2    NaN\n",
       "Name: col_three, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df.col_three, errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you know that the `NaN` values actually represent zeros, you can fill them with zeros using the `fillna()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.7\n",
       "1    8.8\n",
       "2    0.0\n",
       "Name: col_three, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df.col_three, errors='coerce').fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can apply this function to the entire DataFrame all at once by using the `apply()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_one</th>\n",
       "      <th>col_two</th>\n",
       "      <th>col_three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_one  col_two  col_three\n",
       "0      1.1      4.4        7.7\n",
       "1      2.2      5.5        8.8\n",
       "2      3.3      6.6        0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.apply(pd.to_numeric, errors='coerce').fillna(0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one line of code accomplishes our goal, because all of the data types have now been converted to float:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "col_one      float64\n",
       "col_two      float64\n",
       "col_three    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Reduce DataFrame size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas DataFrames are designed to fit into memory, and so sometimes you need to reduce the DataFrame size in order to work with it on your system.\n",
    "\n",
    "Here's the size of the drinks DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Data columns (total 6 columns):\n",
      "country                         193 non-null object\n",
      "beer_servings                   193 non-null int64\n",
      "spirit_servings                 193 non-null int64\n",
      "wine_servings                   193 non-null int64\n",
      "total_litres_of_pure_alcohol    193 non-null float64\n",
      "continent                       193 non-null object\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 30.4 KB\n"
     ]
    }
   ],
   "source": [
    "drinks.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that it currently uses 30.4 KB.\n",
    "\n",
    "If you're having performance problems with your DataFrame, or you can't even read it into memory, there are two easy steps you can take during the file reading process to reduce the DataFrame size.\n",
    "\n",
    "The first step is to only read in the columns that you actually need, which we specify with the \"usecols\" parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Data columns (total 2 columns):\n",
      "beer_servings    193 non-null int64\n",
      "continent        193 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 13.6 KB\n"
     ]
    }
   ],
   "source": [
    "cols = ['beer_servings', 'continent']\n",
    "small_drinks = pd.read_csv('http://bit.ly/drinksbycountry', usecols=cols)\n",
    "small_drinks.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By only reading in these two columns, we've reduced the DataFrame size to 13.6 KB.\n",
    "\n",
    "The second step is to convert any object columns containing categorical data to the category data type, which we specify with the \"dtype\" parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Data columns (total 2 columns):\n",
      "beer_servings    193 non-null int64\n",
      "continent        193 non-null category\n",
      "dtypes: category(1), int64(1)\n",
      "memory usage: 2.3 KB\n"
     ]
    }
   ],
   "source": [
    "dtypes = {'continent':'category'}\n",
    "smaller_drinks = pd.read_csv('http://bit.ly/drinksbycountry', usecols=cols, dtype=dtypes)\n",
    "smaller_drinks.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By reading in the continent column as the category data type, we've further reduced the DataFrame size to 2.3 KB.\n",
    "\n",
    "Keep in mind that the category data type will only reduce memory usage if you have a small number of categories relative to the number of rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Build a DataFrame from multiple files (row-wise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that your dataset is spread across multiple files, but you want to read the dataset into a single DataFrame.\n",
    "\n",
    "For example, I have a small dataset of stock data in which each CSV file only includes a single day. Here's the first day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>31.50</td>\n",
       "      <td>14070500</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>112.52</td>\n",
       "      <td>21701800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>57.42</td>\n",
       "      <td>19189500</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Close    Volume Symbol\n",
       "0  2016-10-03   31.50  14070500   CSCO\n",
       "1  2016-10-03  112.52  21701800   AAPL\n",
       "2  2016-10-03   57.42  19189500   MSFT"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/stocks1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the second day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>113.00</td>\n",
       "      <td>29736800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>57.24</td>\n",
       "      <td>20085900</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>31.35</td>\n",
       "      <td>18460400</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Close    Volume Symbol\n",
       "0  2016-10-04  113.00  29736800   AAPL\n",
       "1  2016-10-04   57.24  20085900   MSFT\n",
       "2  2016-10-04   31.35  18460400   CSCO"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/stocks2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the third day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>57.64</td>\n",
       "      <td>16726400</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>31.59</td>\n",
       "      <td>11808600</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>113.05</td>\n",
       "      <td>21453100</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Close    Volume Symbol\n",
       "0  2016-10-05   57.64  16726400   MSFT\n",
       "1  2016-10-05   31.59  11808600   CSCO\n",
       "2  2016-10-05  113.05  21453100   AAPL"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/stocks3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could read each CSV file into its own DataFrame, combine them together, and then delete the original DataFrames, but that would be memory inefficient and require a lot of code.\n",
    "\n",
    "A better solution is to use the built-in glob module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can pass a pattern to `glob()`, including wildcard characters, and it will return a list of all files that match that pattern.\n",
    "\n",
    "In this case, glob is looking in the \"data\" subdirectory for all CSV files that start with the word \"stocks\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/stocks1.csv', 'data/stocks2.csv', 'data/stocks3.csv']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_files = sorted(glob('data/stocks*.csv'))\n",
    "stock_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "glob returns filenames in an arbitrary order, which is why we sorted the list using Python's built-in `sorted()` function.\n",
    "\n",
    "We can then use a generator expression to read each of the files using `read_csv()` and pass the results to the `concat()` function, which will concatenate the rows into a single DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>31.50</td>\n",
       "      <td>14070500</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>112.52</td>\n",
       "      <td>21701800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>57.42</td>\n",
       "      <td>19189500</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>113.00</td>\n",
       "      <td>29736800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>57.24</td>\n",
       "      <td>20085900</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>31.35</td>\n",
       "      <td>18460400</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>57.64</td>\n",
       "      <td>16726400</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>31.59</td>\n",
       "      <td>11808600</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>113.05</td>\n",
       "      <td>21453100</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Close    Volume Symbol\n",
       "0  2016-10-03   31.50  14070500   CSCO\n",
       "1  2016-10-03  112.52  21701800   AAPL\n",
       "2  2016-10-03   57.42  19189500   MSFT\n",
       "0  2016-10-04  113.00  29736800   AAPL\n",
       "1  2016-10-04   57.24  20085900   MSFT\n",
       "2  2016-10-04   31.35  18460400   CSCO\n",
       "0  2016-10-05   57.64  16726400   MSFT\n",
       "1  2016-10-05   31.59  11808600   CSCO\n",
       "2  2016-10-05  113.05  21453100   AAPL"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat((pd.read_csv(file) for file in stock_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, there are now duplicate values in the index. To avoid that, we can tell the `concat()` function to ignore the index and instead use the default integer index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>31.50</td>\n",
       "      <td>14070500</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>112.52</td>\n",
       "      <td>21701800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>57.42</td>\n",
       "      <td>19189500</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>113.00</td>\n",
       "      <td>29736800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>57.24</td>\n",
       "      <td>20085900</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>31.35</td>\n",
       "      <td>18460400</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>57.64</td>\n",
       "      <td>16726400</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>31.59</td>\n",
       "      <td>11808600</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>113.05</td>\n",
       "      <td>21453100</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Close    Volume Symbol\n",
       "0  2016-10-03   31.50  14070500   CSCO\n",
       "1  2016-10-03  112.52  21701800   AAPL\n",
       "2  2016-10-03   57.42  19189500   MSFT\n",
       "3  2016-10-04  113.00  29736800   AAPL\n",
       "4  2016-10-04   57.24  20085900   MSFT\n",
       "5  2016-10-04   31.35  18460400   CSCO\n",
       "6  2016-10-05   57.64  16726400   MSFT\n",
       "7  2016-10-05   31.59  11808600   CSCO\n",
       "8  2016-10-05  113.05  21453100   AAPL"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat((pd.read_csv(file) for file in stock_files), ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Build a DataFrame from multiple files (column-wise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous trick is useful when each file contains rows from your dataset. But what if each file instead contains columns from your dataset?\n",
    "\n",
    "Here's an example in which the drinks dataset has been split into two CSV files, and each file contains three columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings\n",
       "0  Afghanistan              0                0\n",
       "1      Albania             89              132\n",
       "2      Algeria             25                0\n",
       "3      Andorra            245              138\n",
       "4       Angola            217               57"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/drinks1.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wine_servings  total_litres_of_pure_alcohol continent\n",
       "0              0                           0.0      Asia\n",
       "1             54                           4.9    Europe\n",
       "2             14                           0.7    Africa\n",
       "3            312                          12.4    Europe\n",
       "4             45                           5.9    Africa"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/drinks2.csv').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the previous trick, we'll start by using `glob()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "drink_files = sorted(glob('data/drinks*.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this time, we'll tell the `concat()` function to concatenate along the columns axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat((pd.read_csv(file) for file in drink_files), axis='columns').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our DataFrame has all six columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Create a DataFrame from the clipboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that you have some data stored in an Excel spreadsheet or a [Google Sheet](https://docs.google.com/spreadsheets/d/1ipv_HAykbky8OXUubs9eLL-LQ1rAkexXG61-B4jd0Rc/edit?usp=sharing), and you want to get it into a DataFrame as quickly as possible.\n",
    "\n",
    "Just select the data and copy it to the clipboard. Then, you can use the `read_clipboard()` function to read it into a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column A</th>\n",
       "      <th>Column B</th>\n",
       "      <th>Column C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>seven</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>eight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>nine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column A  Column B Column C\n",
       "0         1       4.4    seven\n",
       "1         2       5.5    eight\n",
       "2         3       6.6     nine"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_clipboard()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like the `read_csv()` function, `read_clipboard()` automatically detects the correct data type for each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Column A      int64\n",
       "Column B    float64\n",
       "Column C     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's copy one other dataset to the clipboard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Left</th>\n",
       "      <th>Right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alice</th>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlie</th>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Left  Right\n",
       "Alice      10     40\n",
       "Bob        20     50\n",
       "Charlie    30     60"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_clipboard()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amazingly, pandas has even identified the first column as the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alice', 'Bob', 'Charlie'], dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep in mind that if you want your work to be reproducible in the future, `read_clipboard()` is not the recommended approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Split a DataFrame into two random subsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that you want to split a DataFrame into two parts, randomly assigning 75% of the rows to one DataFrame and the other 25% to a second DataFrame.\n",
    "\n",
    "For example, we have a DataFrame of movie ratings with 979 rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "979"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `sample()` method to randomly select 75% of the rows and assign them to the \"movies_1\" DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_1 = movies.sample(frac=0.75, random_state=1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can use the `drop()` method to drop all rows that are in \"movies_1\" and assign the remaining rows to \"movies_2\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_2 = movies.drop(movies_1.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the total number of rows is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "979"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies_1) + len(movies_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can see from the index that every movie is in either \"movies_1\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  0,   2,   5,   6,   7,   8,   9,  11,  13,  16,\n",
       "            ...\n",
       "            966, 967, 969, 971, 972, 974, 975, 976, 977, 978],\n",
       "           dtype='int64', length=734)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_1.index.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or \"movies_2\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,   3,   4,  10,  12,  14,  15,  18,  26,  30,\n",
       "            ...\n",
       "            931, 934, 937, 941, 950, 954, 960, 968, 970, 973],\n",
       "           dtype='int64', length=245)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_2.index.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep in mind that this approach will not work if your index values are not unique."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Filter a DataFrame by multiple categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the movies DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the columns is genre:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Crime', 'Action', 'Drama', 'Western', 'Adventure', 'Biography',\n",
       "       'Comedy', 'Animation', 'Mystery', 'Horror', 'Film-Noir', 'Sci-Fi',\n",
       "       'History', 'Thriller', 'Family', 'Fantasy'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wanted to filter the DataFrame to only show movies with the genre Action or Drama or Western, we could use multiple conditions separated by the \"or\" operator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
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       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
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       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
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       "      <th>5</th>\n",
       "      <td>8.9</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>96</td>\n",
       "      <td>[u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.9</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Western</td>\n",
       "      <td>161</td>\n",
       "      <td>[u'Clint Eastwood', u'Eli Wallach', u'Lee Van ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>139</td>\n",
       "      <td>[u'Brad Pitt', u'Edward Norton', u'Helena Bonh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Inception</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>148</td>\n",
       "      <td>[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    star_rating                           title content_rating    genre  \\\n",
       "3           9.0                 The Dark Knight          PG-13   Action   \n",
       "5           8.9                    12 Angry Men      NOT RATED    Drama   \n",
       "6           8.9  The Good, the Bad and the Ugly      NOT RATED  Western   \n",
       "9           8.9                      Fight Club              R    Drama   \n",
       "11          8.8                       Inception          PG-13   Action   \n",
       "\n",
       "    duration                                        actors_list  \n",
       "3        152  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "5         96  [u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...  \n",
       "6        161  [u'Clint Eastwood', u'Eli Wallach', u'Lee Van ...  \n",
       "9        139  [u'Brad Pitt', u'Edward Norton', u'Helena Bonh...  \n",
       "11       148  [u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[(movies.genre == 'Action') |\n",
    "       (movies.genre == 'Drama') |\n",
    "       (movies.genre == 'Western')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, you can actually rewrite this code more clearly by using the `isin()` method and passing it a list of genres:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
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       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
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       "      <td>8.9</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>96</td>\n",
       "      <td>[u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.9</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Western</td>\n",
       "      <td>161</td>\n",
       "      <td>[u'Clint Eastwood', u'Eli Wallach', u'Lee Van ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>139</td>\n",
       "      <td>[u'Brad Pitt', u'Edward Norton', u'Helena Bonh...</td>\n",
       "    </tr>\n",
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       "      <th>11</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Inception</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>148</td>\n",
       "      <td>[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    star_rating                           title content_rating    genre  \\\n",
       "3           9.0                 The Dark Knight          PG-13   Action   \n",
       "5           8.9                    12 Angry Men      NOT RATED    Drama   \n",
       "6           8.9  The Good, the Bad and the Ugly      NOT RATED  Western   \n",
       "9           8.9                      Fight Club              R    Drama   \n",
       "11          8.8                       Inception          PG-13   Action   \n",
       "\n",
       "    duration                                        actors_list  \n",
       "3        152  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "5         96  [u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...  \n",
       "6        161  [u'Clint Eastwood', u'Eli Wallach', u'Lee Van ...  \n",
       "9        139  [u'Brad Pitt', u'Edward Norton', u'Helena Bonh...  \n",
       "11       148  [u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[movies.genre.isin(['Action', 'Drama', 'Western'])].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you want to reverse this filter, so that you are excluding (rather than including) those three genres, you can put a tilde in front of the condition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.9</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>201</td>\n",
       "      <td>[u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                                          title content_rating  \\\n",
       "0          9.3                       The Shawshank Redemption              R   \n",
       "1          9.2                                  The Godfather              R   \n",
       "2          9.1                         The Godfather: Part II              R   \n",
       "4          8.9                                   Pulp Fiction              R   \n",
       "7          8.9  The Lord of the Rings: The Return of the King          PG-13   \n",
       "\n",
       "       genre  duration                                        actors_list  \n",
       "0      Crime       142  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1      Crime       175    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2      Crime       200  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "4      Crime       154  [u'John Travolta', u'Uma Thurman', u'Samuel L....  \n",
       "7  Adventure       201  [u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[~movies.genre.isin(['Action', 'Drama', 'Western'])].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works because tilde is the \"not\" operator in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Filter a DataFrame by largest categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that you needed to filter the movies DataFrame by genre, but only include the 3 largest genres.\n",
    "\n",
    "We'll start by taking the `value_counts()` of genre and saving it as a Series called counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama        278\n",
       "Comedy       156\n",
       "Action       136\n",
       "Crime        124\n",
       "Biography     77\n",
       "Adventure     75\n",
       "Animation     62\n",
       "Horror        29\n",
       "Mystery       16\n",
       "Western        9\n",
       "Sci-Fi         5\n",
       "Thriller       5\n",
       "Film-Noir      3\n",
       "Family         2\n",
       "Fantasy        1\n",
       "History        1\n",
       "Name: genre, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = movies.genre.value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Series method `nlargest()` makes it easy to select the 3 largest values in this Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama     278\n",
       "Comedy    156\n",
       "Action    136\n",
       "Name: genre, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.nlargest(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And all we actually need from this Series is the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Drama', 'Comedy', 'Action'], dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.nlargest(3).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can pass the index object to `isin()`, and it will be treated like a list of genres:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>152</td>\n",
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       "      <th>5</th>\n",
       "      <td>8.9</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>96</td>\n",
       "      <td>[u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>139</td>\n",
       "      <td>[u'Brad Pitt', u'Edward Norton', u'Helena Bonh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Inception</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>148</td>\n",
       "      <td>[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action</td>\n",
       "      <td>124</td>\n",
       "      <td>[u'Mark Hamill', u'Harrison Ford', u'Carrie Fi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    star_rating                                           title  \\\n",
       "3           9.0                                 The Dark Knight   \n",
       "5           8.9                                    12 Angry Men   \n",
       "9           8.9                                      Fight Club   \n",
       "11          8.8                                       Inception   \n",
       "12          8.8  Star Wars: Episode V - The Empire Strikes Back   \n",
       "\n",
       "   content_rating   genre  duration  \\\n",
       "3           PG-13  Action       152   \n",
       "5       NOT RATED   Drama        96   \n",
       "9               R   Drama       139   \n",
       "11          PG-13  Action       148   \n",
       "12             PG  Action       124   \n",
       "\n",
       "                                          actors_list  \n",
       "3   [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "5   [u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...  \n",
       "9   [u'Brad Pitt', u'Edward Norton', u'Helena Bonh...  \n",
       "11  [u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...  \n",
       "12  [u'Mark Hamill', u'Harrison Ford', u'Carrie Fi...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[movies.genre.isin(counts.nlargest(3).index)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, only Drama and Comedy and Action movies remain in the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15. Handle missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a dataset of UFO sightings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State  \\\n",
       "0                Ithaca             NaN       TRIANGLE    NY   \n",
       "1           Willingboro             NaN          OTHER    NJ   \n",
       "2               Holyoke             NaN           OVAL    CO   \n",
       "3               Abilene             NaN           DISK    KS   \n",
       "4  New York Worlds Fair             NaN          LIGHT    NY   \n",
       "\n",
       "                 Time  \n",
       "0 1930-06-01 22:00:00  \n",
       "1 1930-06-30 20:00:00  \n",
       "2 1931-02-15 14:00:00  \n",
       "3 1931-06-01 13:00:00  \n",
       "4 1933-04-18 19:00:00  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that some of the values are missing.\n",
    "\n",
    "To find out how many values are missing in each column, you can use the `isna()` method and then take the `sum()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                  25\n",
       "Colors Reported    15359\n",
       "Shape Reported      2644\n",
       "State                  0\n",
       "Time                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`isna()` generated a DataFrame of True and False values, and `sum()` converted all of the True values to 1 and added them up.\n",
    "\n",
    "Similarly, you can find out the percentage of values that are missing by taking the `mean()` of `isna()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City               0.001371\n",
       "Colors Reported    0.842004\n",
       "Shape Reported     0.144948\n",
       "State              0.000000\n",
       "Time               0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isna().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to drop the columns that have any missing values, you can use the `dropna()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State                Time\n",
       "0    NY 1930-06-01 22:00:00\n",
       "1    NJ 1930-06-30 20:00:00\n",
       "2    CO 1931-02-15 14:00:00\n",
       "3    KS 1931-06-01 13:00:00\n",
       "4    NY 1933-04-18 19:00:00"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(axis='columns').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or if you want to drop columns in which more than 10% of the values are missing, you can set a threshold for `dropna()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City State                Time\n",
       "0                Ithaca    NY 1930-06-01 22:00:00\n",
       "1           Willingboro    NJ 1930-06-30 20:00:00\n",
       "2               Holyoke    CO 1931-02-15 14:00:00\n",
       "3               Abilene    KS 1931-06-01 13:00:00\n",
       "4  New York Worlds Fair    NY 1933-04-18 19:00:00"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(thresh=len(ufo)*0.9, axis='columns').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`len(ufo)` returns the total number of rows, and then we multiply that by 0.9 to tell pandas to only keep columns in which at least 90% of the values are not missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16. Split a string into multiple columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create another example DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Arthur Doe</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane Ann Smith</td>\n",
       "      <td>Washington, DC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name         location\n",
       "0  John Arthur Doe  Los Angeles, CA\n",
       "1   Jane Ann Smith   Washington, DC"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'name':['John Arthur Doe', 'Jane Ann Smith'],\n",
    "                   'location':['Los Angeles, CA', 'Washington, DC']})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we wanted to split the \"name\" column into three separate columns, for first, middle, and last name? We would use the `str.split()` method and tell it to split on a space character and expand the results into a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>Arthur</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Smith</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0       1      2\n",
       "0  John  Arthur    Doe\n",
       "1  Jane     Ann  Smith"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.name.str.split(' ', expand=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These three columns can actually be saved to the original DataFrame in a single assignment statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>location</th>\n",
       "      <th>first</th>\n",
       "      <th>middle</th>\n",
       "      <th>last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Arthur Doe</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>John</td>\n",
       "      <td>Arthur</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane Ann Smith</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Smith</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name         location first  middle   last\n",
       "0  John Arthur Doe  Los Angeles, CA  John  Arthur    Doe\n",
       "1   Jane Ann Smith   Washington, DC  Jane     Ann  Smith"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['first', 'middle', 'last']] = df.name.str.split(' ', expand=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we wanted to split a string, but only keep one of the resulting columns? For example, let's split the location column on \"comma space\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0   1\n",
       "0  Los Angeles  CA\n",
       "1   Washington  DC"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.str.split(', ', expand=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we only cared about saving the city name in column 0, we can just select that column and save it to the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>location</th>\n",
       "      <th>first</th>\n",
       "      <th>middle</th>\n",
       "      <th>last</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Arthur Doe</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>John</td>\n",
       "      <td>Arthur</td>\n",
       "      <td>Doe</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane Ann Smith</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name         location first  middle   last         city\n",
       "0  John Arthur Doe  Los Angeles, CA  John  Arthur    Doe  Los Angeles\n",
       "1   Jane Ann Smith   Washington, DC  Jane     Ann  Smith   Washington"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['city'] = df.location.str.split(', ', expand=True)[0]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 17. Expand a Series of lists into a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create another example DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_one</th>\n",
       "      <th>col_two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>[10, 40]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>[20, 50]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>[30, 60]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col_one   col_two\n",
       "0       a  [10, 40]\n",
       "1       b  [20, 50]\n",
       "2       c  [30, 60]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'col_one':['a', 'b', 'c'], 'col_two':[[10, 40], [20, 50], [30, 60]]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two columns, and the second column contains regular Python lists of integers.\n",
    "\n",
    "If we wanted to expand the second column into its own DataFrame, we can use the `apply()` method on that column and pass it the Series constructor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1\n",
       "0  10  40\n",
       "1  20  50\n",
       "2  30  60"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.col_two.apply(pd.Series)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And by using the `concat()` function, you can combine the original DataFrame with the new DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_one</th>\n",
       "      <th>col_two</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>[10, 40]</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>[20, 50]</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>[30, 60]</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col_one   col_two   0   1\n",
       "0       a  [10, 40]  10  40\n",
       "1       b  [20, 50]  20  50\n",
       "2       c  [30, 60]  30  60"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df, df_new], axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 18. Aggregate by multiple functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a DataFrame of orders from the Chipotle restaurant chain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description  item_price  \n",
       "0                                                NaN        2.39  \n",
       "1                                       [Clementine]        3.39  \n",
       "2                                            [Apple]        3.39  \n",
       "3                                                NaN        2.39  \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...       10.98  \n",
       "6                                                NaN        1.69  \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.75  \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...        9.25  \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...        9.25  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each order has an order_id and consists of one or more rows. To figure out the total price of an order, you sum the item_price for that order_id. For example, here's the total price of order number 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.56"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders.order_id == 1].item_price.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wanted to calculate the total price of every order, you would `groupby()` order_id and then take the sum of item_price for each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id\n",
       "1    11.56\n",
       "2    16.98\n",
       "3    12.67\n",
       "4    21.00\n",
       "5    13.70\n",
       "Name: item_price, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby('order_id').item_price.sum().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, you're not actually limited to aggregating by a single function such as `sum()`. To aggregate by multiple functions, you use the `agg()` method and pass it a list of functions such as `sum()` and `count()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.56</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.67</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sum  count\n",
       "order_id              \n",
       "1         11.56      4\n",
       "2         16.98      1\n",
       "3         12.67      2\n",
       "4         21.00      2\n",
       "5         13.70      2"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby('order_id').item_price.agg(['sum', 'count']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That gives us the total price of each order as well as the number of items in each order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 19. Combine the output of an aggregation with a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take another look at the orders DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description  item_price  \n",
       "0                                                NaN        2.39  \n",
       "1                                       [Clementine]        3.39  \n",
       "2                                            [Apple]        3.39  \n",
       "3                                                NaN        2.39  \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...       10.98  \n",
       "6                                                NaN        1.69  \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.75  \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...        9.25  \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...        9.25  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we wanted to create a new column listing the total price of each order? Recall that we calculated the total price using the `sum()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id\n",
       "1    11.56\n",
       "2    16.98\n",
       "3    12.67\n",
       "4    21.00\n",
       "5    13.70\n",
       "Name: item_price, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.groupby('order_id').item_price.sum().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sum()` is an aggregation function, which means that it returns a reduced version of the input data.\n",
    "\n",
    "In other words, the output of the `sum()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders.groupby('order_id').item_price.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...is smaller than the input to the function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4622"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders.item_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution is to use the `transform()` method, which performs the same calculation but returns output data that is the same shape as the input data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4622"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_price = orders.groupby('order_id').item_price.transform('sum')\n",
    "len(total_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll store the results in a new DataFrame column called total_price:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>3.39</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>3.39</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>10.98</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.69</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.75</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>9.25</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>9.25</td>\n",
       "      <td>13.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description  item_price  total_price  \n",
       "0                                                NaN        2.39        11.56  \n",
       "1                                       [Clementine]        3.39        11.56  \n",
       "2                                            [Apple]        3.39        11.56  \n",
       "3                                                NaN        2.39        11.56  \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98        16.98  \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...       10.98        12.67  \n",
       "6                                                NaN        1.69        12.67  \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.75        21.00  \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...        9.25        21.00  \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...        9.25        13.70  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['total_price'] = total_price\n",
    "orders.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the total price of each order is now listed on every single line.\n",
    "\n",
    "That makes it easy to calculate the percentage of the total order price that each line represents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "      <th>total_price</th>\n",
       "      <th>percent_of_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.56</td>\n",
       "      <td>0.206747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>3.39</td>\n",
       "      <td>11.56</td>\n",
       "      <td>0.293253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>3.39</td>\n",
       "      <td>11.56</td>\n",
       "      <td>0.293253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.56</td>\n",
       "      <td>0.206747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>10.98</td>\n",
       "      <td>12.67</td>\n",
       "      <td>0.866614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.69</td>\n",
       "      <td>12.67</td>\n",
       "      <td>0.133386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.75</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0.559524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>9.25</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0.440476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>9.25</td>\n",
       "      <td>13.70</td>\n",
       "      <td>0.675182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description  item_price  total_price  \\\n",
       "0                                                NaN        2.39        11.56   \n",
       "1                                       [Clementine]        3.39        11.56   \n",
       "2                                            [Apple]        3.39        11.56   \n",
       "3                                                NaN        2.39        11.56   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98        16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...       10.98        12.67   \n",
       "6                                                NaN        1.69        12.67   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.75        21.00   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...        9.25        21.00   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...        9.25        13.70   \n",
       "\n",
       "   percent_of_total  \n",
       "0          0.206747  \n",
       "1          0.293253  \n",
       "2          0.293253  \n",
       "3          0.206747  \n",
       "4          1.000000  \n",
       "5          0.866614  \n",
       "6          0.133386  \n",
       "7          0.559524  \n",
       "8          0.440476  \n",
       "9          0.675182  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['percent_of_total'] = orders.item_price / orders.total_price\n",
    "orders.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 20. Select a slice of rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at another dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the famous Titanic dataset, which shows information about passengers on the Titanic and whether or not they survived.\n",
    "\n",
    "If you wanted a numerical summary of the dataset, you would use the `describe()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the resulting DataFrame might be displaying more information than you need.\n",
    "\n",
    "If you wanted to filter it to only show the \"five-number summary\", you can use the `loc` accessor and pass it a slice of the \"min\" through the \"max\" row labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass     Age  SibSp  Parch      Fare\n",
       "min          1.0       0.0     1.0   0.420    0.0    0.0    0.0000\n",
       "25%        223.5       0.0     2.0  20.125    0.0    0.0    7.9104\n",
       "50%        446.0       0.0     3.0  28.000    0.0    0.0   14.4542\n",
       "75%        668.5       1.0     3.0  38.000    1.0    0.0   31.0000\n",
       "max        891.0       1.0     3.0  80.000    8.0    6.0  512.3292"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe().loc['min':'max']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you're not interested in all of the columns, you can also pass it a slice of column labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>20.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>38.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.0</td>\n",
       "      <td>80.000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Age  SibSp  Parch\n",
       "min     1.0   0.420    0.0    0.0\n",
       "25%     2.0  20.125    0.0    0.0\n",
       "50%     3.0  28.000    0.0    0.0\n",
       "75%     3.0  38.000    1.0    0.0\n",
       "max     3.0  80.000    8.0    6.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe().loc['min':'max', 'Pclass':'Parch']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 21. Reshape a MultiIndexed Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Titanic dataset has a \"Survived\" column made up of ones and zeros, so you can calculate the overall survival rate by taking a mean of that column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Survived.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wanted to calculate the survival rate by a single category such as \"Sex\", you would use a `groupby()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('Sex').Survived.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you wanted to calculate the survival rate across two different categories at once, you would `groupby()` both of those categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass\n",
       "female  1         0.968085\n",
       "        2         0.921053\n",
       "        3         0.500000\n",
       "male    1         0.368852\n",
       "        2         0.157407\n",
       "        3         0.135447\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['Sex', 'Pclass']).Survived.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the survival rate for every combination of Sex and Passenger Class. It's stored as a MultiIndexed Series, meaning that it has multiple index levels to the left of the actual data.\n",
    "\n",
    "It can be hard to read and interact with data in this format, so it's often more convenient to reshape a MultiIndexed Series into a DataFrame by using the `unstack()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass         1         2         3\n",
       "Sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['Sex', 'Pclass']).Survived.mean().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This DataFrame contains the same exact data as the MultiIndexed Series, except that now you can interact with it using familiar DataFrame methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 22. Create a pivot table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you often create DataFrames like the one above, you might find it more convenient to use the `pivot_table()` method instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass         1         2         3\n",
       "Sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='Sex', columns='Pclass', values='Survived', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a pivot table, you directly specify the index, the columns, the values, and the aggregation function.\n",
    "\n",
    "An added benefit of a pivot table is that you can easily add row and column totals by setting `margins=True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass         1         2         3       All\n",
       "Sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='Sex', columns='Pclass', values='Survived', aggfunc='mean',\n",
    "                    margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the overall survival rate as well as the survival rate by Sex and Passenger Class.\n",
    "\n",
    "Finally, you can create a cross-tabulation just by changing the aggregation function from \"mean\" to \"count\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>94</td>\n",
       "      <td>76</td>\n",
       "      <td>144</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>122</td>\n",
       "      <td>108</td>\n",
       "      <td>347</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>216</td>\n",
       "      <td>184</td>\n",
       "      <td>491</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass    1    2    3  All\n",
       "Sex                       \n",
       "female   94   76  144  314\n",
       "male    122  108  347  577\n",
       "All     216  184  491  891"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='Sex', columns='Pclass', values='Survived', aggfunc='count',\n",
    "                    margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the number of records that appear in each combination of categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 23. Convert continuous data into categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the Age column from the Titanic dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "5     NaN\n",
       "6    54.0\n",
       "7     2.0\n",
       "8    27.0\n",
       "9    14.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Age.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's currently continuous data, but what if you wanted to convert it into categorical data?\n",
    "\n",
    "One solution would be to label the age ranges, such as \"child\", \"young adult\", and \"adult\". The best way to do this is by using the `cut()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    young adult\n",
       "1          adult\n",
       "2          adult\n",
       "3          adult\n",
       "4          adult\n",
       "5            NaN\n",
       "6          adult\n",
       "7          child\n",
       "8          adult\n",
       "9          child\n",
       "Name: Age, dtype: category\n",
       "Categories (3, object): [child < young adult < adult]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(titanic.Age, bins=[0, 18, 25, 99], labels=['child', 'young adult', 'adult']).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This assigned each value to a bin with a label. Ages 0 to 18 were assigned the label \"child\", ages 18 to 25 were assigned the label \"young adult\", and ages 25 to 99 were assigned the label \"adult\".\n",
    "\n",
    "Notice that the data type is now \"category\", and the categories are automatically ordered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 24. Change display options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take another look at the Titanic dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the Age column has 1 decimal place and the Fare column has 4 decimal places. What if you wanted to standardize the display to use 2 decimal places?\n",
    "\n",
    "You can use the `set_option()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', '{:.2f}'.format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first argument is the name of the option, and the second argument is a Python format string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.28</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.10</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male 22.00      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.00      1   \n",
       "2                             Heikkinen, Miss. Laina  female 26.00      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.00      1   \n",
       "4                           Allen, Mr. William Henry    male 35.00      0   \n",
       "\n",
       "   Parch            Ticket  Fare Cabin Embarked  \n",
       "0      0         A/5 21171  7.25   NaN        S  \n",
       "1      0          PC 17599 71.28   C85        C  \n",
       "2      0  STON/O2. 3101282  7.92   NaN        S  \n",
       "3      0            113803 53.10  C123        S  \n",
       "4      0            373450  8.05   NaN        S  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that Age and Fare are now using 2 decimal places. Note that this did not change the underlying data, only the display of the data.\n",
    "\n",
    "You can also reset any option back to its default:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.float_format')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many more options you can specify is a similar way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 25. Style a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous trick is useful if you want to change the display of your entire notebook. However, a more flexible and powerful approach is to define the style of a particular DataFrame.\n",
    "\n",
    "Let's return to the stocks DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>31.50</td>\n",
       "      <td>14070500</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>112.52</td>\n",
       "      <td>21701800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>57.42</td>\n",
       "      <td>19189500</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>113.00</td>\n",
       "      <td>29736800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>57.24</td>\n",
       "      <td>20085900</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>31.35</td>\n",
       "      <td>18460400</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>57.64</td>\n",
       "      <td>16726400</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>31.59</td>\n",
       "      <td>11808600</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>113.05</td>\n",
       "      <td>21453100</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Close    Volume Symbol\n",
       "0 2016-10-03   31.50  14070500   CSCO\n",
       "1 2016-10-03  112.52  21701800   AAPL\n",
       "2 2016-10-03   57.42  19189500   MSFT\n",
       "3 2016-10-04  113.00  29736800   AAPL\n",
       "4 2016-10-04   57.24  20085900   MSFT\n",
       "5 2016-10-04   31.35  18460400   CSCO\n",
       "6 2016-10-05   57.64  16726400   MSFT\n",
       "7 2016-10-05   31.59  11808600   CSCO\n",
       "8 2016-10-05  113.05  21453100   AAPL"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a dictionary of format strings that specifies how each column should be formatted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "format_dict = {'Date':'{:%m/%d/%y}', 'Close':'${:.2f}', 'Volume':'{:,}'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can pass it to the DataFrame's `style.format()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Date</th>        <th class=\"col_heading level0 col1\" >Close</th>        <th class=\"col_heading level0 col2\" >Volume</th>        <th class=\"col_heading level0 col3\" >Symbol</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row0_col0\" class=\"data row0 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row0_col1\" class=\"data row0 col1\" >$31.50</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row0_col2\" class=\"data row0 col2\" >14,070,500</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row0_col3\" class=\"data row0 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row1_col0\" class=\"data row1 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row1_col1\" class=\"data row1 col1\" >$112.52</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row1_col2\" class=\"data row1 col2\" >21,701,800</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row1_col3\" class=\"data row1 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row2_col0\" class=\"data row2 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row2_col1\" class=\"data row2 col1\" >$57.42</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row2_col2\" class=\"data row2 col2\" >19,189,500</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row2_col3\" class=\"data row2 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row3_col0\" class=\"data row3 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row3_col1\" class=\"data row3 col1\" >$113.00</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row3_col2\" class=\"data row3 col2\" >29,736,800</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row3_col3\" class=\"data row3 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row4_col0\" class=\"data row4 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row4_col1\" class=\"data row4 col1\" >$57.24</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row4_col2\" class=\"data row4 col2\" >20,085,900</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row4_col3\" class=\"data row4 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row5_col0\" class=\"data row5 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row5_col1\" class=\"data row5 col1\" >$31.35</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row5_col2\" class=\"data row5 col2\" >18,460,400</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row5_col3\" class=\"data row5 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row6_col0\" class=\"data row6 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row6_col1\" class=\"data row6 col1\" >$57.64</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row6_col2\" class=\"data row6 col2\" >16,726,400</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row6_col3\" class=\"data row6 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row7_col0\" class=\"data row7 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row7_col1\" class=\"data row7 col1\" >$31.59</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row7_col2\" class=\"data row7 col2\" >11,808,600</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row7_col3\" class=\"data row7 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row8_col0\" class=\"data row8 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row8_col1\" class=\"data row8 col1\" >$113.05</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row8_col2\" class=\"data row8 col2\" >21,453,100</td>\n",
       "                        <td id=\"T_0cbd80dc_9aa3_11e9_92df_787b8ac075f7row8_col3\" class=\"data row8 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbd42ce9b38>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.style.format(format_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the Date is now in month-day-year format, the closing price has a dollar sign, and the Volume has commas.\n",
    "\n",
    "We can apply more styling by chaining additional methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row5_col1 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row8_col1 {\n",
       "            : ;\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Date</th>        <th class=\"col_heading level0 col1\" >Close</th>        <th class=\"col_heading level0 col2\" >Volume</th>        <th class=\"col_heading level0 col3\" >Symbol</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row0_col0\" class=\"data row0 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row0_col1\" class=\"data row0 col1\" >$31.50</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row0_col2\" class=\"data row0 col2\" >14,070,500</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row0_col3\" class=\"data row0 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row1_col0\" class=\"data row1 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row1_col1\" class=\"data row1 col1\" >$112.52</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row1_col2\" class=\"data row1 col2\" >21,701,800</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row1_col3\" class=\"data row1 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row2_col0\" class=\"data row2 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row2_col1\" class=\"data row2 col1\" >$57.42</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row2_col2\" class=\"data row2 col2\" >19,189,500</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row2_col3\" class=\"data row2 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row3_col0\" class=\"data row3 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row3_col1\" class=\"data row3 col1\" >$113.00</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row3_col2\" class=\"data row3 col2\" >29,736,800</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row3_col3\" class=\"data row3 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row4_col0\" class=\"data row4 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row4_col1\" class=\"data row4 col1\" >$57.24</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row4_col2\" class=\"data row4 col2\" >20,085,900</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row4_col3\" class=\"data row4 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row5_col0\" class=\"data row5 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row5_col1\" class=\"data row5 col1\" >$31.35</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row5_col2\" class=\"data row5 col2\" >18,460,400</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row5_col3\" class=\"data row5 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row6_col0\" class=\"data row6 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row6_col1\" class=\"data row6 col1\" >$57.64</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row6_col2\" class=\"data row6 col2\" >16,726,400</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row6_col3\" class=\"data row6 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row7_col0\" class=\"data row7 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row7_col1\" class=\"data row7 col1\" >$31.59</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row7_col2\" class=\"data row7 col2\" >11,808,600</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row7_col3\" class=\"data row7 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row8_col0\" class=\"data row8 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row8_col1\" class=\"data row8 col1\" >$113.05</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row8_col2\" class=\"data row8 col2\" >21,453,100</td>\n",
       "                        <td id=\"T_0cc195fa_9aa3_11e9_92df_787b8ac075f7row8_col3\" class=\"data row8 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbd1074ad30>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(stocks.style.format(format_dict)\n",
    " .hide_index()\n",
    " .highlight_min('Close', color='red')\n",
    " .highlight_max('Close', color='lightgreen')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've now hidden the index, highlighted the minimum Close value in red, and highlighted the maximum Close value in green.\n",
    "\n",
    "Here's another example of DataFrame styling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row0_col2 {\n",
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       "            color:  #000000;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row1_col2 {\n",
       "            background-color:  #5aa2cf;\n",
       "            color:  #000000;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row2_col2 {\n",
       "            background-color:  #8fc2de;\n",
       "            color:  #000000;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row3_col2 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row4_col2 {\n",
       "            background-color:  #7ab6d9;\n",
       "            color:  #000000;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row5_col2 {\n",
       "            background-color:  #a0cbe2;\n",
       "            color:  #000000;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row6_col2 {\n",
       "            background-color:  #bed8ec;\n",
       "            color:  #000000;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row7_col2 {\n",
       "            background-color:  #f7fbff;\n",
       "            color:  #000000;\n",
       "        }    #T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row8_col2 {\n",
       "            background-color:  #5fa6d1;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Date</th>        <th class=\"col_heading level0 col1\" >Close</th>        <th class=\"col_heading level0 col2\" >Volume</th>        <th class=\"col_heading level0 col3\" >Symbol</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row0_col0\" class=\"data row0 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row0_col1\" class=\"data row0 col1\" >$31.50</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row0_col2\" class=\"data row0 col2\" >14,070,500</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row0_col3\" class=\"data row0 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row1_col0\" class=\"data row1 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row1_col1\" class=\"data row1 col1\" >$112.52</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row1_col2\" class=\"data row1 col2\" >21,701,800</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row1_col3\" class=\"data row1 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row2_col0\" class=\"data row2 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row2_col1\" class=\"data row2 col1\" >$57.42</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row2_col2\" class=\"data row2 col2\" >19,189,500</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row2_col3\" class=\"data row2 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row3_col0\" class=\"data row3 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row3_col1\" class=\"data row3 col1\" >$113.00</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row3_col2\" class=\"data row3 col2\" >29,736,800</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row3_col3\" class=\"data row3 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row4_col0\" class=\"data row4 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row4_col1\" class=\"data row4 col1\" >$57.24</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row4_col2\" class=\"data row4 col2\" >20,085,900</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row4_col3\" class=\"data row4 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row5_col0\" class=\"data row5 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row5_col1\" class=\"data row5 col1\" >$31.35</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row5_col2\" class=\"data row5 col2\" >18,460,400</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row5_col3\" class=\"data row5 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row6_col0\" class=\"data row6 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row6_col1\" class=\"data row6 col1\" >$57.64</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row6_col2\" class=\"data row6 col2\" >16,726,400</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row6_col3\" class=\"data row6 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row7_col0\" class=\"data row7 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row7_col1\" class=\"data row7 col1\" >$31.59</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row7_col2\" class=\"data row7 col2\" >11,808,600</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row7_col3\" class=\"data row7 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row8_col0\" class=\"data row8 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row8_col1\" class=\"data row8 col1\" >$113.05</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row8_col2\" class=\"data row8 col2\" >21,453,100</td>\n",
       "                        <td id=\"T_0cc4051a_9aa3_11e9_92df_787b8ac075f7row8_col3\" class=\"data row8 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbd430b7d30>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(stocks.style.format(format_dict)\n",
    " .hide_index()\n",
    " .background_gradient(subset='Volume', cmap='Blues')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Volume column now has a background gradient to help you easily identify high and low values.\n",
    "\n",
    "And here's one final example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_0cc64082_9aa3_11e9_92df_787b8ac075f7row0_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 73.7%, transparent 73.7%);\n",
       "        }    #T_0cc64082_9aa3_11e9_92df_787b8ac075f7row1_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 86.5%, transparent 86.5%);\n",
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       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 82.3%, transparent 82.3%);\n",
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       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 100.0%, transparent 100.0%);\n",
       "        }    #T_0cc64082_9aa3_11e9_92df_787b8ac075f7row4_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 83.8%, transparent 83.8%);\n",
       "        }    #T_0cc64082_9aa3_11e9_92df_787b8ac075f7row5_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 81.0%, transparent 81.0%);\n",
       "        }    #T_0cc64082_9aa3_11e9_92df_787b8ac075f7row6_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 78.1%, transparent 78.1%);\n",
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       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 69.9%, transparent 69.9%);\n",
       "        }    #T_0cc64082_9aa3_11e9_92df_787b8ac075f7row8_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 86.1%, transparent 86.1%);\n",
       "        }</style><table id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7\" ><caption>Stock Prices from October 2016</caption><thead>    <tr>        <th class=\"col_heading level0 col0\" >Date</th>        <th class=\"col_heading level0 col1\" >Close</th>        <th class=\"col_heading level0 col2\" >Volume</th>        <th class=\"col_heading level0 col3\" >Symbol</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row0_col0\" class=\"data row0 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row0_col1\" class=\"data row0 col1\" >$31.50</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row0_col2\" class=\"data row0 col2\" >14,070,500</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row0_col3\" class=\"data row0 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row1_col0\" class=\"data row1 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row1_col1\" class=\"data row1 col1\" >$112.52</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row1_col2\" class=\"data row1 col2\" >21,701,800</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row1_col3\" class=\"data row1 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row2_col0\" class=\"data row2 col0\" >10/03/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row2_col1\" class=\"data row2 col1\" >$57.42</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row2_col2\" class=\"data row2 col2\" >19,189,500</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row2_col3\" class=\"data row2 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row3_col0\" class=\"data row3 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row3_col1\" class=\"data row3 col1\" >$113.00</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row3_col2\" class=\"data row3 col2\" >29,736,800</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row3_col3\" class=\"data row3 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row4_col0\" class=\"data row4 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row4_col1\" class=\"data row4 col1\" >$57.24</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row4_col2\" class=\"data row4 col2\" >20,085,900</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row4_col3\" class=\"data row4 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row5_col0\" class=\"data row5 col0\" >10/04/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row5_col1\" class=\"data row5 col1\" >$31.35</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row5_col2\" class=\"data row5 col2\" >18,460,400</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row5_col3\" class=\"data row5 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row6_col0\" class=\"data row6 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row6_col1\" class=\"data row6 col1\" >$57.64</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row6_col2\" class=\"data row6 col2\" >16,726,400</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row6_col3\" class=\"data row6 col3\" >MSFT</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row7_col0\" class=\"data row7 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row7_col1\" class=\"data row7 col1\" >$31.59</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row7_col2\" class=\"data row7 col2\" >11,808,600</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row7_col3\" class=\"data row7 col3\" >CSCO</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row8_col0\" class=\"data row8 col0\" >10/05/16</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row8_col1\" class=\"data row8 col1\" >$113.05</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row8_col2\" class=\"data row8 col2\" >21,453,100</td>\n",
       "                        <td id=\"T_0cc64082_9aa3_11e9_92df_787b8ac075f7row8_col3\" class=\"data row8 col3\" >AAPL</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbd10772240>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(stocks.style.format(format_dict)\n",
    " .hide_index()\n",
    " .bar('Volume', color='lightblue', align='zero')\n",
    " .set_caption('Stock Prices from October 2016')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's now a bar chart within the Volume column and a caption above the DataFrame.\n",
    "\n",
    "Note that there are many more options for how you can style your DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Profile a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that you've got a new dataset, and you want to quickly explore it without too much work. There's a separate package called [pandas-profiling](https://github.com/pandas-profiling/pandas-profiling) that is designed for this purpose.\n",
    "\n",
    "First you have to install it using conda or pip. Once that's done, you import `pandas_profiling`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, simply run the `ProfileReport()` function and pass it any DataFrame. It returns an interactive HTML report:\n",
    "\n",
    "- The first section is an overview of the dataset and a list of possible issues with the data.\n",
    "- The next section gives a summary of each column. You can click \"toggle details\" for even more information.\n",
    "- The third section shows a heatmap of the correlation between columns.\n",
    "- And the fourth section shows the head of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "\n",
       "</style>\n",
       "\n",
       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>12 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>891 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>8.1% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>83.6 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>96.1 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>6 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>4 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_Age\"><code>Age</code></a> has 177 / 19.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_Cabin\"><code>Cabin</code></a> has 687 / 77.1% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_Cabin\"><code>Cabin</code></a> has a high cardinality: 148 distinct values  <span class=\"label label-warning\">Warning</span></li><li><a href=\"#pp_var_Fare\"><code>Fare</code></a> has 15 / 1.7% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_Parch\"><code>Parch</code></a> has 678 / 76.1% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_SibSp\"><code>SibSp</code></a> has 608 / 68.2% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_Ticket\"><code>Ticket</code></a> has a high cardinality: 681 distinct values  <span class=\"label label-warning\">Warning</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Age\">Age<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>89</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>10.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>19.9%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>177</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>29.699</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0.42</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>80</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-4171155060974119712\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABMElEQVR4nO3dwYnCUBhG0XGwJIuwJ9fT0xRhT7EBuSRCyK%2BesxeyuXy%2B8MTTsizLD/DU79EPAJOdj36Ao1xu/5s/c/%2B77vAkTGZBIHztgrxi6%2BpYnPdnQSAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCC4zbsjvzl5fxYEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIK7WMO4vzWLBYEgEAgCgSAQCOMO6Q6pTGJBIJym/YnnKwvCNhZ3PQsCQSAQBAJBIBDGveZlf16lr2dBIAgEgkAgOIOwyreeWywIBAvCbj5hdcbdxYJJfMWCIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKB8ADYZyeYbz9csAAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4171155060974119712,#minihistogram-4171155060974119712\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-4171155060974119712\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-4171155060974119712\"\n",
       "                                                  aria-controls=\"quantiles-4171155060974119712\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-4171155060974119712\" aria-controls=\"histogram-4171155060974119712\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-4171155060974119712\" aria-controls=\"common-4171155060974119712\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-4171155060974119712\" aria-controls=\"extreme-4171155060974119712\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-4171155060974119712\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0.42</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>20.125</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>28</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>38</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>56</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>80</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>79.58</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>17.875</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>14.526</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.48912</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>0.17827</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>29.699</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>11.323</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.38911</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>21205</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>211.02</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>7.0 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-4171155060974119712\">\n",
       "            <img 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Rp0%2BweEwAAoIKAK1izZ8/WsmXL9NVXXyk6OlppaWnq2bOn6tWr57tPSEiIHn/8cRunBAAA%2BGkBV7AWLlyo3r17q3///mratOk579OkSROlp6df4skAAAAqJ%2BAK1vr161VUVKTDhw/71lauXKkOHTooIiJCktSyZUu1bNnSrhEBAAB%2BVsBd5P6vf/1L3bp1U1ZWlm9t6tSp6tWrlz7//HMbJwMAAKicgCtYf/7zn3XrrbcqLS3Nt/buu%2B%2BqU6dOmjJlio2TAQAAVE7AFazt27fr4YcfVu3atX1rISEhevjhh7VlyxYbJwMAAKicgCtY9erV0759%2Byqsf/fdd6pbt64NEwEAAFRNwBWsbt26acKECfroo49UVFSk48eP65///KeeeOIJJSUl2T0eAADAeQXcuwhHjhypr7/%2BWkOGDFFQUJBvPSkpSaNGjbJxMgAAgMoJuILl9Xr13HPPae/evdq1a5dq1aqlJk2aqHHjxnaPBgAAUCkBV7DOuP7663X99dfbPQYAAECVBdw1WHv37tXgwYPVunVr3XDDDRU%2BqqqgoEBJSUnKzs72rW3dulUDBgxQbGysEhMTtWTJEr8/s3TpUiUlJSkmJkZ9%2B/bV5s2bLzoXAACoOQLuCNaECRO0f/9%2Bpaen67LLLruobW3atEljxozxe1fikSNH9PDDDys1NVXJycn6%2BOOPlZKSoubNm6t169bKzs7WxIkTNXfuXLVu3VqLFi3S//zP/2jt2rXyer0XGw8AANQAAVewNm/erJdfflmxsbEXtZ2lS5dq5syZysjI8PuhpatXr1b9%2BvV17733SpI6dOigXr16adGiRWrdurWWLFmi22%2B/XW3atJEkDRo0SFlZWVq5cqX69et3UTMBAICaIeBOEUZERCgsLOyit9OxY0etWbNGPXr08FvfvXu3mjVr5rfWtGlT7dy5U5KUm5v7s7cDAACcT8AdwRo4cKCmTZumqVOnXtQpwkaNGp1z/fjx4xVO9dWtW1cnTpyo1O2VkZeXp/z8fL81jydUkZGRld7GzwkJCfb77BZuzQXnc%2BM%2B6ebXm1uzuTWX5M5sAVew1q1bpy1btqhdu3a64oor/H5ljiS99957F7V9r9erY8eO%2Ba0VFxf7jpp5vV4VFxdXuD0iIqLSj5GVlaXMzEy/tZSUFKWmpl7g1OcWHu7Oa8LcmgvO5eZ9kmzO49ZckruyBVzBateundq1a1dt22/WrJk%2B/PBDv7Xc3FxFRUVJkqKiorR79%2B4Kt3fq1KnSj5GcnKzExES/NY8nVIWFxy9wan8hIcEKD/fq6NGTKisrN7LNQODWXHA%2BN%2B6Tbn69uTWbW3NJ1ZstIuLiLzu6EAFXsB555JFq3X5SUpKmTp2qv//977r33nu1adMmvfnmm5o9e7YkqX///kpJSVH37t3Vpk0bLVq0SIcOHarSr%2BmJjIyscDowP/%2BYSkvN7jRlZeXGtxkI3JoLzuXmfZJszuPWXJK7sgVcwZKknTt36uWXX9bevXs1Y8YMvfvuu2ratKmRI1sRERF68cUXNWnSJM2cOVMNGjTQH/7wB7Vv317S9%2B8qHD9%2BvCZMmKADBw6oadOmmjt3rurXr3/Rjw0AAGqGgCtY27Zt0913362YmBht27ZNJSUl2rFjh5566illZmaqS5cuVd7mrl27/L6Ojo7W4sWLf/L%2Bffr0UZ8%2Bfar8OAAAAFIA/piGZ555RkOGDNGCBQtUq1YtSdKTTz6p%2B%2B%2B/v8KF4wAAAIEo4ArWtm3bdMcdd1RYv/vuu/XFF1/YMBEAAEDVBFzBqlWrloqKiiqs79%2B/n19VAwAAHCHgClbXrl31l7/8RYWFhb61PXv2aNKkSercubN9gwEAAFRSwBWs0aNHq7i4WDfddJNOnjypvn37qmfPnvJ4PBo1apTd4wEAAJxXwL2LsF69elq8eLE2bNigf/3rXyovL1ezZs108803Kzg44PogAABABQFXsM7o0KGDOnToYPcYAAAAVRZwBSsxMVFBQUE/efvF/i5CAACA6hZwBeu3v/2tX8E6ffq0vvrqK61fv14jRoywcTIAAIDKCbiCNXz48HOuL1y4UJs2bdL9999/iScCAACoGsdcNd6lSxetW7fO7jEAAADOyzEFa%2BPGjapTp47dYwAAAJxXwJ0i/PEpQMuyVFRUpF27dnF6EAAAOELAFayrr766wrsIa9WqpQceeEC9evWyaSoAAIDKC7iCNWXKFLtHAAAAuCgBV7A%2B/vjjSt%2B3bdu21TgJAADAhQm4gjVo0CBZluX7OOPMacMza0FBQdqxY4ctMwIAAPycgCtYs2bN0uTJkzV69Gi1b99etWrV0tatWzVhwgTdc8896tKli90jAgAA/KyA%2BzENTz/9tMaPH6%2BuXbuqXr16qlOnjuLj4/XEE0/oxRdf1DXXXOP7AAAACEQBV7Dy8vL0i1/8osJ6vXr1VFhYaMNEAAAAVRNwBSsmJkbTpk1TUVGRb%2B3w4cOaOnWqOnToYONkAAAAlRNw12D94Q9/0AMPPKBOnTqpcePGkqS9e/eqUaNGmj9/vr3DAQAAVELAFawmTZpo5cqVevPNN7Vnzx5J0j333KPbb79dXq/X5ukAAADOL%2BAKliSFh4drwIAB%2Buabb3TttddK%2Bv6nuQMAADhBwF2DZVmWnnnmGbVt21Y9e/bUd999p9GjR2vs2LE6ffq03eMBAACcV8AVrAULFmjZsmUaP368ateuLUnq2rWr/u///k8zZsyweToAAIDzC7iClZWVpccff1x9%2B/b1/fT2Hj16aNKkSXrrrbdsng4AAOD8Aq5gffPNN7rhhhsqrDdv3lwHDx60YSIAAICqCbiCdc011%2Bizzz6rsL5u3TrfBe8AAACBLODeRfjggw/qT3/6kw4cOCDLsrRhwwYtXrxYCxYs0NixY%2B0eDwAA4LwCrmD169dPpaWlmjNnjoqLi/X444/riiuuUFpamu6%2B%2B267xwMAADivgCtYy5cv12233abk5GQVFBTIsixdccUVdo8FAABQaQF3DdaTTz7pu5i9QYMGlCsAAOA4AXcEq3Hjxtq1a5eaNGlSbY%2BxfPlyjR8/3m/tzA8x3bZtm4YOHars7Gx5PD/89cyYMUOdOnWqtpkAAIB7BFzBioqKUnp6uubNm6fGjRurTp06frdPnjz5oh%2Bjd%2B/e6t27t%2B/rAwcOqF%2B/fsrIyJD0fcl64YUXFB8ff9GPBQAAap6AK1j79u1TmzZtJEn5%2BfnV/niWZSkjI0OdO3dWnz599PXXX%2BvIkSNq2bJltT82AABwp4AoWJMnT9ajjz6q0NBQLViw4JI%2B9rJly5Sbm6vZs2dLknJychQWFqa0tDTl5OSoYcOGGjRokPr3739J5wIAAM4VEAVr/vz5evjhhxUaGupbe/DBBzV58mRFRkZW2%2BOWl5drzpw5%2Bt3vfqd69epJkkpKShQTE6O0tDRFRUUpOztbw4cPV1hYmLp3716p7ebl5VU4%2BubxhBrLEhIS7PfZLdyaC87nxn3Sza83t2Zzay7JndkComBZllVh7dNPP9WpU6eq9XGzs7OVl5fnd3Tqjjvu0B133OH7umPHjrrjjjv09ttvV7pgZWVlKTMz028tJSVFqampZgb/j/Bwr9HtBQq35oJzuXmfJJvzuDWX5K5sAVGw7LJq1SolJSX5HTl79dVXKxytKikpqXCx/c9JTk5WYmKi35rHE6rCwuMXP7S%2Bb/jh4V4dPXpSZWXlRrYZCNyaC87nxn3Sza83t2Zzay6perNFRIQZ3V5l1eiCtWnTJt1///1%2Ba0VFRZo2bZquu%2B46tWjRQuvXr9eKFSv0wgsvVHq7kZGRFU4H5ucfU2mp2Z2mrKzc%2BDYDgVtzwbncvE%2BSzXncmktyV7aAKVhBQUGX/DG/%2BeabCkXogQce0IkTJ/TII4/o0KFDuvbaa/X0008rLi7uks8HAACcKWAK1pNPPul3Gu706dOaOnWqwsL8D%2B2Z%2BDlYZ2zevLnCWlBQkIYNG6Zhw4YZexwAAFCzBETBatu2bYV33cXGxqqwsFCFhYU2TQUAAHBhAqJgXeqffQUAAFCd3PMDJwAAAAJEQBzBAoBAlvTMB3aPUCVvj0iwewSgxuMIFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZ57B4AF6f79A/tHqHS3h6RYPcIAABcEhzBAgAAMKzGFqyVK1eqZcuWio2N9X1kZGRIktatW6devXopJiZG3bt319q1a22eFgAAOEmNPUWYk5OjPn36aPLkyX7rX375pYYPH65p06apc%2BfOWr16tUaMGKHVq1fryiuvtGlaAADgJDX2CFZOTo5atWpVYX3p0qWKi4tT165d5fF41KNHD7Vt21ZZWVk2TAkAAJyoRh7BKi8v1/bt2%2BX1ejVv3jyVlZXplltuUXp6unJzc9WsWTO/%2Bzdt2lQ7d%2B60aVoAAOA0NbJgFRQUqGXLlurWrZtmzpypwsJCjR49WhkZGSopKZHX6/W7f926dXXixIlKbz8vL0/5%2Bfl%2Bax5PqCIjI43MHxIS7PfZKTyen5/XqbmAQHO%2B15rk7tebW7O5NZfkzmw1smA1bNhQixYt8n3t9XqVkZGhO%2B%2B8U%2B3atVNxcbHf/YuLixUWFlbp7WdlZSkzM9NvLSUlRampqRc3%2BI%2BEh3vPf6cAEhFRub9Dp%2BUCAk1lX2uSu19vbs3m1lySu7LVyIK1c%2BdOrVixQiNHjlRQUJAkqaSkRMHBwWrdurV27Njhd//c3NxzXq/1U5KTk5WYmOi35vGEqrDw%2BMUPr%2B8bfni4V0ePnjSyvUvlfPnPzlVWVn6JpgLcpzLfa9z8enNrNrfmkqo3W1X%2BwWFSjSxY9evX16JFi3T55Zdr8ODBysvL09SpU/Xb3/5Wd9xxh15%2B%2BWWtXLlSt956q1avXq2NGzdq3Lhxld5%2BZGRkhdOB%2BfnHVFpqdqdx2gussvnLysqN/10BNUlVXj9ufr25NZtbc0nuyuaek51VcNVVV%2Bm5557Te%2B%2B9p/j4ePXr10/R0dF6/PHH1aRJE/31r3/Vc889p7Zt22r27NmaNWuWrr/%2BervHBgAADlEjj2BJUnx8vBYvXnzO226%2B%2BWbdfPPNl3giAADgFjXyCBYAAEB1omABAAAYVmNPEeLS6z79Q7tHAADgkuAIFgAAgGEcwQIAl3HS0eK3RyTYPQJQLTiCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMNqZMHauXOnBg8erPj4eCUkJGjUqFEqKCiQJI0fP16tWrVSbGys7yMrK8vmiQEAgJPUuIJVXFysoUOHKjY2Vv/4xz%2B0YsUKHT58WI899pgkKScnRxMnTtTmzZt9H8nJyTZPDQAAnKTGFaz9%2B/erRYsWSklJUe3atRUREaHk5GR9/PHHKikp0eeff65WrVrZPSYAAHCwGlewfvnLX2revHkKCQnxra1atUo33nijdu7cqdLSUs2cOVM33XSTunXrpueff17l5eU2TgwAAJzGY/cAdrIsS9OnT9fatWu1cOFCHTx4UPHx8Ro4cKCmTZumHTt2KCUlRcHBwRo6dGilt5uXl6f8/Hy/NY8nVJGRkUbmDgkJ9vsMAE7l8Zj/PubW75FuzSW5M1uQZVmW3UPYoaioSGPHjtX27ds1Z84cNW/e/Jz3mzdvnlauXKnXX3%2B90tueNWuWMjMz/dZSUlKUmpp6UTOfS9y4d4xvEwAulU8m3Wb3CEC1qJFHsPbt26eHHnpIV199tV599VU1aNBAkvTuu%2B/q4MGDuuuuu3z3LSkpUd26dau0/eTkZCUmJvqteTyhKiw8fvHD6/uGHx7u1dGjJ41sDwDsYur74tnO/h5ZVuaeSzzcmkuq3mwREWFGt1dZNa5gHTlyRA888IDat2%2BvSZMmKTj4h8ORlmVp8uTJuu6669S%2BfXtt2bJF8%2BfP19ixY6v0GJGRkRVOB%2BbnHxEWYXIAAAw5SURBVFNpqdmdxm0vMAA1T9IzH9g9QpW8PSLB7hFUVlZu/P8ngcJN2WpcwXr99de1f/9%2Bvf3223rnHf/Ta5s3b9bYsWM1YcIEHThwQA0bNtTw4cPVp08fm6YFAABOVGOvwbrU8vOPGduWxxOsiIgwFRYed9y//gDAyew8gnX29363HOU5ozqzNWp0mdHtVZZ7LtcHAAAIEBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgmMfuAQAAcIru0z%2B0e4QqeXtEgt0j1FgcwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIJ1DocOHdKwYcMUFxendu3aadKkSSotLbV7LAAA4BAUrHMYMWKEQkND9cEHH%2BjVV1/Vhg0b9Pe//93usQAAgEPwuwh/5KuvvtLGjRu1fv16eb1eXXvttRo2bJimTp2qoUOH2j0eAACV5qTfnfjJpNvsHsEojmD9yO7du1W/fn1deeWVvrUmTZpo//79Onr0qI2TAQAAp%2BAI1o8cP35cXq/Xb%2B3M1ydOnFB4ePh5t5GXl6f8/Hy/NY8nVJGRkUZmDAkJ9vsMAIAbuOn/axSsHwkNDdXJkyf91s58HRYWVqltZGVlKTMz02/tkUce0fDhw43MmJeXp5dfnqfk5GRXHVLNy8tTVlaWkpOTjZXRQEE2ZyKbM7k1m1tzSd9nmzVrlquyuacqGhIVFaXDhw/r4MGDvrU9e/boqquu0mWXXVapbSQnJ%2Bv111/3%2B0hOTjY2Y35%2BvjIzMyscJXM6t%2BaSyOZUZHMmt2Zzay7Jndk4gvUjjRs3Vps2bfTUU0/piSeeUGFhoWbPnq3%2B/ftXehuRkZGuaeAAAKDqOIJ1DjNnzlRpaal%2B85vf6M4779TNN9%2BsYcOG2T0WAABwCI5gnUPDhg01c%2BZMu8cAAAAOFTJhwoQJdg%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%2BGxnTJ8%2B3WrRooU1evRoy7Isa%2B/evVZ0dLS1Zs0a6/Tp09Zbb71ltW7d2vruu%2B9snrRqpkyZYo0ZM6bCutPz3XfffVZKSop15MgR69ixY9YjjzxiPfzww67ZH8/47rvvrISEBOuNN95w/HNWVlZmJSQkWC%2B//LJVVlZmffvtt1a3bt2szMxMx2f7MY5gOcRXX32ljRs3KiMjQ16vV9dee62GDRumRYsW2T3aBVm6dKnS09OVlpbmt7569WrVr19f9957rzwejzp06KBevXo5Juf%2B/fvVokULpaSkqHbt2oqIiFBycrI%2B/vhjx2dr0KCBPvroI/Xt21dBQUE6fPiwTp06pQYNGjg%2Bm/T90bnVq1fr1ltv9a0tXbpUcXFx6tq1qzwej3r06KG2bdsqKyvLxkmrLicnx3f042xOzrdt2zZt3bpVU6ZMUXh4uOrVq6eJEycqPT3dFfvjGZZlKSMjQ507d1afPn0c/ZxJ0pEjR5Sfn6/y8nJZ//lNfcHBwfJ6vY7P9mMULIfYvXu36tevryuvvNK31qRJE%2B3fv19Hjx61cbIL07FjR61Zs0Y9evTwW9%2B9e7eaNWvmt9a0aVPt3LnzUo53wX75y19q3rx5CgkJ8a2tWrVKN954o%2BOzSVK9evUkSbfccot69eqlRo0aqW/fvo7PdujQIY0bN05/%2Bctf5PV6feu5ubmOziVJ5eXl2r59u95//3116dJFnTp10h//%2BEcdOXLE0fk%2B%2B%2BwzNW3aVK%2B88oqSkpLUsWNHPf3002rUqJHj98ezLVu2TLm5ub7T1k5%2BziQpIiJCgwYN0tNPP63o6Gjdcsstaty4sQYNGuT4bD9GwXKI48eP%2B33jl%2BT7%2BsSJE3aMdFEaNWokj8dTYf1cOevWrevIjJZl6dlnn9XatWs1btw4V2VbvXq11q9fr%2BDgYKWmpjo6W3l5uTIyMjR48GC1aNHC7zYn5zqjoKBALVu2VLdu3bRy5UotXrxYX375pTIyMhyd78iRI9q1a5e%2B/PJLLV26VG%2B88YYOHDig0aNHOzrX2crLyzVnzhz97ne/8/3jxunZysvLVbduXf3xj3/Uli1btGLFCu3Zs0czZ850fLYfo2A5RGhoqE6ePOm3dubrsLAwO0aqFl6v13fR9BnFxcWOy1hUVKTU1FS9%2BeabWrhwoZo3b%2B6abNL33/SuvPJKZWRk6IMPPnB0tueee061a9fWwIEDK9zm5FxnNGzYUIsWLVL//v3l9Xp19dVXKyMjQ%2BvXr5dlWY7NV7t2bUnSuHHjVK9ePTVs2FAjRozQunXrHJ3rbNnZ2crLy1P//v19a07fJ9esWaNVq1bpnnvuUe3atRUVFaWUlBT97//%2Br%2BOz/RgFyyGioqJ0%2BPBhHTx40Le2Z88eXXXVVbrssstsnMysZs2aaffu3X5rubm5ioqKsmmiqtu3b5/69eunoqIivfrqq2revLkk52f79NNPddttt/ne7SNJJSUlqlWrlpo2berYbMuWLdPGjRsVFxenuLg4rVixQitWrFBcXJzjnzPp%2B3e1PvPMM77rXaTvn7fg4GC1bt3asfmaNm2q8vJynT592rdWXl4uSbrhhhscm%2Btsq1atUlJSkkJDQ31rTt8nv/32W7/vIZLk8XhUq1Ytx2erwM4r7FE1d999t5WWlmYdO3bM9y7CmTNn2j3WRTv7XYQFBQVWXFyc9dJLL1klJSXWhg0brNjYWGvDhg02T1k5hw8ftjp37myNGTPGKisr87vN6dmKioqsW265xXrqqaesU6dOWd98843Vv39/a/z48Y7PdrbRo0f73kWYm5trRUdHW2%2B99ZbvXU3R0dHWF198YfOUlfftt99aMTEx1vPPP2%2BdPn3a%2Bve//23deeed1mOPPebofCUlJVZSUpI1fPhwq6ioyDp06JB1//33WykpKa7ZH3v27Gm98sorfmtOfs4sy7J2795ttWrVypozZ45VWlpq7du3z%2BrZs6c1ZcoUx2f7MQqWg%2BTn51vDhw%2B34uPjrfbt21tTpkyxSktL7R7rop1dsCzLsj777DMrOTnZio2NtX7zm99Yr732mo3TVc2LL75oNWvWzPrVr35lxcTE%2BH1YlrOzWdb33xwHDx5sxcXFWV26dLGmTZtmnTp1yrIs52c74%2ByCZVmWtX79eqt3795WTEyMdfvtt1vvv/%2B%2BjdNdmOzsbN9z0759e2vixIlWcXGxZVnOzvfdd99ZI0aMsBISEqy4uDhr1KhR1pEjRyzLcsf%2BGBMTc87nw8nPmWVZ1ocffmgNGDDAatOmjdW5c2e/7yNOz3a2IMs667gxAAAALhrXYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhv0/22YEeoqcVs0AAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-4171155060974119712\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">24.0</td>\n",
       "        <td class=\"number\">30</td>\n",
       "        <td class=\"number\">3.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">22.0</td>\n",
       "        <td class=\"number\">27</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">18.0</td>\n",
       "        <td class=\"number\">26</td>\n",
       "        <td class=\"number\">2.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">28.0</td>\n",
       "        <td class=\"number\">25</td>\n",
       "        <td class=\"number\">2.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">19.0</td>\n",
       "        <td class=\"number\">25</td>\n",
       "        <td class=\"number\">2.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">30.0</td>\n",
       "        <td class=\"number\">25</td>\n",
       "        <td class=\"number\">2.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">21.0</td>\n",
       "        <td class=\"number\">24</td>\n",
       "        <td class=\"number\">2.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">25.0</td>\n",
       "        <td class=\"number\">23</td>\n",
       "        <td class=\"number\">2.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">36.0</td>\n",
       "        <td class=\"number\">22</td>\n",
       "        <td class=\"number\">2.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">29.0</td>\n",
       "        <td class=\"number\">20</td>\n",
       "        <td class=\"number\">2.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (78)</td>\n",
       "        <td class=\"number\">467</td>\n",
       "        <td class=\"number\">52.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">177</td>\n",
       "        <td class=\"number\">19.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:38%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-4171155060974119712\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.42</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.67</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.75</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.83</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.92</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">70.0</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">70.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">71.0</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">74.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">80.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Cabin\">Cabin<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"alert\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>148</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>16.6%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>77.1%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>687</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-1421855812487504818\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>G6</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.4%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        4\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>C23 C25 C27</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.4%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        4\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>B96 B98</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.4%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        4\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (144)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:28%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 21.5%\">\n",
       "            192\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"missing\">\n",
       "    <th>(Missing)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 77.1%\">\n",
       "            687\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-1421855812487504818, #minifreqtable-1421855812487504818\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-1421855812487504818\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">G6</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C23 C25 C27</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">B96 B98</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">D</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">F2</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">F33</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">E101</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C22 C26</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C124</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">D35</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (137)</td>\n",
       "        <td class=\"number\">173</td>\n",
       "        <td class=\"number\">19.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">687</td>\n",
       "        <td class=\"number\">77.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Embarked\">Embarked<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>0.4%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.2%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>2</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-1431686408448639776\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>S</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 72.3%\">\n",
       "            644\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>C</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:26%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 18.9%\">\n",
       "            168\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>Q</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:12%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 8.6%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        77\n",
       "    </td>\n",
       "</tr><tr class=\"missing\">\n",
       "    <th>(Missing)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.2%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        2\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-1431686408448639776, #minifreqtable-1431686408448639776\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-1431686408448639776\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">S</td>\n",
       "        <td class=\"number\">644</td>\n",
       "        <td class=\"number\">72.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C</td>\n",
       "        <td class=\"number\">168</td>\n",
       "        <td class=\"number\">18.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:26%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Q</td>\n",
       "        <td class=\"number\">77</td>\n",
       "        <td class=\"number\">8.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Fare\">Fare<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>248</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>27.8%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>32.204</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>512.33</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>1.7%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-3293906366883129334\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABE0lEQVR4nO3dsQkCQRBAURVLsgh7MrYni7CnNRf56MJ5h76XL0zyGSba/Rhj7ICXDmsPAFt2XHuAZ6fL7eM39%2Bt5gUnABoEkEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgbC5f9Jn%2BFudpdggEAQCQSAQBALhJ470GQ573vG3gXzDTIQzhLuc/RhjrD0EbJUbBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBMIDkmkUlTnQli0AAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-3293906366883129334,#minihistogram-3293906366883129334\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-3293906366883129334\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-3293906366883129334\"\n",
       "                                                  aria-controls=\"quantiles-3293906366883129334\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-3293906366883129334\" aria-controls=\"histogram-3293906366883129334\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-3293906366883129334\" aria-controls=\"common-3293906366883129334\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-3293906366883129334\" aria-controls=\"extreme-3293906366883129334\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-3293906366883129334\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>7.225</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>7.9104</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>14.454</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>31</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>112.08</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>512.33</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>512.33</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>23.09</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>49.693</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.5431</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>33.398</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>32.204</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>28.164</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>4.7873</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>28694</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>2469.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>7.0 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-3293906366883129334\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAgAElEQVR4nO3df1iUdb7/8RcwCCPKYUypzavvpQnkmnZgxR9kUrKSm4q1iHHU46a1uadI0kvRSks3I93VrJTqci2zlHNE3diyyGj3mHVKEcu03GihMrflJCioCI4g3N8/WufsLBZIH7pn4Pm4Lv7wc9/MvD%2BvxF5zzw8CLMuyBAAAAGMC7R4AAACgo6FgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDHHYP0FlUVtYYv83AwAD16BGmqqpaNTVZxm%2B/MyJT88jUPDJtH%2BRqni9k2qtXd1vulytYfiwwMEABAQEKDAywe5QOg0zNI1PzyLR9kKt5nTlTChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGOawewB8P/ELd9g9Qqu9PnuE3SMAAPCD4AoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLAOWbBeeeUVxcXFeX0NHDhQAwcOlCTt2rVLKSkpio2N1U033aSdO3d6ff%2B6deuUmJio2NhYTZs2TZ9//rkd2wAAAH6qQxasCRMmaP/%2B/Z6vHTt2KCIiQtnZ2Tp8%2BLBmzZqle%2B%2B9V/v27dOsWbM0e/ZsHT16VJKUn5%2BvjRs36rnnnlNRUZGuvvpqZWZmyrIsm3cFAAD8RYcsWP/IsixlZWXphhtu0M0336z8/HzFx8dr9OjRcjgcGjt2rIYMGaK8vDxJ0pYtWzRlyhRFR0crJCREc%2BfOVXl5uYqKimzeCQAA8BcdvmC9/PLLKisr03333SdJKisrU0xMjNc5UVFRKikpueDx4OBg9enTx3McAACgJR36lz03NTXpmWee0X/8x3%2BoW7dukqTa2lo5nU6v80JDQ1VXV9eq461RUVGhyspKrzWHo6siIyPbso1vFRTkX/3Y4fD9ec9n6m/Z%2BjIyNY9M2we5mteZM%2B3QBauoqEgVFRVKS0vzrDmdTrndbq/z3G63wsLCWnW8NfLy8pSTk%2BO1lpGRoczMzIvdQoficrU%2BQ7uFhztbPgkXhUzNI9P2Qa7mdcZMO3TBeuONN5ScnKyuXbt61mJiYnTo0CGv88rKyjzvMIyOjlZpaalGjRolSWpoaNDhw4ebPa34XdLT05WUlOS15nB0VXV1bVu3ckH%2B9ojA9P7bQ1BQoMLDnTp16owaG5vsHqdDIFPzyLR9kKt5vpCpXQ/uO3TBev/99/WLX/zCa23ChAl6/vnnVVBQoBtvvFGFhYXau3evFi5cKEmaOHGi1qxZo8TERPXt21ePP/64evbsqfj4%2BFbfb2RkZLOnAysra3TuXOf%2BgfWn/Tc2NvnVvP6ATM0j0/ZBruZ1xkw7dMH66quvmhWdfv366amnntLKlSu1cOFC9e7dW2vWrFHfvn0lSWlpaaqpqVFGRoaqqqo0aNAgrV27VsHBwXZsAQAA%2BKEOXbD2799/wfWRI0dq5MiRFzwWEBCg22%2B/Xbfffnt7jgYAADow/3oRDwAAgB%2BgYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGddiCdeLECc2fP1/Dhg3TkCFDdPfdd6uiokKSdODAAU2aNElxcXFKSkrS1q1bvb43Pz9fycnJio2NVWpqqvbv32/HFgAAgJ/qsAVr1qxZqqur05tvvqmdO3cqKChIDz74oE6ePKmZM2fqlltuUXFxsbKzs7Vs2TIdPHhQklRUVKSlS5dq%2BfLlKi4u1oQJE3TXXXfpzJkzNu8IAAD4iw5ZsD7%2B%2BGMdOHBAy5cvV3h4uLp166alS5dq3rx5KiwsVEREhKZOnSqHw6GEhASlpKQoNzdXkrR161aNGzdOgwcPVnBwsKZPny6Xy6WCggKbdwUAAPyFw%2B4B2sPBgwcVFRWlLVu26L/%2B67905swZjRw5UgsWLFBpaaliYmK8zo%2BKitK2bdskSWVlZZo4cWKz4yUlJa2%2B/4qKClVWVnqtORxdFRkZ2cYdXVhQkH/1Y4fD9%2Bc9n6m/ZevLyNQ8Mm0f5GpeZ860QxaskydP6tNPP9XAgQOVn58vt9ut%2BfPna8GCBerZs6ecTqfX%2BaGhoaqrq5Mk1dbWfufx1sjLy1NOTo7XWkZGhjIzM9u4o47B5Qqze4RWCw93tnwSLgqZmkem7YNczeuMmXbIgtWlSxdJ0sKFCxUSEqJu3bpp9uzZuvXWW5Wamiq32%2B11vtvtVljYN//zdzqdFzzucrlaff/p6elKSkryWnM4uqq6urYt2/lW/vaIwPT%2B20NQUKDCw506deqMGhub7B6nQyBT88i0fZCreb6QqV0P7jtkwYqKilJTU5MaGhoUEhIiSWpq%2BuY/7I9//GP953/%2Bp9f5ZWVlio6OliRFR0ertLS02fHExMRW339kZGSzpwMrK2t07lzn/oH1p/03Njb51bz%2BgEzNI9P2Qa7mdcZM/esSSCtde%2B21uuKKK/TAAw%2BotrZWVVVVevzxxzV69GiNHz9ex44d04YNG9TQ0KA9e/Zo%2B/btntddpaWlafv27dqzZ48aGhq0YcMGHT9%2BXMnJyTbvCgAA%2BIsOWbCCg4O1ceNGBQUFacyYMRozZowuu%2BwyPfroo3K5XFq/fr127NihYcOGadGiRVq0aJGGDx8uSUpISNDixYu1ZMkSDR06VK%2B99prWrVuniIgIm3cFAAD8RYBlWZbdQ3QGlZU1xm/T4QhU8sp3jN9ue3l99gi7R2iRwxEolytM1dW1ne5ydnshU/PItH2Qq3m%2BkGmvXt1tud8OeQULAADAThQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYFiHLVgFBQUaMGCA4uLiPF9ZWVmSpF27diklJUWxsbG66aabtHPnTq/vXbdunRITExUbG6tp06bp888/t2MLAADAT3XYgvXRRx/p5ptv1v79%2Bz1fK1as0OHDhzVr1izde%2B%2B92rdvn2bNmqXZs2fr6NGjkqT8/Hxt3LhRzz33nIqKinT11VcrMzNTlmXZvCMAAOAvOnTBGjhwYLP1/Px8xcfHa/To0XI4HBo7dqyGDBmivLw8SdKWLVs0ZcoURUdHKyQkRHPnzlV5ebmKiop%2B6C0AAAA/1SELVlNTkw4dOqS33npLo0aNUmJioh588EGdPHlSZWVliomJ8To/KipKJSUlktTseHBwsPr06eM5DgAA0BKH3QO0h6qqKg0YMEBjxozR6tWrVV1drQULFigrK0v19fVyOp1e54eGhqqurk6SVFtb%2B53HW6OiokKVlZVeaw5HV0VGRrZxRxcWFORf/djh8P15z2fqb9n6MjI1j0zbB7ma15kz7ZAFq2fPnsrNzfX82el0KisrS7feequGDRsmt9vtdb7b7VZYWJjn3O863hp5eXnKycnxWsvIyFBmZubFbqVDcblan6HdwsOdLZ%2BEi0Km5pFp%2ByBX8zpjph2yYJWUlOjVV1/V3LlzFRAQIEmqr69XYGCgrrnmGn3yySde55eVlXlerxUdHa3S0lKNGjVKktTQ0KDDhw83e1rxu6SnpyspKclrzeHoqurq2u%2BzrWb87RGB6f23h6CgQIWHO3Xq1Bk1NjbZPU6HQKbmkWn7IFfzfCFTux7cd8iCFRERodzcXP3Lv/yLZsyYoYqKCq1YsUI///nPdcstt%2BiFF15QQUGBbrzxRhUWFmrv3r1auHChJGnixIlas2aNEhMT1bdvXz3%2B%2BOPq2bOn4uPjW33/kZGRzZ4OrKys0blznfsH1p/239jY5Ffz%2BgMyNY9M2we5mtcZM%2B2QBeuyyy7T2rVrtWrVKj3zzDMKCQnRuHHjlJWVpZCQED311FNauXKlFi5cqN69e2vNmjXq27evJCktLU01NTXKyMhQVVWVBg0apLVr1yo4ONjmXQEAAH8RYPEBTz%2BIysoa47fpcAQqeeU7xm%2B3vbw%2Be4TdI7TI4QiUyxWm6uraTvdoq72QqXlk2j7I1TxfyLRXr%2B623K9/vYgHAADAD1CwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIb5XMFqbGy0ewQAAIDvxecKVmJion7729%2BqrKzM7lEAAADaxOcK1j333KMPPvhA48eP16RJk7R582bV1Jj/PX4AAADtxecK1uTJk7V582bt2LFD1157rdatW6frrrtOc%2BfO1XvvvWf3eAAAAC3yuYJ1Xp8%2BfTRnzhzt2LFDGRkZ%2BtOf/qQ77rhDSUlJev7553mtFgAA8FkOuwf4NgcOHNAf/vAHFRQUqL6%2BXsnJyUpNTdXRo0f15JNP6qOPPtKqVavsHhMAAKAZnytYTz/9tF5%2B%2BWV9%2BeWXGjRokObMmaPx48erW7dunnOCgoL00EMP2TglAADAt/O5grVp0yZNmDBBaWlpioqKuuA5/fr107x5837gyQAAAFrH5wrW22%2B/rdOnT%2BvEiROetYKCAiUkJMjlckmSBgwYoAEDBtg1IgAAwHfyuRe5//nPf9aYMWOUl5fnWVuxYoVSUlL0l7/8xcbJAAAAWsfnCtZvf/tb3XjjjZozZ45n7Y9//KMSExO1fPlyGycDAABoHZ8rWIcOHdLMmTPVpUsXz1pQUJBmzpypDz/80MbJAAAAWsfnCla3bt105MiRZutff/21QkNDbZgIAADg4vhcwRozZoyWLFmi9957T6dPn1Ztba327Nmjhx9%2BWMnJyXaPBwAA0CKfexfh3Llz9de//lW33367AgICPOvJycmaP3%2B%2BjZMBAAC0js8VLKfTqbVr1%2BqLL77Qp59%2BquDgYPXr1099%2BvSxezQAAIBW8bmCdV7fvn3Vt29fu8cAAAC4aD5XsL744gs9/PDDev/999XQ0NDs%2BCeffGLDVAAAAK3ncwVryZIlKi8v17x589S9e3e7xwEAALhoPlew9u/frxdeeEFxcXF2jwIAANAmPvcxDS6XS2FhYXaPAQAA0GY%2BV7CmTZumVatWqaamxu5RAAAA2sTnniLctWuXPvzwQw0bNkyXXHKJ16/MkaQ//elPNk0GAADQOj5XsIYNG6Zhw4YZua3GxkZNnz5dvXv39vyi6F27dmnlypX661//qh/96EeaP3%2B%2BRo0a5fmedevWaePGjTp16pQGDRqkX//617ryyiuNzAMAADoHnytY99xzj7HbysnJ0b59%2B9S7d29J0uHDhzVr1iytWrVKN9xwgwoLCzV79mwVFhbq0ksvVX5%2BvjZu3KjnnntO/%2B///T89/vjjyszM1Pbt270%2BVR4AAOC7%2BNxrsCSppKRE999/v/7t3/5NR48eVW5uroqKii7qNnbv3q3CwkLdeOONnrX8/HzFx8dr9OjRcjgcGjt2rIYMGaK8vDxJ0pYtWzRlyhRFR0crJCREc%2BfOVXl5%2BUXfNwAA6Nx87grWxx9/rMmTJys2NlYff/yx6uvr9cknn%2BjRRx9VTk6O19N53%2Bb48eNauHChnn76aW3YsMGzXlZWppiYGK9zo6KiVFJS4jl%2B5513eo4FBwerT58%2BKikp0fDhw1u9h4qKClVWVnqtORxdFRkZ2erbaI2gIJ/sx9/K4fD9ec9n6m/Z%2BjIyNY9M2we5mteZM/W5grVy5UrdfvvtmjNnjuezsB555BF17969VQWrqalJWVlZmjFjhvr37%2B91rLa2Vk6n02stNDRUdXV1rTreWnl5ecrJyfFay8jIUGZm5kXdTkfjcvnPx2%2BEhztbPgkXhUzNI9P2Qa7mdcZMfa5gffzxx1q8eHGz9cmTJ2vz5s0tfv/atWvVpUsXTZs2rdkxp9Mpt9vtteZ2uz2fu9XS8dZKT09XUlKS15rD0VXV1bUXdTst8bdHBKb33x6CggIVHu7UqVNn1NjYZPc4HQKZmkem7YNczfOFTO16cO9zBSs4OFinT59utl5eXt7s6tKFvPzyy6qoqFB8fLwkeQrTH//4R02dOlWHDh3yOr%2BsrEwDBw6UJEVHR6u0tNRzlayhoUGHDx9u9rRiSyIjI5s9HVhZWaNz5zr3D6w/7b%2Bxscmv5vUHZGoembYPcjWvM2bqc5dARo8erccee0zV1dWetc8%2B%2B0zZ2dm64YYbWvz%2BHTt26IMPPtC%2Bffu0b98%2BjR8/XuPHj9e%2Bffs0YcIE7d27VwUFBTp37pwKCgq0d%2B9e3XzzzZKkiRMnatOmTSopKdHZs2f12GOPqWfPnp6yBgAA0Bo%2BdwVrwYIF%2BuUvf6lrr71WlmUpNTVVp0%2BfVv/%2B/TV//vzvddv9%2BvXTU089pZUrV2rhwoXq3bu31qxZo759%2B0qS0tLSVFNTo4yMDFVVVWnQoEFau3atgoODTWwNAAB0EgGWZVl2D3Ehu3fv1p///Gc1NTUpJiZGI0eOVGCgz11wa7XKSvO/%2BsfhCFTyyneM3257eX32CLtHaJHDESiXK0zV1bWd7nJ2eyFT88i0fZCreb6Qaa9e3W25X5%2B7gnVeQkKCEhIS7B4DAADgovlcwUpKSvrOT03ndxECAABf53MF6%2Bc//7lXwWpoaNCXX36pt99%2BW7Nnz7ZxMgAAgNbxuYI1a9asC65v2rRJ77//vn7xi1/8wBMBAABcHL951fioUaO0a9cuu8cAAABokd8UrL179yokJMTuMQAAAFrkc08R/vNTgJZl6fTp0/r00095ehAAAPgFnytYl19%2BebN3EQYHB%2Bu2225TSkqKTVMBAAC0ns8VrOXLl9s9AgAAwPficwWruLi41ecOGTKkHScBAABoG58rWNOnT5dlWZ6v884/bXh%2BLSAgQJ988oktMwIAAHwXnytYa9as0bJly7RgwQINHz5cwcHBOnDggJYsWaIpU6Zo1KhRdo8IAADwnXzuYxp%2B85vfaPHixRo9erS6deumkJAQDR06VA8//LDWr1%2Bv3r17e74AAAB8kc8VrIqKCv3oRz9qtt6tWzdVV1fbMBEAAMDF8bmCFRsbq1WrVun06dOetRMnTmjFihVKSEiwcTIAAIDW8bnXYC1atEi33XabEhMT1adPH0nSF198oV69eunFF1%2B0dzgAAIBW8LmC1a9fPxUUFGj79u367LPPJElTpkzRuHHj5HQ6bZ4OAACgZT5XsCQpPDxckyZN0ldffaUrrrhC0jef5g4AAOAPfO41WJZlaeXKlRoyZIjGjx%2Bvr7/%2BWgsWLND999%2BvhoYGu8cDAABokc8VrI0bN%2Brll1/W4sWL1aVLF0nS6NGj9d///d968sknbZ4OAACgZT5XsPLy8vTQQw8pNTXV8%2BntY8eOVXZ2tl577TWbpwMAAGiZzxWsr776Sj/%2B8Y%2BbrV911VU6duyYDRMBAABcHJ8rWL1799bBgwebre/atcvzgncAAABf5nPvIrzjjjv061//WkePHpVlWdq9e7c2b96sjRs36v7777d7PAAAgBb5XMGaOHGizp07p2eeeUZut1sPPfSQLrnkEs2ZM0eTJ0%2B2ezwAAIAW%2BVzBeuWVV/Szn/1M6enpqqqqkmVZuuSSS%2BweCwAAoNV87jVYjzzyiOfF7D169KBcAQAAv%2BNzBatPnz769NNP7R4DAACgzXzuKcLo6GjNmzdPzz77rPr06aOQkBCv48uWLbNpMgAAgNbxuYJ15MgRDR48WJJUWVlp8zQAAAAXzycK1rJly3Tvvfeqa9eu2rhxo93jAAAAfC8%2B8RqsF198UWfOnPFau%2BOOO1RRUWHTRAAAAG3nEwXLsqxmax988IHOnj3b5tvcvXu3Jk2apJ/85CcaMWKEli5dKrfbLUk6cOCAJk2apLi4OCUlJWnr1q1e35ufn6/k5GTFxsYqNTVV%2B/fvb/McAACg8/GJgmVaVVWVfvWrX2ny5Mnat2%2Bf8vPztXfvXv3ud7/TyZMnNXPmTN1yyy0qLi5Wdna2li1b5vn1PEVFRVq6dKmWL1%2Bu4uJiTZgwQXfddVezK2wAAADfpkMWrB49eui9995TamqqAgICdOLECZ09e1Y9evRQYWGhIiIiNHXqVDkcDiUkJCglJUW5ubmSpK1bt2rcuHEaPHiwgoODNX36dLlcLhUUFNi8KwAA4C984kXukhQQEGD09rp16yZJuv7663X06FHFx8crNTVVTzzxhGJiYrzOjYqK0rZt2yRJZWVlmjhxYrPjJSUlrb7vioqKZu%2BAdDi6KjIysi1b%2BVZBQf7Vjx0O35/3fKb%2Blq0vI1PzyLR9kKt5nTlTnylYjzzyiNdnXjU0NGjFihUKCwvzOu9iPwersLBQJ0%2Be1Lx585SZmalLL71UTqfT65zQ0FDV1dVJkmpra7/zeGvk5eUpJyfHay0jI0OZmZkXNXtH43KFtXySjwgPd7Z8Ei4KmZpHpu2DXM3rjJn6RMEaMmRIsys%2BcXFxqq6uVnV19fe67dDQUIWGhiorK0uTJk3StGnTVFNT43WO2%2B32FDmn0%2Bl5Mfw/Hne5XK2%2Bz/T0dCUlJXmtORxdVV1d28ZdXJi/PSIwvf/2EBQUqPBwp06dOqPGxia7x%2BkQyNQ8Mm0f5GqeL2Rq14N7nyhYpj/76oMPPtADDzygV155RV26dJEk1dfXKzg4WFFRUXr33Xe9zi8rK1N0dLSkbz5JvrS0tNnxxMTEVt9/ZGRks6cDKytrdO5c5/6B9af9NzY2%2BdW8/oBMzSPT9kGu5nXGTP3rEkgrXXXVVXK73XrsscdUX1%2Bvv/3tb/rNb36jtLQ0jRkzRseOHdOGDRvU0NCgPXv2aPv27Z7XXaWlpWn79u3as2ePGhoatGHDBh0/flzJyck27woAAPgLn7iCZVpYWJieffZZPfrooxoxYoS6d%2B%2BulJQUZWRkqEuXLlq/fr2ys7O1evVq9ejRQ4sWLdLw4cMlSQkJCVq8eLGWLFmio0ePKioqSuvWrVNERITNuwIAAP4iwLrQp3zCuMrKmpZPukgOR6CSV75j/Hbby%2BuzR9g9QoscjkC5XGGqrq7tdJez2wuZmkem7YNczfOFTHv16m7L/XbIpwgBAADsRMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhnXYglVSUqIZM2Zo6NChGjFihObPn6%2BqqipJ0oEDBzRp0iTFxcUpKSlJW7du9fre/Px8JScnKzY2Vqmpqdq/f78dWwAAAH6qQxYst9utX/7yl4qLi9P//M//6NVXX9WJEyf0wAMP6OTJk5o5c6ZuueUWFRcXKzs7W8uWLdPBgwclSUVFRVq6dKmWL1%2Bu4uJiTZgwQXfddZfOnDlj864AAIC/6JAFq7y8XP3791dGRoa6dOkil8ul9PR0FRcXq7CwUBEREZo6daocDocSEhKUkpKi3NxcSdLWrVs1btw4DR48WMHBwZo%2BfbpcLpcKCgps3hUAAPAXHbJgXXnllXr22WcVFBTkWXvjjTd09dVXq7S0VDExMV7nR0VFqaSkRJJUVlb2nccBAABa4rB7gPZmWZaeeOIJ7dy5U5s2bdKLL74op9PpdU5oaKjq6uokSbW1td95vDUqKipUWVnpteZwdFVkZGQbd3FhQUH%2B1Y8dDt%2Bf93ym/patLyNT88i0fZCreZ050w5dsE6fPq37779fhw4d0qZNm3TVVVfJ6XSqpqbG6zy3262wsDBJktPplNvtbnbc5XK1%2Bn7z8vKUk5PjtZaRkaHMzMw27qRjcLnC7B6h1cLDnS2fhItCpuaRafsgV/M6Y6YdtmAdOXJEd955py6//HJt27ZNPXr0kCTFxMTo3Xff9Tq3rKxM0dHRkqTo6GiVlpY2O56YmNjq%2B05PT1dSUpLXmsPRVdXVtW3Zyrfyt0cEpvffHoKCAhUe7tSpU2fU2Nhk9zgdApmaR6btg1zN84VM7Xpw3yEL1smTJ3Xbbbdp%2BPDhys7OVmDg/xWR5ORkrVixQhs2bNDUqVP1/vvva/v27Xr66aclSWlpacrIyNBNN92kwYMHKzc3V8ePH1dycnKr7z8yMrLZ04GVlTU6d65z/8D60/4bG5v8al5/QKbmkWn7IFfzOmOmHbJgvfTSSyovL9frr7%2BuHTt2eB3bv3%2B/1q9fr%2BzsbK1evVo9evTQokWLNHz4cElSQkKCFi9erCVLlujo0aOKiorSurXtfyMAAA4lSURBVHXrFBERYcdWAACAHwqwLMuye4jOoLKypuWTLpLDEajkle8Yv9328vrsEXaP0CKHI1AuV5iqq2s73aOt9kKm5pFp%2ByBX83wh0169uttyv/71Ih4AAAA/QMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADOvwBauqqkrJyckqKiryrB04cECTJk1SXFyckpKStHXrVq/vyc/PV3JysmJjY5Wamqr9%2B/f/0GMDAAA/1qEL1vvvv6/09HQdOXLEs3by5EnNnDlTt9xyi4qLi5Wdna1ly5bp4MGDkqSioiItXbpUy5cvV3FxsSZMmKC77rpLZ86csWsbAADAzzjsHqC95Ofna/Xq1crKytKcOXM864WFhYqIiNDUqVMlSQkJCUpJSVFubq6uueYabd26VePGjdPgwYMlSdOnT1deXp4KCgo0ceJEW/bSUdz0xLt2j3BRXp89wu4RAAB%2BqsMWrOuuu04pKSlyOBxeBau0tFQxMTFe50ZFRWnbtm2SpLKysmZFKioqSiUlJa2%2B74qKClVWVnqtORxdFRkZebHb%2BE5BQR36AqTtHA7yNeH831P%2BvppDpu2DXM3rzJl22ILVq1evC67X1tbK6XR6rYWGhqqurq5Vx1sjLy9POTk5XmsZGRnKzMxs9W3Afi5XmN0jdCjh4c6WT8JFIdP2Qa7mdcZMO2zB%2BjZOp1M1NTVea263W2FhYZ7jbre72XGXy9Xq%2B0hPT1dSUpLXmsPRVdXVtW2c%2BsI64yOCH5Lp/16dVVBQoMLDnTp16owaG5vsHqdDINP2Qa7m%2BUKmdj1Y7nQFKyYmRu%2B%2B6/1aoLKyMkVHR0uSoqOjVVpa2ux4YmJiq%2B8jMjKy2dOBlZU1OneOH1h/wn8vsxobm8jUMDJtH%2BRqXmfMtNNdAklOTtaxY8e0YcMGNTQ0aM%2BePdq%2BfbvndVdpaWnavn279uzZo4aGBm3YsEHHjx9XcnKyzZMDAAB/0emuYLlcLq1fv17Z2dlavXq1evTooUWLFmn48OGSvnlX4eLFi7VkyRIdPXpUUVFRWrdunSIiImyeHAAA%2BItOUbA%2B/fRTrz8PGjRImzdv/tbzb775Zt18883tPRYAAOigOt1ThAAAAO2NggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAxz2D0A4KtueuJdu0e4KK/PHmH3CACAv%2BMKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYn4N1AcePH9eDDz6ovXv3KigoSBMmTNCCBQvkcBAXYAKfMQago%2BMK1gXMnj1bXbt21TvvvKNt27Zp9%2B7d2rBhg91jAQAAP0HB%2Bidffvml9u7dq6ysLDmdTl1xxRW6%2B%2B67lZuba/doAADAT/Cc1z8pLS1VRESELr30Us9av379VF5erlOnTik8PNzG6QDYgac0AVwsCtY/qa2tldPp9Fo7/%2Be6urpWFayKigpVVlZ6rTkcXRUZGWluUElBQVyAxP/xtxKA9uNw8G9DW5z/N7Uj/duavPIdu0dotTfnjbR7BKMoWP%2Bka9euOnPmjNfa%2BT%2BHhYW16jby8vKUk5PjtXbPPfdo1qxZZob8u4qKCt12WanS09ONl7fOqqKiQnl5eWRqEJmaR6bto6KiQi%2B88GyHynVf9s9svf/O/He149R0Q6Kjo3XixAkdO3bMs/bZZ5/psssuU/fu3Vt1G%2Bnp6XrppZe8vtLT043PWllZqZycnGZXy9B2ZGoemZpHpu2DXM3rzJlyBeuf9OnTR4MHD9ajjz6qhx9%2BWNXV1Xr66aeVlpbW6tuIjIzsdE0dAAD8H65gXcDq1at17tw5/fSnP9Wtt96qkSNH6u6777Z7LAAA4Ce4gnUBPXv21OrVq%2B0eAwAA%2BKmgJUuWLLF7CLRdWFiYhg4d2uoX4KNlZGoemZpHpu2DXM3rrJkGWJZl2T0EAABAR8JrsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbD81PHjx3X33XcrPj5ew4YNU3Z2ts6dO2f3WH6hqqpKycnJKioq8qwdOHBAkyZNUlxcnJKSkrR161av78nPz1dycrJiY2OVmpqq/fv3/9Bj%2B6SSkhLNmDFDQ4cO1YgRIzR//nxVVVVJItO22r17tyZNmqSf/OQnGjFihJYuXSq32y2JTL%2BvxsZGTZs2Tffdd59nbdeuXUpJSVFsbKxuuukm7dy50%2Bt71q1bp8TERMXGxmratGn6/PPPf%2BixfVZBQYEGDBiguLg4z1dWVpYkcpUkWfBL//7v/27NnTvXqqurs44cOWKNGzfOWrdund1j%2Bbx9%2B/ZZo0ePtmJiYqw9e/ZYlmVZJ06csIYOHWpt2rTJamhosN577z0rLi7OOnDggGVZlrVnzx4rLi7O2rdvn1VfX289//zz1rBhw6y6ujo7t2K7M2fOWCNGjLCefPJJ6%2BzZs1ZVVZV15513Wr/61a/ItI2OHz9uDRo0yPr9739vNTY2WkePHrXGjx9vPfnkk2RqwBNPPGH179/fWrBggWVZlvXFF19YgwYNst58802roaHBeu2116xrrrnG%2Bvrrry3LsqyXXnrJGjlypPWXv/zFcrvd1rJly6xx48ZZTU1Ndm7DZyxfvty67777mq2T6ze4guWHvvzyS%2B3du1dZWVlyOp264oordPfddys3N9fu0Xxafn6%2B5s2bpzlz5nitFxYWKiIiQlOnTpXD4VBCQoJSUlI8eW7dulXjxo3T4MGDFRwcrOnTp8vlcqmgoMCObfiM8vJy9e/fXxkZGerSpYtcLpfS09NVXFxMpm3Uo0cPvffee0pNTVVAQIBOnDihs2fPqkePHmT6Pe3evVuFhYW68cYbPWv5%2BfmKj4/X6NGj5XA4NHbsWA0ZMkR5eXmSpC1btmjKlCmKjo5WSEiI5s6dq/Lycq%2Br353ZRx99pIEDBzZbJ9dvULD8UGlpqSIiInTppZd61vr166fy8nKdOnXKxsl823XXXac333xTY8eO9VovLS1VTEyM11pUVJRKSkokSWVlZd95vLO68sor9eyzzyooKMiz9sYbb%2Bjqq68m0%2B%2BhW7dukqTrr79eKSkp6tWrl1JTU8n0ezh%2B/LgWLlyoxx57TE6n07PeUmb/fDw4OFh9%2BvQhU0lNTU06dOiQ3nrrLY0aNUqJiYl68MEHdfLkSXL9OwqWH6qtrfX6R0KS5891dXV2jOQXevXqJYfD0Wz9QnmGhoZ6smzpOCTLsvT4449r586dWrhwIZkaUFhYqLfffluBgYHKzMwk0zZqampSVlaWZsyYof79%2B3sdI9O2q6qq0oABAzRmzBgVFBRo8%2BbNOnz4sLKyssj17yhYfqhr1646c%2BaM19r5P4eFhdkxkl9zOp2eFxGf53a7PVm2dLyzO336tDIzM7V9%2B3Zt2rRJV111FZkaEBoaqksvvVRZWVl65513yLSN1q5dqy5dumjatGnNjpFp2/Xs2VO5ublKS0uT0%2BnU5ZdfrqysLL399tuyLItcRcHyS9HR0Tpx4oSOHTvmWfvss8902WWXqXv37jZO5p9iYmJUWlrqtVZWVqbo6GhJ3%2BT9Xcc7syNHjmjixIk6ffq0tm3bpquuukoSmbbVBx98oJ/97Geqr6/3rNXX1ys4OFhRUVFk2gYvv/yy9u7dq/j4eMXHx%2BvVV1/Vq6%2B%2Bqvj4%2BIv%2Be9rQ0KDDhw83e/qrMyopKdHKlStlWZZnrb6%2BXoGBgbrmmmvIVeJdhP5q8uTJ1pw5c6yamhrPuwhXr15t91h%2B4x/fRVhVVWXFx8dbzz//vFVfX2/t3r3biouLs3bv3m1ZluV5t9bu3bs9784aMmSIVV1dbecWbHfixAnrhhtusO677z6rsbHR6xiZts3p06et66%2B/3nr00Uets2fPWl999ZWVlpZmLV68mEwNWbBggeddhGVlZdagQYOs1157zfNut0GDBlmff/65ZVmWtWXLFmvkyJHWJ5984nm3W3JyslVfX2/nFnzC//7v/1qxsbHW7373O6uhocH629/%2BZt16663WAw88QK5/R8HyU5WVldasWbOsoUOHWsOHD7eWL19unTt3zu6x/MY/FizLsqyDBw9a6enpVlxcnPXTn/7U%2Bv3vf%2B91/h/%2B8AdrzJgxVmxsrJWWlmZ9%2BOGHP/TIPmf9%2BvVWTEyM9a//%2Bq9WbGys15dlkWlblZaWWjNmzLDi4%2BOtUaNGWatWrbLOnj1rWRaZmvCPBcuyLOvtt9%2B2JkyYYMXGxlrjxo2z3nrrLc%2BxpqYm67nnnrOSkpKs2NhYa9q0aZ6SAMsqKiry/H0cPny4tXTpUsvtdluWRa6WZVkBlvUP1/cAAADwvfEaLAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8P1L48KDHAAWoAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-3293906366883129334\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">8.05</td>\n",
       "        <td class=\"number\">43</td>\n",
       "        <td class=\"number\">4.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">13.0</td>\n",
       "        <td class=\"number\">42</td>\n",
       "        <td class=\"number\">4.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.8958</td>\n",
       "        <td class=\"number\">38</td>\n",
       "        <td class=\"number\">4.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.75</td>\n",
       "        <td class=\"number\">34</td>\n",
       "        <td class=\"number\">3.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">26.0</td>\n",
       "        <td class=\"number\">31</td>\n",
       "        <td class=\"number\">3.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10.5</td>\n",
       "        <td class=\"number\">24</td>\n",
       "        <td class=\"number\">2.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.925</td>\n",
       "        <td class=\"number\">18</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.775</td>\n",
       "        <td class=\"number\">16</td>\n",
       "        <td class=\"number\">1.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">26.55</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (238)</td>\n",
       "        <td class=\"number\">615</td>\n",
       "        <td class=\"number\">69.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-3293906366883129334\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">15</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.0125</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6.2375</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6.4375</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">227.525</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">247.5208</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">262.375</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">263.0</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">512.3292</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:75%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Name\">Name<br/>\n",
       "            <small>Categorical, Unique</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3 collapse in\" id=\"minivalues-9188296569047803955\"><table border=\"1\" class=\"dataframe example_values\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>First 3 values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Mockler, Miss. Helen Mary \"Ellie\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\")</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minivalues-9188296569047803955\"><table border=\"1\" class=\"dataframe example_values\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Last 3 values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Gustafsson, Mr. Karl Gideon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dowdell, Miss. Elizabeth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#values-9188296569047803955,#minivalues-9188296569047803955\" aria-expanded=\"false\"\n",
       "       aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 collapse\" id=\"values-9188296569047803955\">\n",
       "    <p class=\"h4\">First 10 values</p>\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">Abbing, Mr. Anthony</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Abbott, Mr. Rossmore Edward</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Abelson, Mr. Samuel</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "    <p class=\"h4\">Last 10 values</p>\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">de Mulder, Mr. Theodore</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">de Pelsmaeker, Mr. Alfons</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">del Carlo, Mr. Sebastiano</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">van Billiard, Mr. Austin Blyler</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">van Melkebeke, Mr. Philemon</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Parch\">Parch<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.8%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.38159</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>6</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>76.1%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-8289598693405372876\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABG0lEQVR4nO3dwQnCQBBAUSOWZBH25NmeLMKe1rvIhwRior53X5jLZ5K97DTGGAfgrePWA8CenbYe4NX5ep995nG7rDAJ2CCQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJhd89AL%2BHpaNZig0D4iQ3yKXM3lS31/WwQCAKBIBAIAoHgJ31nllxZz7Xk8uBfr9KnMcbYegjYK59YEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEJ4mkheVLZBUIQAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-8289598693405372876,#minihistogram-8289598693405372876\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-8289598693405372876\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-8289598693405372876\"\n",
       "                                                  aria-controls=\"quantiles-8289598693405372876\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-8289598693405372876\" aria-controls=\"histogram-8289598693405372876\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-8289598693405372876\" aria-controls=\"common-8289598693405372876\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-8289598693405372876\" aria-controls=\"extreme-8289598693405372876\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-8289598693405372876\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.80606</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>2.1123</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>9.7781</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.38159</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.58074</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>2.7491</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>340</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.64973</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>7.0 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-8289598693405372876\">\n",
       "            <img 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r%2Bcpv3yKEAAAwE4ULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGFeV7A6OjrsHgEAAOCCeF3BSklJ0ZNPPqnq6mq7RwEAADgvXlewHnjgAX3yySeaMmWKsrKy9Morr6ipqcnusQAAADzmdQXrtttu0yuvvKLt27fruuuu0/r163X99ddr/vz5%2Buijj%2BweDwAAwJLXFayzhgwZonnz5mn79u3Kzc3Vu%2B%2B%2Bq3vvvVepqal6%2BeWXea0WAADwWg67B/hX9u/frz/84Q8qLS3VmTNnlJaWpoyMDB09elS/%2Bc1v9Omnn%2Bqpp56ye0wAAIAevK5gPf/883r99df19ddfa%2BTIkZo3b56mTJmiiIiI7nOCgoL06KOP2jglAADAv%2BZ1BWvTpk2aOnWqMjMzFRsb%2B73nDB06VAsWLLjIkwEAAHjG6wrWe%2B%2B9p1OnTunEiRPda6WlpUpOTlZ0dLQkadiwYRo2bJhdIwIAALjldS9y/8tf/qJJkyapuLi4e23VqlVKT0/X//7v/9o4GQAAgGe8rmA9%2BeSTuummmzRv3rzutT/96U9KSUnRypUrz%2BlaHR0dysnJ0S9/%2BcvutV27dik9PV0JCQm6%2BeabtXPnTpevWb9%2BvVJSUpSQkKCcnBx9%2BeWXF7YhAADQ53hdwTp48KBmzZqlfv36da8FBQVp1qxZ%2BvOf/3xO1yosLNTevXu7Pz98%2BLDmzJmjBx98UHv37tWcOXM0d%2B5cHT16VJJUUlKijRs36sUXX1RZWZmGDx%2BuvLw8dXV1mdkcAADoE7yuYEVEROjIkSM91v/%2B978rNDTU4%2Bvs3r1bO3bs0E033dS9VlJSoqSkJE2cOFEOh0O33HKLRo8e3f105Kuvvqrbb79dcXFxCgkJ0fz581VTU6OysrIL3xgAAOgzvO5F7pMmTdKyZcv0q1/9Stdcc40CAgL06aef6rHHHlNaWppH1zh%2B/LgWL16s559/XkVFRd3r1dXVio%2BPdzk3NjZWlZWV3cfvu%2B%2B%2B7mPBwcEaMmSIKisrNW7cOI/3UFtbq7q6Opc1hyNMMTExHl/DE0FBXteP3XI4Lv68ZzPytawuFvKxRkbWyMg98rHmjxl5XcGaP3%2B%2B/vrXv%2Bqee%2B5RQEBA93paWpoWLlxo%2BfWdnZ3Kz8/X3XffrauuusrlWHNzs5xOp8taaGioWlpaPDruqeLiYhUWFrqs5ebmKi8v75yu42%2Bio8Nte%2BzISKf1SX0Y%2BVgjI2tk5B75WPOnjLyuYDmdTq1bt05fffWVDh06pODgYA0dOlRDhgzx6OvXrVunfv36KScn53uv3dra6rLW2tqq8PBwj457Kjs7W6mpqS5rDkeYGhqaz%2Bk6Vnyt6ZvevyeCggIVGenUyZOn1dHRedEf39uRjzUyskZG7pGPtd7MyK6/3HtdwTrriiuu0BVXXHHOX/f666%2BrtrZWSUlJktRdmP70pz/pjjvu0MGDB13Or66u1ogRIyRJcXFxqqqq0oQJEyRJbW1tOnz4cI%2BnFa3ExMT0eDqwrq5J7e19%2BxvLzv13dHT2%2BfzdIR9rZGSNjNwjH2v%2BlJHXFayvvvpKjz32mD7%2B%2BGO1tbX1OP7555%2B7/frt27e7fH72LRpWrlypL774Qi%2B//LJKS0t10003aceOHSovL9fixYslSTNmzNCzzz6rlJQUXXHFFXr66ac1cODA7rIGAADgCa8rWMuWLVNNTY0WLFig/v37G7320KFD9dxzz2n16tVavHixLrvsMj377LPdd8oyMzPV1NSk3Nxc1dfXa%2BTIkVq3bp2Cg4ONzgEAAPxbQJeXvcnTNddco9/97ndKTEy0exSj6uqajF/T4QhU2ur3jV%2B3t7w1d/xFf0yHI1DR0eFqaGj2m9vOJpGPNTKyRkbukY%2B13sxo0CCzN2s85XWvko6Ojj7nF5UDAAB4E68rWDk5OXrqqafU1GT%2Bjg8AAMDF4HWvwdq1a5f%2B/Oc/a%2BzYsbrkkktc/skcSXr33XdtmgwAAMAzXlewxo4dq7Fjx9o9BgAAwHnzuoL1wAMP2D0CAADABfG612BJUmVlpR566CH9%2B7//u44eParNmzfzDy4DAACf4XUF67PPPlNWVpa%2B%2BeYbffbZZzpz5ow%2B//xz3XPPPdq5c6fd4wEAAFjyuoK1evVq3XPPPdq4cWP3G3z%2B%2Bte/1s9%2B9rMe/4AyAACAN/K6gvXZZ59p%2BvTpPdZvu%2B02ffnllzZMBAAAcG68rmAFBwfr1KlTPdZramrkdDptmAgAAODceF3BmjhxotasWaOGhobutS%2B%2B%2BEIFBQX68Y9/bN9gAAAAHvK6grVo0SK1trbquuuu0%2BnTp5WRkaEpU6bI4XBo4cKFdo8HAABgyeveBysiIkKvvPKKdu/erb/85S/q7OxUfHy8brjhBgUGel0fBAAA6MHrCtZZycnJSk5OtnsMAACAc%2BZ1BSs1NVUBAQH/8jj/FiEAAPB2Xlewbr31VpeC1dbWpq%2B//lrvvfee5s6da%2BNkAAAAnvG6gjVnzpzvXd%2B0aZM%2B/vhj/exnP7vIEwEAAJwbn3nV%2BIQJE7Rr1y67xwAAALDkMwWrvLxcISEhdo8BAABgyeueIvznpwC7urp06tQpHTp0iKcHAQCAT/C6gvXDH/6wx28RBgcH684771R6erpNUwEAAHjO6wrWypUr7R4BAADggnhdwaqoqPD43NGjR/fiJAAAAOfH6wrWXXfdpa6uru6Ps84%2BbXh2LSAgQJ9//rktMwIAALjjdQXr2Wef1YoVK7Ro0SKNGzdOwcHB2r9/v5YtW6bbb79dEyZMsHtEAAAAt7zubRqeeOIJLV26VBMnTlRERIRCQkI0ZswYPfbYY3rppZd02WWXdX8AAAB4I68rWLW1tfq3f/u3HusRERFqaGiwYSIAAIBz43UFKyEhQU899ZROnTrVvXbixAmtWrVKycnJNk4GAADgGa97DdaSJUt05513KiUlRUOGDJEkffXVVxo0aJB%2B//vf2zscAACAB7yuYA0dOlSlpaXatm2bvvjiC0nS7bffrsmTJ8vpdNo8HQAAgDWvK1iSFBkZqaysLH3zzTe6/PLLJX33bu4AAAC%2BwOteg9XV1aXVq1dr9OjRmjJliv7%2B979r0aJFeuihh9TW1mb3eAAAAJa8rmBt3LhRr7/%2BupYuXap%2B/fpJkiZOnKj//u//1m9%2B8xubpwMAALDmdQWruLhYjz76qDIyMrrfvf2WW25RQUGB/vjHP9o8HQAAgDWvK1jffPONrr766h7rV155pY4dO2bDRAAAAOfG6wrWZZddpgMHDvRY37VrV/cL3j2xe/duZWVl6dprr9X48eO1fPlytba2SpL279%2BvrKwsJSYmKjU1VVu2bHH52pKSEqWlpSkhIUEZGRnat2/fhW0KAAD0KV5XsO6991796le/0ssvv6yuri7t3r1bq1at0pNPPqmcnByPrlFfX69f/OIXuu2227R3716VlJSovLxcv/3tb9XY2KhZs2Zp%2BvTpqqioUEFBgVasWNFd6srKyrR8%2BXKtXLlSFRUVmjp1qu6//36dPn26N7cNAAD8iNe9TcOMGTPU3t6uF154Qa2trXr00Ud1ySWXaN68ebrttts8usaAAQP00UcfKSIiQl1dXTpx4oS%2B/fZbDRgwQDt27FBUVJTuuOMOSVJycrLS09O1efNmXXPNNdqyZYsmT56sUaNGSZLuuusuFRcXq7S0VDNmzOi1fQMAAP/hdQXrjTfe0E9/%2BlNlZ2ervr5eXV1duuSSS875OhEREZKkG2%2B8UUePHlVSUpIyMjL0zDPPKD4%2B3uXc2NhYbd26VZJUXV3do0jFxsaqsrLS48eura1VXV2dy5rDEaaYmJhz3oc7QUFedwPSLYfj4s97NiNfy%2BpiIR9rZGSNjNwjH2v%2BmJHXFaxf//rXGj58uH7wgx9owIABF3y9HTt2qLGxUQsWLFBeXp4uvfTSHu8IHxoaqpaWFklSc3Oz2%2BOeKC4uVmFhoctabm6u8vLyznMX/iE6Oty2x46M5F8BcId8rJGRNTJyj3ys%2BVNGXlewhgwZokOHDmno0KFGrhcaGqrQ0FDl5%2BcrKytLOTk5ampqcjmntbVV4eHf/fB3Op3dL4b/x%2BPR0dEeP2Z2drZSU1Nd1hyOMDU0NJ/nLr6frzV90/v3RFBQoCIjnTp58rQ6Ojov%2BuN7O/KxRkbWyMg98rHWmxnZ9Zd7rytYcXFxWrBggTZs2KAhQ4YoJCTE5fiKFSssr/HJJ5/o4Ycf1htvvNH9ZqVnzpxRcHCwYmNj9eGHH7qcX11drbi4uO7Hr6qq6nE8JSXF4z3ExMT0eDqwrq5J7e19%2BxvLzv13dHT2%2BfzdIR9rZGSNjNwjH2v%2BlJHX3QI5cuSIRo0apfDwcNXV1embb75x%2BfDElVdeqdbWVq1Zs0ZnzpzR3/72Nz3xxBPKzMzUpEmTdOzYMRUVFamtrU179uzRtm3bul93lZmZqW3btmnPnj1qa2tTUVGRjh8/rrS0tN7cNgAA8CNecQdrxYoVevDBBxUWFqaNGzde8PXCw8O1YcMGPf744xo/frz69%2B%2Bv9PR05ebmql%2B/fnrppZdUUFCgtWvXasCAAVqyZInGjRsn6bvfKly6dKmWLVumo0ePKjY2VuvXr1dUVNQFzwUAAPqGgK6uri67h7j66qv1wQcfuPy24L333qsVK1YY/807u9TVNVmfdI4cjkClrX7f%2BHV7y1tzx1/0x3Q4AhUdHa6Ghma/ue1sEvlYIyNrZOQe%2BVjrzYwGDepv9Hqe8oqnCL%2Bv433yySf69ttvbZgGAADgwnhFwQIAAPAnFCwAAADDvKZgBQQE2D0CAACAEV7xW4TSd%2B/g/o/vedXW1qZVq1Z1vwHoWZ68DxYAAICdvKJgjR49use/3ZeYmKiGhgY1NDTYNBUAAMD58YqCZeK9rwAAALyF17wGCwAAwF9QsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDMbwtWZWWl7r77bo0ZM0bjx4/XwoULVV9fL0nav3%2B/srKylJiYqNTUVG3ZssXla0tKSpSWlqaEhARlZGRo3759dmwBAAD4KL8sWK2trfr5z3%2BuxMREffDBB3rzzTd14sQJPfzww2psbNSsWbM0ffp0VVRUqKCgQCtWrNCBAwckSWVlZVq%2BfLlWrlypiooKTZ06Vffff79Onz5t864AAICv8MuCVVNTo6uuukq5ubnq16%2BfoqOjlZ2drYqKCu3YsUNRUVG644475HA4lJycrPT0dG3evFmStGXLFk2ePFmjRo1ScHCw7rrrLkVHR6u0tNTmXQEAAF/hsHuA3vCjH/1IGzZscFl7%2B%2B23NXz4cFVVVSk%2BPt7lWGxsrLZu3SpJqq6u1owZM3ocr6ys9Pjxa2trVVdX57LmcIQpJibmXLZhKSjIt/qxw3Hx5z2bka9ldbGQjzUyskZG7pGPNX/MyC8L1j/q6urSM888o507d2rTpk36/e9/L6fT6XJOaGioWlpaJEnNzc1uj3uiuLhYhYWFLmu5ubnKy8s7z134h%2BjocNseOzLSaX1SH0Y%2B1sjIGhm5Rz7W/Ckjvy5Yp06d0kMPPaSDBw9q06ZNuvLKK%2BV0OtXU1ORyXmtrq8LDv/vh73Q61dra2uN4dHS0x4%2BbnZ2t1NRUlzWHI0wNDc3nuZPv52tN3/T%2BPREUFKjISKdOnjytjo7Oi/743o58rJGRNTJyj3ys9WZGdv3l3m8L1pEjR3Tffffphz/8obZu3aoBAwZIkuLj4/Xhhx%2B6nFtdXa24uDhJUlxcnKqqqnocT0lJ8fixY2JiejwdWFfXpPb2vv2NZef%2BOzo6%2B3z%2B7pCPNTKyRkbukY81f8rIt26BeKixsVF33nmnrr32Wr344ovd5UqS0tLSdOzYMRUVFamtrU179uzRtm3bul93lZmZqW3btmnPnj1qa2tTUVGRjh8/rrS0NLu2AwAAfIxf3sF67bXXVFNTo7feekvbt293ObZv3z699NJLKigo0Nq1a7DloK8AAA0pSURBVDVgwAAtWbJE48aNkyQlJydr6dKlWrZsmY4eParY2FitX79eUVFRdmwFAAD4oICurq4uu4foC%2BrqmqxPOkcOR6DSVr9v/Lq95a254y/6YzocgYqODldDQ7Pf3HY2iXyskZE1MnKPfKz1ZkaDBvU3ej1P%2BeVThAAAAHaiYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDHHYPgL7j5mc%2BtHuEc/LW3PF2jwAA8FHcwQIAADCMggUAAGAYBQsAAMAwvy9Y9fX1SktLU1lZWffa/v37lZWVpcTERKWmpmrLli0uX1NSUqK0tDQlJCQoIyND%2B/btu9hjAwAAH%2BbXBevjjz9Wdna2jhw50r3W2NioWbNmafr06aqoqFBBQYFWrFihAwcOSJLKysq0fPlyrVy5UhUVFZo6daruv/9%2BnT592q5tAAAAH%2BO3BaukpEQLFizQvHnzXNZ37NihqKgo3XHHHXI4HEpOTlZ6ero2b94sSdqyZYsmT56sUaNGKTg4WHfddZeio6NVWlpqxzYAAIAP8tu3abj%2B%2BuuVnp4uh8PhUrKqqqoUHx/vcm5sbKy2bt0qSaqurtaMGTN6HK%2BsrPT4sWtra1VXV%2Bey5nCEKSYm5ly34VZQkN/2Y6/gcPh/vmf/DPFn6V8jI2tk5B75WPPHjPy2YA0aNOh715ubm%2BV0Ol3WQkND1dLS4tFxTxQXF6uwsNBlLTc3V3l5eR5fA/aLjg63e4SLJjLSaX1SH0dG1sjIPfKx5k8Z%2BW3B%2BlecTqeamppc1lpbWxUeHt59vLW1tcfx6Ohojx8jOztbqampLmsOR5gaGprPc%2Brv509N3xuZ/u/ljYKCAhUZ6dTJk6fV0dFp9zheiYyskZF75GOtNzOy6y/Lfa5gxcfH68MPXd9RvLq6WnFxcZKkuLg4VVVV9TiekpLi8WPExMT0eDqwrq5J7e18Y/mSvvTfq6Ojs0/t93yQkTUyco98rPlTRn3uFkhaWpqOHTumoqIitbW1ac%2BePdq2bVv3664yMzO1bds27dmzR21tbSoqKtLx48eVlpZm8%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%2BnE6nLr/8cs2ePVubN2%2B2ezQAAOAjHHYP4G2qqqoUFRWlSy%2B9tHtt6NChqqmp0cmTJxUZGWnjdADskLR4u90j%2BK235o63e4RzcvMzH9o9wjnxtXz9CQXrnzQ3N8vpdLqsnf28paXFo4JVW1ururo6lzWHI0wxMTHmBpUUFMQNyN7kcJBvb/GlbPk%2B612%2B9GfBF/lKvme/z/zp%2B42C9U/CwsJ0%2BvRpl7Wzn4eHh3t0jeLiYhUWFrqsPfDAA5ozZ46ZIf9PbW2t7hxcpezsbOPlzV/U1taquLi4T2S0t%2BCn5/w1fSmf88X3mbW%2B9OeI77PeUVtbq9/9boNfZeQ/VdGQuLg4nThxQseOHete%2B%2BKLLzR48GD179/fo2tkZ2frtddec/nIzs42PmtdXZ0KCwt73C3D/0dG7pGPNTKyRkbukY81f8yIO1j/ZMiQIRo1apQef/xxPfbYY2poaNDzzz%2BvzMxMj68RExPjNw0cAACcO%2B5gfY%2B1a9eqvb1dP/nJTzRz5kzdcMMNmj17tt1jAQAAH8EdrO8xcOBArV271u4xAACAjwpatmzZMruHwPkLDw/XmDFjPH4Bfl9ERu6RjzUyskZG7pGPNX/LKKCrq6vL7iEAAAD8Ca/BAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwfJRx48f1%2BzZs5WUlKSxY8eqoKBA7e3tdo/lderr65WWlqaysjK7R/E6lZWVuvvuuzVmzBiNHz9eCxcuVH19vd1jeZXdu3crKytL1157rcaPH6/ly5ertbXV7rG8TkdHh3JycvTLX/7S7lG8TmlpqYYNG6bExMTuj/z8fLvH8ionTpzQwoULNXbsWI0ePVqzZ89WbW2t3WNdMAqWj5o7d67CwsL0/vvva%2BvWrdq9e7eKiorsHsurfPzxx8rOztaRI0fsHsXrtLa26uc//7kSExP1wQcf6M0339SJEyf08MMP2z2a16ivr9cvfvEL3Xbbbdq7d69KSkpUXl6u3/72t3aP5nUKCwu1d%2B9eu8fwSp9%2B%2BqmmTZumffv2dX%2BsWrXK7rG8ypw5c9TS0qJ33nlHO3fuVFBQkB555BG7x7pgFCwf9PXXX6u8vFz5%2BflyOp26/PLLNXv2bG3evNnu0bxGSUmJFixYoHnz5tk9ileqqanRVVddpdzcXPXr10/R0dHKzs5WRUWF3aN5jQEDBuijjz5SRkaGAgICdOLECX377bcaMGCA3aN5ld27d2vHjh266aab7B7FK3366acaMWKE3WN4rc8%2B%2B0z79%2B/XypUrFRkZqYiICC1fvlwLFiywe7QLRsHyQVVVVYqKitKll17avTZ06FDV1NTo5MmTNk7mPa6//nq98847uuWWW%2BwexSv96Ec/0oYNGxQUFNS99vbbb2v48OE2TuV9IiIiJEk33nij0tPTNWjQIGVkZNg8lfc4fvy4Fi9erDVr1sjpdNo9jtfp7OzUwYMH9T//8z%2BaMGGCUlJS9Mgjj6ixsdHu0bzGgQMHFBsbq1dffVVpaWm6/vrr9cQTT2jQoEF2j3bBKFg%2BqLm5ucf/mZ39vKWlxY6RvM6gQYPkcDjsHsMndHV16emnn9bOnTu1ePFiu8fxSjt27NB7772nwMBA5eXl2T2OV%2Bjs7FR%2Bfr7uvvtuXXXVVXaP45Xq6%2Bs1bNgwTZo0SaWlpXrllVd0%2BPBhXoP1DxobG3Xo0CEdPnxYJSUl%2BsMf/qCjR49q0aJFdo92wfgJ5IPCwsJ0%2BvRpl7Wzn4eHh9sxEnzUqVOn9NBDD%2BngwYPatGmTrrzySrtH8kqhoaEKDQ1Vfn6%2BsrKy1NjYqB/84Ad2j2WrdevWqV%2B/fsrJybF7FK81cOBAl5duOJ1O5efna%2BbMmTp16lT3HdK%2BrF%2B/fpKkxYsXKyQkRBEREZo7d65mzpyp5uZmn/6Zxh0sHxQXF6cTJ07o2LFj3WtffPGFBg8erP79%2B9s4GXzJkSNHNGPGDJ06dUpbt26lXP2TTz75RD/96U915syZ7rUzZ84oODiYp8Mkvf766yovL1dSUpKSkpL05ptv6s0331RSUpLdo3mNyspKrV69Wl1dXd1rZ86cUWBgYHex6OtiY2PV2dmptra27rXOzk5JcsnNF1GwfNCQIUM0atQoPf744zp16pT%2B%2Bte/6vnnn1dmZqbdo8FHNDY26s4779S1116rF198kRduf48rr7xSra2tWrNmjc6cOaO//e1veuKJJ5SZmckPR0nbt2/XJ598or1792rv3r2aMmWKpkyZwm8T/oOoqCht3rxZGzZsUHt7u2pqarRq1Srdeuut/Bn6P9ddd50uv/xyPfzww2publZ9fb2efvppTZw40efv8FGwfNTatWvV3t6un/zkJ5o5c6ZuuOEGzZ492%2B6x4CNee%2B011dTU6K233tKoUaNc3qMH3wkPD9eGDRtUVVWl8ePHKycnR9dddx1vZQGPDR48WOvWrdO7776rMWPGaMaMGRo5cqQeffRRu0fzGsHBwdq4caOCgoI0adIkTZo0SYMHD9bjjz9u92gXLKDL1%2B/BAQAAeBnuYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhv0/NWm3ony/IkoAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-8289598693405372876\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">678</td>\n",
       "        <td class=\"number\">76.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">118</td>\n",
       "        <td class=\"number\">13.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">80</td>\n",
       "        <td class=\"number\">9.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-8289598693405372876\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">678</td>\n",
       "        <td class=\"number\">76.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">118</td>\n",
       "        <td class=\"number\">13.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">80</td>\n",
       "        <td class=\"number\">9.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">80</td>\n",
       "        <td class=\"number\">9.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_PassengerId\">PassengerId<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>891</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>446</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>891</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram7511063217000768741\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABA0lEQVR4nO3WwQnCQBRFUSOWZBH25Do9WYQ9fRuQCwZCgpyzH3gMXGaWmZkL8NX16AFwZrejB/C/7s/Xz2fe62OHJdudLpAtlwp78cWCIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoGwzMwcPQLOygsCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQC4QNpqQz4e59xtwAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives7511063217000768741,#minihistogram7511063217000768741\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives7511063217000768741\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles7511063217000768741\"\n",
       "                                                  aria-controls=\"quantiles7511063217000768741\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram7511063217000768741\" aria-controls=\"histogram7511063217000768741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common7511063217000768741\" aria-controls=\"common7511063217000768741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme7511063217000768741\" aria-controls=\"extreme7511063217000768741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles7511063217000768741\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>45.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>223.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>446</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>668.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>846.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>891</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>890</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>445</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>257.35</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.57703</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>446</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>222.75</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>397386</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>66231</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>7.0 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram7511063217000768741\">\n",
       "            <img 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4LrKSkpAuuL126VJ9//rlGjRp1hScCAADw1Gg%2BFX733Xdry5Ytdo8BAADQeAIrKytLzZo1s3sMAAAA571F%2BPO3AC3LUmlpqfbv38/bgwAAoEFwXGBdf/31532L0NfXVw899JDi4uJsmgoAAOD/OS6w0tLS7B4BAADgVzkusLZv317nbSMjIy/jJAAAABfmuMAaPXq0LMuqvZxz7m3Dc2teXl7au3evLTMCAICmzXGB9dprr2nmzJmaMmWKbr/9dvn6%2Bio7O1vTpk3T8OHDdffdd9s9IgAAaOIcd5qGWbNm6bnnnlNMTIwCAwPVrFkzde/eXdOnT9dbb72ltm3b1l4AAADs4LjAys/P13XXXXfeemBgoIqKimyYCAAAwJPjAqtbt256%2BeWXVVpaWrtWXFysOXPmqEePHjZOBgAAcJbjPoM1depUPfTQQ%2BrTp4/at28vSTp8%2BLDatGmjd955x97hAAAA5MDA6tChg9atW6c1a9bo4MGDkqThw4drwIABcrvdNk8HAADgwMCSpJYtW2rYsGH67rvv1K5dO0lnz%2BYOAADQEDjuM1iWZWnu3LmKjIzUwIED9cMPP2jKlCl66qmnVFlZafd4AAAAzgusJUuWaPXq1Xruuefk5%2BcnSYqJidF//vMfvfrqqzZPBwAA4MDAysjI0LPPPqshQ4bUnr29f//%2BSk1N1QcffGDzdAAAAA4MrO%2B%2B%2B0433XTTeesdO3bU8ePH67Wv6upqjRw5Uk8%2B%2BWTt2pYtWxQXF6du3brp3nvv1ebNmy95ZgAA0LQ4LrDatm2rXbt2nbe%2BZcuW2g%2B811V6erp27NhR%2B/ORI0eUlJSkxx9/XDt27FBSUpKSk5N17NixS54bAAA0HY4LrEceeUTPP/%2B8Fi1aJMuylJmZqTlz5mj27NkaOXJknfeTmZmpjRs3qm/fvrVrq1atUkREhGJiYuRyudS/f39FRkYqIyPjctwVAADQSDnuNA1Dhw5VVVWVFi5cqPLycj377LO66qqrNGHCBD3wwAN12seJEyeUkpKiBQsWaPHixbXrOTk5CgsL89g2JCRE%2B/btM3kXAABAI%2Be4wPrXv/6lP/zhD4qPj1dhYaEsy9JVV11V59%2BvqanRpEmTNGbMGHXq1MnjulOnTp13slJ/f3%2BVlZXVa8b8/HwVFBR4rLlczRUcHFyv/fwaHx/HvfgIAMAvcrka1/Oa4wJrxowZ6ty5s373u9%2BpdevW9f79119/XX5%2Bfhd8O9Htdqu8vNxjrby8XAEBAfW6jYyMDKWnp3usJSYmavz48fWeFwCApiAoqH7PtQ2d4wKrffv22r9/vzp06PCbfn/16tXKz89XRESEJNUG1UcffaQRI0Zoz549Htvn5OSoS5cu9bqN%2BPh4RUdHe6y5XM1VVHTqN818IT4%2B3mrZkj8NBABoHEw%2BR/6UXeHmuMAKDQ3VxIkT9eabb6p9%2B/Zq1qyZx/UzZ8781d9fv369x8/nTtGQlpamgwcPatGiRVq3bp369u2rjRs3KisrSykpKfWaMTg4%2BLy3AwsKTqqqqqZe%2BwEAoKlobM%2BRjgus3Nxc3XbbbZJ03uecLlWHDh3017/%2BVXPnzlVKSoratm2r1157TTfccIPR2wEAAI2bl2VZlt1DXMzMmTP1%2BOOPq3nz5naP8psVFJw0uj%2BXy1tBQQGKSFl/8Y0BAGjgPkzueVn226ZNi8uy34txxEf233nnHZ0%2Bfdpj7ZFHHlF%2Bfr5NEwEAAPwyRwTWhV5k%2B%2BKLL3TmzBkbpgEAAPh1jggsAAAAJyGwAAAADHNMYHl5edk9AgAAQJ045jQNM2bM8DjnVWVlpebMmXPeWdYvdh4sAACAy80RgRUZGXneOa/Cw8NVVFSkoqIim6YCAAC4MEcE1pIlS%2BweAQAAoM4c8xksAAAApyCwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADGuSgbVv3z6NGTNG3bt3V8%2BePTV58mQVFhZKkrKzszVs2DCFh4crOjpaK1assHlaAADgNE0usMrLy/WXv/xF4eHh%2BvTTT7V27VoVFxfr6aefVklJicaOHavBgwdr%2B/btSk1N1cyZM7Vr1y67xwYAAA7S5AIrLy9PnTp1UmJiovz8/BQUFKT4%2BHht375dGzduVKtWrTRixAi5XC716NFDcXFxWrZsmd1jAwAAB3HZPcCVduONN%2BrNN9/0WNuwYYM6d%2B6sAwcOKCwszOO6kJAQrVy5sl63kZ%2Bfr4KCAo81l6u5goODf9vQF%2BDj0%2BTaGADQiLlcjet5rckF1k9ZlqV58%2BZp8%2BbNWrp0qd555x253W6Pbfz9/VVWVlav/WZkZCg9Pd1jLTExUePHj7/kmQEAaIyCggLsHsGoJhtYpaWleuqpp7Rnzx4tXbpUHTt2lNvt1smTJz22Ky8vV0BA/Q56fHy8oqOjPdZcruYqKjp1yXOf4%2BPjrZYt3RffEAAABzD5HPlTdoVbkwys3NxcPfroo7r%2B%2Buu1cuVKtW7dWpIUFham//73vx7b5uTkKDQ0tF77Dw4OPu/twIKCk6qqqrm0wQEAaKQa23Nk43rDsw5KSkr00EMP6dZbb9Xf//732riSpNjYWB0/flyLFy9WZWWltm3bpjVr1mjo0KE2TgwAAJymyb2C9d577ykvL08ffvih1q9f73Hdzp079dZbbyk1NVXz589X69atNXXqVN1%2B%2B%2B02TQsAAJzIy7Isy%2B4hmoKCgpMX36geXC5vBQUFKCJl/cU3BgCggfswuedl2W%2BbNi0uy34vpsm9RQgAAHC5EVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgXcOLECSUkJCgiIkJRUVFKTU1VVVWV3WMBAACHILAuIDk5Wc2bN9cnn3yilStXKjMzU4sXL7Z7LAAA4BAE1s8cPXpUWVlZmjRpktxut9q1a6eEhAQtW7bM7tEAAIBDEFg/c%2BDAAbVq1UrXXHNN7VqHDh2Ul5enH3/80cbJAACAU7jsHqChOXXqlNxut8fauZ/LysrUsmXLi%2B4jPz9fBQUFHmsuV3MFBwcbm9PHhzYGADQeLlfjel4jsH6mefPmOn36tMfauZ8DAgLqtI%2BMjAylp6d7rI0bN05JSUlmhtTZiHv77Te17vF4o%2BGG%2BsnPz1dGRobi4zkOduI4NBwci4aB42C/xpWLBoSGhqq4uFjHjx%2BvXTt48KCuvfZatWjRok77iI%2BP13vvvedxiY%2BPNzpnQUGB0tPTz3ulDFcWx6Fh4Dg0HByLhoHjYD9ewfqZ9u3b67bbbtOLL76o6dOnq6ioSAsWLNB9991X530EBwfzPwYAAJowXsG6gPnz56uqqkr33HOP7r//fvXu3VsJCQl2jwUAAByCV7Au4Oqrr9b8%2BfPtHgMAADiUz7Rp06bZPQR%2Bm4CAAHXv3r3OH77H5cFxaBg4Dg0Hx6Jh4DjYy8uyLMvuIQAAABoTPoMFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIHlQCdOnFBCQoIiIiIUFRWl1NRUVVVV2T1Wo7Nv3z6NGTNG3bt3V8%2BePTV58mQVFhZKkrKzszVs2DCFh4crOjpaK1as8PjdVatWKTY2Vt26ddOQIUO0c%2BdOO%2B5Co1JdXa2RI0fqySefrF3bsmWL4uLi1K1bN917773avHmzx%2B%2B88cYb6tOnj7p166aRI0fq0KFDV3rsRqO4uFiTJ09WVFSUIiMjlZCQoPz8fEk8Hq60PXv2aMSIEYqIiFCvXr00Y8YMVVRUSOIx0aBYcJwHH3zQeuKJJ6yysjIrNzfXGjBggPXGG2/YPVajcvr0aatnz57Wq6%2B%2Bap05c8YqLCy0Hn30Ueuxxx6ziouLre7du1tLly61Kisrra1bt1rh4eFWdna2ZVmWtW3bNis8PNzasWOHVVFRYS1atMiKioqyysrKbL5XzjZv3jyrU6dO1pQpUyzLsqzDhw9bXbt2tTZt2mRVVlZaH3zwgXXLLbdYP/zwg2VZlvXee%2B9ZvXv3tr755hurvLzcmjlzpjVgwACrpqbGzrvhWA8%2B%2BKCVmJholZSUWCdPnrTGjRtnjR07lsfDFVZdXW317NnTevvtt63q6mrr%2B%2B%2B/t/r162elp6fzmGhgeAXLYY4ePaqsrCxNmjRJbrdb7dq1U0JCgpYtW2b3aI1KXl6eOnXqpMTERPn5%2BSkoKEjx8fHavn27Nm7cqFatWmnEiBFyuVzq0aOH4uLiao/BihUrNGDAAN12223y9fXV6NGjFRQUpHXr1tl8r5wrMzNTGzduVN%2B%2BfWvXVq1apYiICMXExMjlcql///6KjIxURkaGJGn58uUaPny4QkND1axZMz3xxBPKy8vTZ599ZtfdcKzdu3crOztbaWlpatmypQIDA/XCCy9o4sSJPB6usJKSEhUUFKimpkbW//7Snbe3t9xuN4%2BJBobAcpgDBw6oVatWuuaaa2rXOnTooLy8PP344482Tta43HjjjXrzzTfl4%2BNTu7ZhwwZ17txZBw4cUFhYmMf2ISEh2rdvnyQpJyfnV69H/Zw4cUIpKSl66aWX5Ha7a9cv9u/88%2Bt9fX3Vvn17jsNvsGvXLoWEhGj58uWKjY1Vr169NGvWLLVp04bHwxUWFBSk0aNHa9asWeratavuvPNOtW/fXqNHj%2BYx0cAQWA5z6tQpjycZSbU/l5WV2TFSo2dZll555RVt3rxZKSkpFzwG/v7%2Btf/%2BF7sedVdTU6NJkyZpzJgx6tSpk8d1HIcrp6SkRPv379eRI0e0atUqvf/%2B%2Bzp27JimTJnCcbjCampq5O/vr2eeeUZffvml1q5dq4MHD2r%2B/PkciwaGwHKY5s2b6/Tp0x5r534OCAiwY6RGrbS0VOPHj9eaNWu0dOlSdezYUW63W%2BXl5R7blZeX1/77X%2Bx61N3rr78uPz8/jRw58rzrOA5Xjp%2BfnyQpJSVFgYGBuvrqq5WcnKwtW7bIsiyOwxW0adMmbdiwQcOHD5efn59CQ0OVmJiof/7znzwmGhgCy2FCQ0NVXFys48eP164dPHhQ1157rVq0aGHjZI1Pbm6uhg4dqtLSUq1cuVIdO3aUJIWFhenAgQMe2%2Bbk5Cg0NFTS2WP0a9ej7lavXq2srCxFREQoIiJCa9eu1dq1axUREVHv41BZWakjR46c9xYKLi4kJEQ1NTWqrKysXaupqZEk3XTTTTwerqDvv/%2B%2B9huD57hcLvn6%2BvKYaGhs/pA9foMHHnjAmjBhgnXy5MnabxHOnz/f7rEaleLiYuuuu%2B6ynnzySau6utrjusLCQisiIsJatGiRVVFRYWVmZlrh4eFWZmamZVlW7beoMjMza781FRkZaRUVFdlxVxqVKVOm1H6LMCcnx%2Bratav1wQcf1H5jqmvXrtahQ4csy7Ks5cuXW71797b27t1b%2B42p2NhYq6Kiws674EgVFRVWbGyslZSUZJWWllonTpywRo0aZSUmJvJ4uMIOHDhgdenSxVq4cKFVVVVl5ebmWgMHDrTS0tJ4TDQwXpb1v68hwDGOHz%2Bu6dOn68op2zMAAAE2SURBVLPPPpO3t7cGDx6siRMnenwgG5dm0aJFSktLk9vtlpeXl8d1O3fu1FdffaXU1FR98803at26tRISEjRkyJDabVavXq2FCxfq2LFjCgkJ0dSpU/X73//%2BSt%2BNRufcObDS0tIkSZ988onmzp2r3NxctW3bVpMmTdKdd94p6exn5xYtWqRly5apsLBQXbt21fPPP68bbrjBtvmd7NixY0pLS9P27dt15swZRUdHKyUlRS1btuTxcIVt3bpV8%2BbN06FDh9SiRQsNGjSo9hvPPCYaDgILAADAMD6DBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYNj/ATL3HHApXFreAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common7511063217000768741\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">891</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">293</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">304</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">303</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">302</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">301</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">300</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">299</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">298</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">297</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (881)</td>\n",
       "        <td class=\"number\">881</td>\n",
       "        <td class=\"number\">98.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme7511063217000768741\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">887</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">888</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">889</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">890</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">891</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Pclass\">Pclass<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>3</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.3%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>2.3086</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>3</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-4508119578538581684\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABC0lEQVR4nO3cwQkCQRAAQRVDMghz8m1OBmFOYwLSoCC3nFX/hfk0M689zswcgLdOWw8AKztvPQD7dbk9Pn7zvF9/MMn3bBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIy/2suIff%2BNgPGwSCQCAsd2L9OyfmWmwQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAjHmZmth4BV2SAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQXv/dEZPR0QjEAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4508119578538581684,#minihistogram-4508119578538581684\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-4508119578538581684\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-4508119578538581684\"\n",
       "                                                  aria-controls=\"quantiles-4508119578538581684\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-4508119578538581684\" aria-controls=\"histogram-4508119578538581684\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-4508119578538581684\" aria-controls=\"common-4508119578538581684\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-4508119578538581684\" aria-controls=\"extreme-4508119578538581684\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-4508119578538581684\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.83607</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.36215</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.28</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>2.3086</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.76197</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.63055</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>2057</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.69902</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>7.0 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-4508119578538581684\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAAgAElEQVR4nO3df1RVdb7/8ZcCyhEjMaXutFoLR8CuhhcSf5BJyZVMBXVQhknHyR/pjJGmS00dK50c0sbU/JEtr2U/lHUlHb3VpI7VmM4tB/HHmJl4QTPH6xJIUAFFFPb9o6/n2wmVg/PBc/bu%2BViL5Tqfvdm8X%2BFn%2BeqczaGJZVmWAAAAYExTXw8AAADgNBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGBYoK8H%2BLEoKSk3fs2mTZuodesQlZZWqrbWMn59X3FqLsm52ZyaSyKbHTk1l%2BTcbI2Zq23b24xez1s8g2VjTZs2UZMmTdS0aRNfj2KUU3NJzs3m1FwS2ezIqbkk52ZzYi4KFgAAgGEULAAAAMMoWAAAAIY5tmBt3rxZHTt2VFxcnPtj2rRpkqQdO3YoNTVVsbGx6tevn7Zv3%2B7xuatWrVJiYqJiY2M1YsQIHTt2zBcRAACATTm2YB08eFCDBg3S/v373R8LFizQ8ePHNWHCBD399NPas2ePJkyYoEmTJqmoqEiStGnTJq1Zs0ZvvPGGcnNz1alTJ02cOFGW5Zyf1gAAAI3L0QXrvvvuq7O%2BadMmxcfHq0%2BfPgoMDFT//v3VtWtX5eTkSJLeffddDRs2TFFRUWrevLmmTJmiU6dOKTc391ZHAAAANuXIglVbW6tDhw7p008/Ve/evZWYmKjnnntO586dU2FhoaKjoz3Oj4yMVH5%2BviTVOR4UFKSIiAj3cQAAgPo48o1GS0tL1bFjR/Xt21dLly5VWVmZpk%2BfrmnTpqm6uloul8vj/ODgYF24cEGSVFlZecPj3iguLlZJSYnHWmBgC4WHh99komsLCGjq8adTODWX5NxsTs0lkc2OnJpLcm42J%2BZyZMFq06aNsrOz3Y9dLpemTZumn//85%2Brevbuqqqo8zq%2BqqlJISIj73Bsd90ZOTo6WL1/usZaZmamJEyc2NIpXQkNd9Z9kQ07NJTk3m1NzSWSzI6fmkpybzUm5HFmw8vPz9ac//UlTpkxRkybfvStsdXW1mjZtqs6dO%2Bvw4cMe5xcWFrrv14qKilJBQYF69%2B4tSbp8%2BbKOHz9e52XFG8nIyFBSUpLHWmBgC5WVVf4zseoICGiq0FCXzp%2B/qJqaWqPX9iWn5pKcm82puSSy2ZFTc0nOzdaYucLCvH%2BCxCRHFqxWrVopOztbt99%2Bu0aNGqXi4mItWLBAP/vZzzR48GC9/fbb2rx5sx555BFt27ZNu3fv1qxZsyRJQ4YM0bJly5SYmKh27dpp8eLFatOmjeLj473%2B%2BuHh4XVeDiwpKdeVK42zGWpqahvt2r7k1FySc7M5NZdENjtyai7JudmclMuRBeuuu%2B7SypUrtWjRIr322mtq3ry5BgwYoGnTpql58%2BZ69dVX9fLLL2vWrFm6%2B%2B67tWzZMrVr106SNHToUJWXlyszM1OlpaWKiYnRypUrFRQU5ONUAADALppYvMHTLVFSUm78moGBTRUWFqKyskrHNH7Jubkk52Zzai6JbHbk1FySc7M1Zq62bW8zej1vOfIZLAAAIPV75TNfj%2BC1PVmP%2BnoEo5zz85AAAAB%2BgoIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMEcXrJqaGo0YMUIzZsxwr%2B3YsUOpqamKjY1Vv379tH37do/PWbVqlRITExUbG6sRI0bo2LFjt3psAABgc44uWMuXL9eePXvcj48fP64JEybo6aef1p49ezRhwgRNmjRJRUVFkqRNmzZpzZo1euONN5Sbm6tOnTpp4sSJsizLVxEAAIANObZg7dq1S9u2bdMjjzziXtu0aZPi4%2BPVp08fBQYGqn///uratatycnIkSe%2B%2B%2B66GDRumqKgoNW/eXFOmTNGpU6eUm5vrqxgAAMCGHFmwzpw5o1mzZmnhwoVyuVzu9cLCQkVHR3ucGxkZqfz8/GseDwoKUkREhPs4AACANwJ9PYBptbW1mjZtmkaNGqV7773X41hlZaVH4ZKk4OBgXbhwwavj3iouLlZJSYnHWmBgC4WHhzfoOvUJCGjq8adTODWX5NxsTs0lkc2OnJpLcnY2yVm5HFewVq5cqWbNmmnEiBF1jrlcLlVVVXmsVVVVKSQkxKvj3srJydHy5cs91jIzMzVx4sQGXcdboaGu%2Bk%2ByIafmkpybzam5JLLZkVNzSc7N5qRcjitY7733noqLixUfHy9J7sL08ccfa/jw4Tp06JDH%2BYWFhbrvvvskSVFRUSooKFDv3r0lSZcvX9bx48frvKxYn4yMDCUlJXmsBQa2UFlZ5U1lup6AgKYKDXXp/PmLqqmpNXptX3JqLsm52ZyaSyKbHTk1l%2BTsbJIaJVdYWMOeJDHFcQVr69atHo%2BvvkXD/PnzdfToUb355pvavHmzHnnkEW3btk27d%2B/WrFmzJElDhgzRsmXLlJiYqHbt2mnx4sVq06aNu6x5Kzw8vM7LgSUl5bpypXE2Q01NbaNd25ecmktybjan5pLIZkdOzSU5N5uTcjmuYN1I%2B/bt9eqrr%2Brll1/WrFmzdPfdd2vZsmVq166dJGno0KEqLy9XZmamSktLFRMTo5UrVyooKMjHkwMAADtxfMGaP3%2B%2Bx%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%2B/YpJSVF6enpWrduncrLy309FgAAgNf8rmA99thjWrdunbZu3aoHHnhAq1at0oMPPqgpU6bo888/9/o6u3btUnp6uu6//3717NlTc%2BfOVVVVlSTpwIEDSk9PV1xcnJKSkrR%2B/XqPz920aZOSk5MVGxurtLQ07d%2B/32hGAADgbH5XsK6KiIjQ5MmTtXXrVmVmZuqTTz7RmDFjlJSUpDfffPOG92qVlpbq17/%2BtR577DHt2bNHmzZt0u7du/Uf//EfOnfunMaNG6fBgwcrLy9PWVlZmjdvnr744gtJUm5urubOnav58%2BcrLy9PAwcO1Pjx43Xx4sVbFR0AANhcoK8HuJ4DBw7ov/7rv7R582ZVV1crOTlZaWlpKioq0pIlS3Tw4EEtWrTomp/bunVrff7552rZsqUsy9LZs2d16dIltW7dWtu2bVOrVq00fPhwSVJCQoJSU1OVnZ2tzp07a/369RowYIC6dOkiSRo5cqRycnK0efNmDRky5JblBwAA9uV3BWvFihV677339M033ygmJkaTJ09WSkqKWrZs6T4nICBAzz///A2vc/X8hx56SEVFRYqPj1daWppeeeUVRUdHe5wbGRmpDRs2SJIKCwvrFKnIyEjl5%2BebiAcAAH4E/K5grV27VgMHDtTQoUMVGRl5zXPat2%2BvqVOnenW9bdu26dy5c5o6daomTpyoO%2B%2B8Uy6Xy%2BOc4OBgXbhwQZJUWVl5w%2BPeKC4uVklJicdaYGALhYeHe30NbwQENPX40ymcmktybjan5pLIZkdOzSU5O5vkrFx%2BV7B27typiooKnT171r22efNmJSQkKCwsTJLUsWNHdezY0avrBQcHKzg4WNOmTVN6erpGjBhR56cSq6qqFBISIklyuVzum%2BG/f/zq1/ZGTk6Oli9f7rGWmZmpiRMnen2NhggNddV/kg05NZfk3GxOzSWRzY6cmktybjYn5fK7gvXVV19p7NixSktL0/Tp0yVJCxYs0OXLl7V69eo6L%2B9dy759%2B/Tb3/5W77//vpo1ayZJqq6uVlBQkCIjI/XZZ595nF9YWKioqChJUlRUlAoKCuocT0xM9DpDRkaGkpKSPNYCA1uorKzS62t4IyCgqUJDXTp//qJqamqNXtuXnJpLcm42p%2BaSyGZHTs0lOTubpEbJFRYWYvR63vK7gvWHP/xBjzzyiCZPnuxe%2B/jjj/Xcc89p/vz5Wr16db3X6NChg6qqqrRw4UJNmTJFJSUleumllzR06FD17dtXCxcu1FtvvaXhw4dr7969%2BuCDD7RixQpJ0tChQ5WZmal%2B/fqpS5cuys7O1pkzZ5ScnOx1hvDw8DovB5aUlOvKlcbZDDU1tY12bV9yai7Judmcmksimx05NZfk3GxOyuV3BevQoUOaN2%2Be%2B5kn6bub2seNG6e0tDSvrhESEqLXX39dL774onr27KnbbrtNqampyszMVLNmzbR69WplZWVp6dKlat26tZ599ln16NFD0nc/VTh79mzNmTNHRUVFioyM1KpVq9SqVatGyQsAAJzH7wpWy5YtdeLECd1zzz0e66dPn1ZwcLDX14mMjLzus10xMTFat27ddT930KBBGjRokNdfCwAA4Pv87nb9vn37as6cOfr8889VUVGhyspK/e1vf9MLL7zQoJfpAAAAfMXvnsGaMmWK/vGPf2j06NFq0qSJez05OVnPPPOMDycDAADwjt8VLJfLpZUrV%2Brrr7/WkSNHFBQUpPbt2ysiIsLXowEAAHjF7wrWVe3atVO7du18PQYAAECD%2BV3B%2Bvrrr/XCCy9o7969unz5cp3jhw8f9sFUAAAA3vO7gjVnzhydOnVKU6dO1W233ebrcQAAABrM7wrW/v379fbbbysuLs7XowAAANwUv3ubhrCwMPfvBQQAALAjvytYI0aM0KJFi%2Br8QmYAAAC78LuXCHfs2KG///3v6t69u%2B644w6PX5kjSZ988omPJgMAAPCO3xWs7t27q3v37r4eAwAA4Kb5XcF66qmnfD0CAADAP8Xv7sGSpPz8fM2cOVO/%2BMUvVFRUpOzsbOXm5vp6LAAAAK/4XcH68ssvlZ6erpMnT%2BrLL79UdXW1Dh8%2BrNGjR2v79u2%2BHg8AAKBeflewXn75ZY0ePVpr1qxRUFCQJOn3v/%2B9fvWrX2n58uU%2Bng4AAKB%2BflewvvzySw0ePLjO%2BmOPPaZjx475YCIAAICG8buCFRQUpIqKijrrp06dksvl8sFEAAAADeN3BatPnz5auHChysrK3GtHjx5VVlaWHn74Yd8NBgAA4CW/K1jTp09XVVWVHnjgAV28eFFpaWlKSUlRYGCgnnnmGV%2BPBwAAUC%2B/ex%2Bsli1bat26ddq1a5e%2B%2Buor1dbWKjo6Wr169VLTpn7XBwEAAOrwu4J1VUJCghISEnw9BgAAQIP5XcFKSkpSkyZNrnuc30UIAAD8nd8VrJ/97GceBevy5cv65ptvtHPnTk2aNMmHkwEAAHjH7wrWhAkTrrm%2Bdu1a7d27V7/61a9u8UQAAAANY5u7xnv37q0dO3b4egwAAIB62aZg7d69W82bN/f1GAAAAPXyu5cIf/gSoGVZqqio0JEjR3h5EAAA2ILfFayf/OQndX6KMCgoSI8//rhSU1N9NBUAAID3/K5gzZ8/39cjAAAA/FP8rmDl5eV5fW7Xrl0bcRIAAICb43cFa%2BTIkbIsy/1x1dWXDa%2BuNWnSRIcPH/bJjAAAADfidwVr2bJlmjdvnqZPn64ePXooKChIBw4c0Jw5czRs2DD17t3b1yMCAADckN%2B9TcNLL72k2bNnq0%2BfPmrZsqWaN2%2Bubt266YUXXtDq1at19913uz8AAAD8kd8VrOLiYv3Lv/xLnfWWLVuqrKzMBxMBAAA0jN8VrNjYWC1atEgVFRXutbNnz2rBggVKSEjw4WQAAADe8bt7sJ599lk9/vjjSkxMVEREhCTp66%2B/Vtu2bfXOO%2B/4djgAAAAv%2BF3Bat%2B%2BvTZv3qwPPvhAR48elSQNGzZMAwYMkMvl8vF0AAAA9fO7giVJoaGhSk9P18mTJ3XPPfdI%2Bu7d3AEAAOzA7%2B7BsixLL7/8srp27aqUlBSdPn1a06dP18yZM3X58mVfjwcAAFAvvytYa9as0XvvvafZs2erWbNmkqQ%2BffroL3/5i5YsWeLj6QAAAOrndwUrJydHzz//vNLS0tzv3t6/f39lZWXpww8/9PF0AAAA9fO7gnXy5En967/%2Ba531Dh066Ntvv/XBRAAAAA3jdwXr7rvv1hdffFFnfceOHe4b3gEAAPyZ3/0U4ZgxY/S73/1ORUVFsixLu3bt0rp167RmzRrNnDnT1%2BMBAADUy%2B8K1pAhQ3TlyhW99tprqqqq0vPPP6877rhDkydP1mOPPebr8QAAAOrldwXr/fff16OPPqqMjAyVlpbKsizdcccdvh4LAADAa353D9bvf/97983srVu3plwBAADb8btnsCIiInTkyBG1b9/e16PYQvysrb4ewWtbJvX09QgAANwSflewoqKiNHXqVL3%2B%2BuuKiIhQ8%2BbNPY7PmzfPR5MBAAB4x%2B8K1okTJ9SlSxdJUklJiY%2BnAQAAaDi/KFjz5s3T008/rRYtWmjNmjW%2BHgcAAOCf4hc3ub/zzju6ePGix9qYMWNUXFzso4kAAABunl8ULMuy6qzt27dPly5duulr5ufna9SoUerWrZt69uypZ555RqWlpZKkAwcOKD09XXFxcUpKStL69es9PnfTpk1KTk5WbGys0tLStH///pueAwAA/Pj4RcEyraqqSk888YTi4uL03//93/rTn/6ks2fP6re//a3OnTuncePGafDgwcrLy1NWVpbmzZvn/vU8ubm5mjt3rubPn6%2B8vDwNHDhQ48ePr/MMGwAAwPU4smCdOnVK9957rzIzM9WsWTOFhYUpIyNDeXl52rZtm1q1aqXhw4crMDBQCQkJSk1NVXZ2tiRp/fr1GjBggLp06aKgoCCNHDlSYWFh2rx5s49TAQAAu/CbgtWkSRNj1/rpT3%2Bq119/XQEBAe61P//5z%2BrUqZMKCgoUHR3tcX5kZKTy8/MlSYWFhTc8DgAAUB%2B/%2BClC6bt3cP/%2Be15dvnxZCxYsUEhIiMd5DX0fLMuy9Morr2j79u1au3at3nnnHblcLo9zgoODdeHCBUlSZWXlDY97o7i4uM5bTAQGtlB4eHiDZq9PQIDf9GOvBAZ6N%2B/VXHbL5w2nZnNqLolsduTUXJKzs0nOyuUXBatr1651CklcXJzKyspUVlZ209etqKjQzJkzdejQIa1du1YdOnSQy%2BVSeXm5x3lVVVXuIudyuVRVVVXneFhYmNdfNycnR8uXL/dYy8zM1MSJE28yiTOEhYXUf9L3hIa66j/Jppyazam5JLLZkVNzSc7N5qRcflGwGuO9r06cOKGxY8fqJz/5iTZs2KDWrVtLkqKjo/XZZ595nFtYWKioqChJ372TfEFBQZ3jiYmJXn/tjIwMJSUleawFBrZQWVnlzUS5Lrs1fW/zBwQ0VWioS%2BfPX1RNTW0jT3VrOTWbU3NJZLMjp%2BaSnJ1NUqPkauj/3JviFwXLtHPnzunxxx9Xjx49lJWVpaZN/38RSU5O1oIFC/TWW29p%2BPDh2rt3rz744AOtWLFCkjR06FBlZmaqX79%2B6tKli7Kzs3XmzBklJyd7/fXDw8PrvBxYUlKuK1ectxkaoqH5a2pqHfvfzKnZnJpLIpsdOTWX5NxsTsrlyIK1ceNGnTp1Slu2bNHWrZ6/DHn//v1avXq1srKytHTpUrVu3VrPPvusevToIUlKSEjQ7NmzNWfOHBUVFSkyMlKrVq1Sq1atfBEFAADYkCML1qhRozRq1KjrHo%2BJidG6deuue3zQoEEaNGhQY4wGAAB%2BBOx1Ew8AAIANULAAAAAMo2ABAAAYRsECAAAwjIIFAABgmCN/ihAATIqftbX%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%2Bbpiy%2B%2BkCTl5uZq7ty5mj9/vvLy8jRw4ECNHz9eFy9e9FUMAABgM44tWJs2bdLUqVM1efJkj/Vt27apVatWGj58uAIDA5WQkKDU1FRlZ2dLktavX68BAwaoS5cuCgoK0siRIxUWFqbNmzf7IgYAALAhxxasBx98UB999JH69%2B/vsV5QUKDo6GiPtcjISOXn50uSCgsLb3gcAACgPoG%2BHqCxtG3b9prrlZWVcrlcHmvBwcG6cOGCV8e9UVxcrJKSEo%2B1wMAWCg8P9/oa3ggIsFc/Dgz0bt6rueyWzxtOzebUXJI9M/3Y95pTc0nOziY5K5djC9b1uFwulZeXe6xVVVUpJCTEfbyqqqrO8bCwMK%2B/Rk5OjpYvX%2B6xlpmZqYkTJ97k1M4QFhbSoPNDQ131n2RTTs3m1Fx2w177jlNzSc7N5qRcP7qCFR0drc8%2B%2B8xjrbCwUFFRUZKkqKgoFRQU1DmemJjo9dfIyMhQUlKSx1pgYAuVlVXe5NTXZrem723%2BgICmCg116fz5i6qpqW3kqW4tp2Zzai7JfvtMYq85NZfk7GySGiVXQ/%2BHw5QfXcFKTk7WggUL9NZbb2n48OHau3evPvjgA61YsUKSNHToUGVmZqpfv37q0qWLsrOzdebMGSUnJ3v9NcLDw%2Bu8HFhSUq4rV5y3GRqioflramod%2B9/Mqdmcmstu2GvfcWouybnZnJTrR1ewwsLCtHr1amVlZWnp0qVq3bq1nn32WfXo0UOSlJCQoNmzZ2vOnDkqKipSZGSkVq1apVatWvl4cgAAYBc/ioJ15MgRj8cxMTFat27ddc8fNGiQBg0a1NhjAQAAh7LfzQUAAAB%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%2B4JtvvtHu3bs1bdo0uVwu3XPPPXryySeVnZ3t69EAAIBNULB%2BoKCgQK1atdKdd97pXmvfvr1OnTql8%2BfP%2B3AyAABgF4G%2BHsDfVFZWyuVyeaxdfXzhwgWFhobWe43i4mKVlJR4rAUGtlB4eLi5QSUFBNirHwcGejfv1Vx2y%2BcNp2Zzai7Jnpl%2B7HvNqbkkZ2eTnJWLgvUDLVq00MWLFz3Wrj4OCQnx6ho5OTlavny5x9pTTz2lCRMmmBny/ykuLtbjdxUoIyPDeHnzpeLiYr399uuOyyU5N5tTc0nO3WeSc79vTs0lNTzbnqxHb8FU/7zi4mItW7bMUd8z51RFQ6KionT27Fl9%2B%2B237rWjR4/qrrvu0m233ebVNTIyMrRx40aPj4yMDOOzlpSUaPny5XWeLbM7p4CNaD0AAAolSURBVOaSnJvNqbkkstmRU3NJzs3mxFw8g/UDERER6tKli1588UW98MILKisr04oVKzR06FCvrxEeHu6YBg4AABqOZ7CuYenSpbpy5Yr%2B/d//XT//%2Bc/Vq1cvPfnkk74eCwAA2ATPYF1DmzZttHTpUl%2BPAQAAbCpgzpw5c3w9BG5eSEiIunXr5vUN%2BHbh1FySc7M5NZdENjtyai7JudmclquJZVmWr4cAAABwEu7BAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwfJTpaWlSk5OVm5u7nXP2bFjh1JTUxUbG6t%2B/fpp%2B/btHsdXrVqlxMRExcbGasSIETp27Fhjj10vb3L953/%2Bp/r27au4uDj17dtX2dnZ7mO1tbWKi4tTbGys4uLi3B8XLly4FePfkDfZnnjiCcXExHjMvnPnTklSTU2NXnrpJT3wwAOKi4vT%2BPHjVVxcfKvGv6H6sj3xxBMemeLi4tShQwc9//zzkqRvv/1WHTp08DielJR0KyN4yM/P16hRo9StWzf17NlTzzzzjEpLS695rp32WUNy2W2fNSSb3faZt9nsts927dql9PR03X///erZs6fmzp2rqqqqa55rp33mNQt%2BZ8%2BePVafPn2s6Oho629/%2B9s1z/n666%2BtmJgY66OPPrIuX75sffjhh1bnzp2t06dPW5ZlWRs3brR69epl/c///I9VVVVlzZs3zxowYIBVW1t7K6N48CbXRx99ZMXHx1v79%2B%2B3amtrrX379lnx8fHW1q1bLcuyrCNHjlidOnWyLl26dCtHr5c32SzLsrp3727l5uZe89iyZcus1NRU69SpU1Z5ebk1adIka%2BzYsY01ste8zfZ969evtx566CGrqKjIsizL%2Bstf/mL17t27Mcf02sWLF62ePXtaS5YssS5dumSVlpZaY8eOtX7961/XOddO%2B6whuey2zxqSzbLstc8amu37/HmfnTlzxoqJibH%2B%2BMc/WjU1NVZRUZGVkpJiLVmypM65dtpnDUHB8jMbN260Hn74YevDDz%2B84T9oixYtskaNGuWxNmbMGPdf3l/84hfWa6%2B95j5WXV1txcXFWbt27Wq84W/A21xr1661Vq5c6bGWmZlpzZ0717Isy9qwYYOVlpbW6PM2hLfZTpw4Yd17771WeXn5NY8nJiZa77//vvtxSUmJ1aFDB%2BvEiRONMrc3vM32fUePHrU6d%2B5s5eXludeWLFliTZgwoTFH9drRo0etMWPGWFeuXHGvffzxx9b9999f51w77bOG5LLbPmtINrvts4Zk%2B%2BHn%2BfM%2BsyzL/T2ora21jhw5YiUnJ1tr1qypc56d9llD8BKhn3nwwQf10UcfqX///jc8r7CwUNHR0R5rkZGRys/Pv%2BbxoKAgRUREuI/fat7mGj58uMaNG%2Bd%2BfObMGeXl5em%2B%2B%2B6TJB08eFCXLl3SkCFD1KNHDw0fPlz79u1r1Nnr4222gwcPKiQkRJMnT1aPHj2UkpKiDRs2SJLKy8t1%2BvRpj%2B9ZmzZtdPvtt%2BvIkSONOv%2BNeJvt%2B373u99p8ODBio%2BPd68dPHhQp0%2BfVkpKinr06KGxY8eqsLCwMUau109/%2BlO9/vrrCggIcK/9%2Bc9/VqdOneqca6d91pBcdttnDclmt33WkGzf5%2B/7TJJatmwpSXrooYeUmpqqtm3bKi0trc55dtpnDUHB8jNt27ZVYGBgvedVVlbK5XJ5rAUHB7vvkajv%2BK3mba7vKykp0dixY3XfffcpJSVF0ncZOnfurBUrVujTTz9VUlKSxowZo3/84x%2BNMbZXvM1WXV2t2NhYTZ48WX/96181Y8YMZWVlacuWLaqsrJQktWjRwuNzgoOD3cd8oaHftz179ujAgQN66qmnPNZDQ0PVpUsXvfPOO/r4448VERGhUaNGqby83PTIDWJZlhYvXqzt27dr1qxZdY7bbZ9dVV%2Bu77PLPruqvmx23GdXeft9s9s%2B27Ztm3bu3KmmTZtq4sSJdY7bdZ/Vp2H/4sFvuFyuOjcLVlVVKSQkxKvj/u7vf/%2B7nn76acXHx2vevHnuf%2BRnzJjhcd6YMWO0ceNG7dixQ7/85S99MarXBg8erMGDB7sfP/jggxo8eLC2bNmiBx54QJJ08eJFj8%2Bx0/dMknJyctSvXz%2B1bdvWY33hwoUej2fOnKk//vGP2rNnj3r37n0rR3SrqKjQzJkzdejQIa1du1YdOnSoc44d95k3ua6y2z7zJptd91lDvm922mfSd2UoODhY06ZNU3p6us6dO6fbb7/dfdyO%2B8wbPINlU9HR0SooKPBYKywsVFRUlCQpKirK4/jly5d1/PjxOk/D%2BqMNGzZo5MiRevzxx7Vw4UI1a9bMfWzx4sX66quvPM6vrq5W8%2BbNb/WYDbZhwwZt2bLFY%2B3q7LfffrvuvPNOj6fzS0pKdPbsWVt8zyTpypUr%2BuSTTzRw4ECP9YqKCr300kv63//9X/daTU2Nrly5ouDg4Fs9piTpxIkTGjJkiCoqKrRhw4br/mNmt33mbS7JfvvM22x23GcN%2Bb7ZZZ/t27dPjz76qKqrq91r1dXVCgoKqvNslN32mdd8ewsYbuRGNxUXFhZaMTEx1ocffuj%2BqYuYmBjr2LFjlmVZ1rvvvmv16tXLOnz4sPunLpKTk63q6upbGeGabpRr69atVqdOnaydO3de8/hvfvMba9iwYVZxcbF16dIla9myZVaPHj2ssrKyxhzZazfK9uabb1oJCQnWoUOHrJqaGmv79u0eN6kuXrzYSklJsU6cOOH%2B6aZf/vKXt3L8G6rvJvcvv/zS6tixo1VVVVXn2MCBA60JEyZY58%2BftyoqKqznnnvO6tevn0/%2BPp49e9Z6%2BOGHrRkzZlg1NTU3PNdO%2B6whuey2zxqSzW77rCHZLMs%2B%2B6yiosJ66KGHrBdffNG6dOmSdfLkSWvo0KHW7Nmz65xrp33WEBQsP/bDf9BiY2Ot9957z/14586d1sCBA63Y2FhrwIAB1qeffuo%2BVltba73xxhtWUlKSFRsba40YMcL9l9XXbpQrJSXFuvfee63Y2FiPj%2Beee86yLMsqKyuzZsyYYSUkJLhzHT582Cc5ruVG2Wpra61XX33V6t27t9W5c2drwIAB1pYtW9znVldXWwsWLLB69epl3X///db48eOtb7/99pZnuJ76/j5u2bLFSkhIuObnnjx50srMzLS6detmxcXFWb/5zW%2BskydPNvrM17J69WorOjra%2Brd/%2B7c6f88sy777rCG57LbPGpLNbvusoX8f7bLPLMuyCgoKrFGjRlnx8fFW7969rUWLFrnf%2BsOu%2B6whmliWZfn6WTQAAAAn4R4sAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCw/wMIhRN3Y8WYggAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-4508119578538581684\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">491</td>\n",
       "        <td class=\"number\">55.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">216</td>\n",
       "        <td class=\"number\">24.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:44%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">184</td>\n",
       "        <td class=\"number\">20.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:38%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-4508119578538581684\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">216</td>\n",
       "        <td class=\"number\">24.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:44%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">184</td>\n",
       "        <td class=\"number\">20.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:38%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">491</td>\n",
       "        <td class=\"number\">55.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">216</td>\n",
       "        <td class=\"number\">24.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:44%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">184</td>\n",
       "        <td class=\"number\">20.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:38%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">491</td>\n",
       "        <td class=\"number\">55.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Sex\">Sex<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>0.2%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.0%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-2865421403268465329\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>male</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 64.8%\">\n",
       "            577\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>female</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:54%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 35.2%\">\n",
       "            314\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-2865421403268465329, #minifreqtable-2865421403268465329\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-2865421403268465329\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">male</td>\n",
       "        <td class=\"number\">577</td>\n",
       "        <td class=\"number\">64.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">female</td>\n",
       "        <td class=\"number\">314</td>\n",
       "        <td class=\"number\">35.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:54%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_SibSp\">SibSp<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.8%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.52301</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>8</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>68.2%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram6976591126133551184\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2B17YcXAAABH0lEQVR4nO3dwQkCMRRAwVUsySLsybM9WYQ9xbvIg13QDTpzD/xDHiEQyGGMMRbgrePeA8DMTnsP8Op8va9e87hdPjAJOEEgCQSCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEwnTfH2zhywQ%2BxQkCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQC4Sde824x6wvgWef6V38byBZrN%2B%2B3Nu6vzLUs88V%2BGGOMvYeAWbmDQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQHgCAXUdlWAU5dkAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives6976591126133551184,#minihistogram6976591126133551184\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives6976591126133551184\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles6976591126133551184\"\n",
       "                                                  aria-controls=\"quantiles6976591126133551184\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram6976591126133551184\" aria-controls=\"histogram6976591126133551184\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common6976591126133551184\" aria-controls=\"common6976591126133551184\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme6976591126133551184\" aria-controls=\"extreme6976591126133551184\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles6976591126133551184\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>1.1027</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>2.1085</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>17.88</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.52301</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.71378</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>3.6954</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>466</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>1.216</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>7.0 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram6976591126133551184\">\n",
       "            <img 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rl5%2Bdr9%2B7dGjlypCRpzJgx2rhxo4qKivTdd99p5cqVatOmTUNZAwAA8Id1Z7B%2BTMeOHfX73/9eK1as0Lx583T99dfr%2Beef14033ihJSklJ0ZkzZ5SWlqby8nJ17dpVa9asUXh4uMOTAwCAYGJ9wVq6dKnP5/3791f//v0vuW9ISIgmTZqkSZMm/RSjAQAAS1l3iRAAAMBpAVewamtrnR4BAADgqgRcwRowYIB%2B85vfqKSkxOlRAAAArkjAFazHHntM%2B/bt07BhwzR27Fht2rRJZ86ccXosAAAAvwVcwbrvvvu0adMmbdu2TX369NHatWvVr18/zZw5Ux999JHT4wEAAFxWwBWsizp06KAZM2Zo27ZtSktL07vvvquHHnpIAwcO1H/8x39wrxYAAAhYAfs2Dfv379ef//xn5efn6/z580pOTtbo0aN17Ngx/e53v9Nnn32mZ5991ukxAQAAGgm4grV69Wq9/vrr%2Bvrrr9W1a1fNmDFDw4YNU1RUVMM%2BYWFheuqppxycEgAA4IcFXMHauHGjRowYoZSUFMXFxV1yn44dO2rWrFk/8WQAAAD%2BCbiC9d577%2Bns2bM6efJkw1p%2Bfr569%2B4tj8cjSerSpYu6dOni1IgAAAA/KuBucv/b3/6mwYMHKzc3t2Ft%2BfLlGj58uP77v//bwckAAAD8E3AF6ze/%2BY3uvPNOzZgxo2HtL3/5iwYMGNDo/xUEAAAIRAFXsA4ePKjJkyerRYsWDWthYWGaPHmyPv30UwcnAwAA8E/AFayoqCgdPXq00frf//53RUREODARAABA0wRcwRo8eLAyMzP10Ucf6ezZs6qsrNTHH3%2Bsp59%2BWsnJyU6PBwAAcFkB9yrCmTNn6ptvvtGkSZMUEhLSsJ6cnKzZs2c7OBkAAIB/Aq5gud1urVmzRl999ZUOHTqk8PBwdezYUR06dHB6NAAAAL8EXMG66MYbb9SNN97o9BgAAABNFnAF66uvvtLTTz%2BtvXv36sKFC422f/HFFw5MBQAA4L%2BAK1iZmZkqLS3VrFmzdM011zg9DgAAQJMFXMH65JNP9Mc//lEJCQlOjwIAAHBFAu5tGjwejyIjI50eAwAA4IoFXMEaP368nn32WZ05c8bpUQAAAK5IwF0i3Llzpz799FP17NlT1157rc9/mSNJ7777rkOTAQAA%2BCfgClbPnj3Vs2dPp8cAAAC4YgFXsB577DGnRwAAALgqAXcPliQVFRXpySef1D//8z/r2LFjysnJUUFBgdNjAQAA%2BCXgCtbnn3%2BusWPH6ttvv9Xnn3%2Bu8%2BfP64svvtCkSZO0Y8cOp8cDAAC4rIArWCtWrNCkSZO0YcMGhYeHS5IWL16sBx54QNnZ2Q5PBwAAcHkBV7A%2B//xzjRo1qtH6fffdpy%2B//NKBiQAAAJom4ApWeHi4zp4922i9tLRUbrfbgYkAAACaJuAK1qBBg7Ry5UpVVFQ0rB0%2BfFhZWVn61a9%2B5dxgAAAAfgq4gjVnzhxVV1erT58%2BOnfunEaPHq1hw4bJ5XJp9uzZTo8HAABwWQH3PlhRUVHatGmTdu3apb/97W%2Bqq6tTp06d1L9/f4WGBlwfBAAAaCTgCtZFvXv3Vu/evZ0eAwAAoMkCrmANHDhQISEhP7id/4sQAAAEuoArWPfcc49Pwbpw4YK%2B/vprvffee5o%2BfbqDkwEAAPgn4ArWtGnTLrm%2BceNG7d27Vw888MBPPBEAAEDTBM1d43fccYd27tzp9BgAAACXFTQFa/fu3WrZsqXTYwAAAFxWwF0i/MdLgPX19Tp79qwOHTrUpMuDu3bt0rPPPqvDhw/L7XbrrrvuUkZGhiIiIrR//34tXrxYJSUl8ng8mjJlisaOHdvwtXl5eVq9erW8Xq9uuukmLViwQAkJCcYyAgAAuwVcwbruuusavYowPDxcEyZM0PDhw/06Rnl5uR599FFlZmZq1KhROn78uB566CH94Q9/0IQJEzR58mSlp6crNTVVe/bsUVpamjp37qzbbrtNBQUFWrRokdauXavbbrtNOTk5mjJlinbs2MF/1QMAAPwScAVr6dKlV32M1q1b66OPPlJUVJTq6%2Bt18uRJfffdd2rdurW2b9%2BumJgYjRs3TtL377c1fPhw5eTk6LbbbtOrr76qoUOHKjExUZI0ceJE5ebmKj8/X2PGjLnq2QAAgP0CrmDt2bPH7327d%2B/%2Bg9uioqIkSbfffruOHTumpKQkjR49Wr/97W/VqVMnn33j4uK0efNmSVJJSUmjIhUXF6eioiK/5wIAAD9vAVewJk6cqPr6%2BoaPiy5eNry4FhISoi%2B%2B%2BOKyx9u%2BfbtOnTqlWbNmKT09Xe3atWt0qS8iIkJVVVWSpMrKyh/d7o%2BysjJ5vV6fNZerlWJjY/0%2Bhj/CwoLmNQqSJJfLv3kv5gq2fP6wNZutuSSyBSNbc0n2ZrMxV8AVrOeff15LlizRnDlz1KtXL4WHh2v//v3KzMzU/fffrzvuuKNJx4uIiFBERIQyMjI0duxYjR8/XmfOnPHZp7q6WpGRkZIkt9ut6urqRts9Ho/fj5mbm6vs7GyftbS0NKWnpzdpdtt4PJFN2j862t573mzNZmsuiWzByNZckr3ZbMoVcAVr2bJlWrhwofr169ew1qNHDz399NOaPXu2/uVf/uWyx9i3b5/mzp2rN954Qy1atJAknT9/XuHh4YqLi9OHH37os39JSYni4%2BMlSfHx8SouLm60fcCAAX5nSE1N1cCBA33WXK5Wqqio9PsY/gi2pu9v/rCwUEVHu3X69DnV1tY181Q/LVuz2ZpLIlswsjWXZG%2B25szV1F/uTQm4glVWVqZ/%2Bqd/arQeFRWliooKv47RuXNnVVdXa%2BXKlZo5c6a8Xq%2BWLVumlJQUDR48WCtXrtT69es1btw47d27V1u2bNHq1aslSSkpKUpLS9OQIUOUmJionJwcnThxQsnJyX5niI2NbXQ50Os9o5oae34YrkRT89fW1ln7PbM1m625JLIFI1tzSfZmsylXwJ0C6datm5599lmdPXu2Ye3kyZNavny5evfu7dcxIiMjtW7dOhUXF6tv374aP368%2BvTpo7lz58rj8eill17Stm3b1LNnT82fP1/z589Xr169JH3/qsKFCxcqMzNTPXr00Jtvvqm1a9cqJiamWfICAAD7hNT/3zvJA8Dhw4c1YcIEVVVVqUOHDpKkr776Sm3bttXLL7%2Bs9u3bOzvgFfJ6z1x%2BpyZyuUKVvOJ948dtLm9N7%2BvXfi5XqDyeSFVUVFrzm8xFtmazNZdEtmBkay7J3mzNmatt22uMHs9fAXeJsGPHjsrPz9eWLVt0%2BPBhSdL999%2BvoUOH8kafAAAgKARcwZKk6OhojR07Vt9%2B%2B61uuOEGSd%2B/mzsAAEAwCLh7sOrr67VixQp1795dw4YN09///nfNmTNHTz75pC5cuOD0eAAAAJcVcAVrw4YNev3117Vw4cKGt1gYNGiQ/vrXv%2Bp3v/udw9MBAABcXsAVrNzcXD311FMaPXp0w7u333333crKytKbb77p8HQAAACXF3AF69tvv9UvfvGLRuudO3fW8ePHHZgIAACgaQKuYF1//fU6cOBAo/WdO3c23PAOAAAQyALuVYQPPfSQ/u3f/k3Hjh1TfX29du3apU2bNmnDhg168sknnR4PAADgsgKuYI0ZM0Y1NTV64YUXVF1draeeekrXXnutZsyYofvuu8/p8QAAAC4r4ArWG2%2B8obvuukupqakqLy9XfX29rr32WqfHAgAA8FvA3YO1ePHihpvZW7duTbkCAABBJ%2BAKVocOHXTo0CGnxwAAALhiAXeJMD4%2BXrNmzdK6devUoUMHtWzZ0mf7kiVLHJoMAADAPwFXsI4eParExERJktfrdXgaAACApguIgrVkyRI9/vjjatWqlTZs2OD0OAAAAFclIO7Bevnll3Xu3DmftYceekhlZWUOTQQAAHDlAqJg1dfXN1rbt2%2BfvvvuOwemAQAAuDoBUbAAAABsQsECAAAwLGAKVkhIiNMjAAAAGBEQryKUvn8H9//7nlcXLlzQ8uXLFRkZ6bMf74MFAAACXUAUrO7duzd6z6uEhARVVFSooqLCoakAAACuTEAULN77CgAA2CRg7sECAACwBQULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCYy%2BkBmktRUZGWLVumgwcPKjw8XH379tUTTzyh1q1ba//%2B/Vq8eLFKSkrk8Xg0ZcoUjR07tuFr8/LytHr1anm9Xt10001asGCBEhISHExjhyG//dDpEZrkrel9nR4BABCkrDyDVV1drYcfflgJCQn64IMPtHXrVp08eVJz587VqVOnNHnyZI0aNUp79uxRVlaWlixZogMHDkiSCgoKtGjRIi1dulR79uzRiBEjNGXKFJ07d87hVAAAIFhYWbBKS0t18803Ky0tTS1atJDH41Fqaqr27Nmj7du3KyYmRuPGjZPL5VLv3r01fPhw5eTkSJJeffVVDR06VImJiQoPD9fEiRPl8XiUn5/vcCoAABAsrCxYN910k9atW6ewsLCGtbffflu33HKLiouL1alTJ5/94%2BLiVFRUJEkqKSn50e0AAACXY%2B09WBfV19frt7/9rXbs2KGNGzfq5Zdfltvt9tknIiJCVVVVkqTKysof3e6PsrIyeb1enzWXq5ViY2OvMMWlhYVZ2Y8Dhstl/vt78Tmz7bmzNZdEtmBkay7J3mw25rK6YJ09e1ZPPvmkDh48qI0bN6pz585yu906c%2BaMz37V1dWKjIyUJLndblVXVzfa7vF4/H7c3NxcZWdn%2B6ylpaUpPT39CpPACR5PZLMdOzraffmdgpCtuSSyBSNbc0n2ZrMpl7UF6%2BjRo3rkkUd03XXXafPmzWrdurUkqVOnTvrwQ99Xs5WUlCg%2BPl6SFB8fr%2BLi4kbbBwwY4Pdjp6amauDAgT5rLlcrVVRUXkmUH2RT0w9Epp8v6fvnLDrardOnz6m2ts748Z1iay6JbMHI1lySvdmaM1dz/rL8Y6wsWKdOndKECRPUq1cvZWVlKTT0/y8iycnJWr58udavX69x48Zp79692rJli1avXi1JSklJUVpamoYMGaLExETl5OToxIkTSk5O9vvxY2NjG10O9HrPqKbGnh%2BGn4PmfL5qa%2Bus/PNgay6JbMHI1lySvdlsymVlwXrttddUWlqqt956S9u2bfPZ9sknn%2Bill15SVlaWVq1apdatW2v%2B/Pnq1auXJKl3795auHChMjMzdezYMcXFxWnt2rWKiYlxIgoAAAhCIfX19fVOD/Fz4PWeufxOTeRyhSp5xfvGj4vvNccbjbpcofJ4IlVRUWnNb2mSvbkksgUjW3NJ9mZrzlxt215j9Hj%2B4iYeAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBh1hes8vJyJScnq6CgoGFt//79Gjt2rBISEjRw4EC9%2BuqrPl%2BTl5en5ORkdevWTaNHj9Ynn3zyU48NAACCmNUFa%2B/evUpNTdXRo0cb1k6dOqXJkydr1KhR2rNnj7KysrRkyRIdOHBAklRQUKBFixZp6dKl2rNnj0aMGKEpU6bo3LlzTsUAAABBxtqClZeXp1mzZmnGjBk%2B69u3b1dMTIzGjRsnl8ul3r17a/jw4crJyZEkvfrqqxo6dKgSExMVHh6uiRMnyuPxKD8/34kYAAAgCFlbsPr166d33nlHd999t896cXGxOnXq5LMWFxenoqIiSVJJScmPbgcAALgcl9MDNJe2bdtecr2yslJut9tnLSIiQlVVVX5t90dZWZm8Xq/PmsvVSrGxsX4fwx9hYdb244Dgcpn//l58zmx77mzNJZEtGNmaS7I3m425rC1YP8TtduvMmTM%2Ba9XV1YqMjGzYXl1d3Wi7x%2BPx%2BzFyc3OVnZ3ts5aWlqb09PQrnBpO8Hgim%2B3Y0dHuy6wQ%2BRAAAAvpSURBVO8UhGzNJZEtGNmaS7I3m025fnYFq1OnTvrwww991kpKShQfHy9Jio%2BPV3FxcaPtAwYM8PsxUlNTNXDgQJ81l6uVKioqr3DqS7Op6Qci08%2BX9P1zFh3t1unT51RbW2f8%2BE6xNZdEtmBkay7J3mzNmas5f1n%2BMT%2B7gpWcnKzly5dr/fr1GjdunPbu3astW7Zo9erVkqSUlBSlpaVpyJAhSkxMVE5Ojk6cOKHk5GS/HyM2NrbR5UCv94xqauz5Yfg5aM7nq7a2zso/D7bmksgWjGzNJdmbzaZcP7uC5fF49NJLLykrK0urVq1S69atNX/%2BfPXq1UuS1Lt3by1cuFCZmZk6duyY4uLitHbtWsXExDg8OQAACBY/i4J16NAhn8%2B7du2qTZs2/eD%2BI0eO1MiRI5t7LAAAYClu4gEAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMczk9ABCohvz2Q6dHaJK3pvd1egQAwP/DGSwAAADDKFgAAACGUbAAAAAMo2ABAAAYxk3uAHAZSfO2OT1Ck/CCB8B5FCzAEsH0qkcKAADbcYkQAADAMAoWAACAYRSsSzhx4oSmTp2qpKQk9ezZU1lZWaqpqXF6LAAAECS4B%2BsSpk%2Bfrnbt2un999/X8ePHNWXKFK1fv14PP/yw06MBAOC3YLo3szDrLqdHMIqC9Q%2B%2B/vpr7d69W%2B%2B9957cbrduuOEGTZ06VcuXL6dgAQgKwfSPKi94gK24RPgPiouLFRMTo3bt2jWsdezYUaWlpTp9%2BrSDkwEAgGDBGax/UFlZKbfb7bN28fOqqipFR0df9hhlZWXyer0%2Bay5XK8XGxpobVFJYGP0YwcnlCp4/u/ycNa/m%2BLNw8Tmz8bmzOZtkVy4K1j9o1aqVzp0757N28fPIyEi/jpGbm6vs7Gyftccee0zTpk0zM%2BT/U1ZWpgnti5Wammq8vDmprKxMubm51uWS7M1may7J3p8zyd7nraysTH/84zrrcklNzxYs9zWVlZXp%2Beeft%2Bo5s6cqGhIfH6%2BTJ0/q%2BPHjDWuHDx9W%2B/btdc011/h1jNTUVL322ms%2BH6mpqcZn9Xq9ys7ObnS2LNjZmkuyN5utuSSyBSNbc0n2ZrMxF2ew/kGHDh2UmJioZ555Rk8//bQqKiq0evVqpaSk%2BH2M2NhYaxo4AABoOs5gXcKqVatUU1OjX//617r33nvVv39/TZ061emxAABAkOAM1iW0adNGq1atcnoMAAAQpMIyMzMznR4CVy4yMlI9evTw%2Bwb8YGFrLsnebLbmksgWjGzNJdmbzbZcIfX19fVODwEAAGAT7sECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBClInTpzQ1KlTlZSUpJ49eyorK0s1NTVOj2VMeXm5kpOTVVBQ4PQoRhQVFenBBx9Ujx491LdvX82ePVvl5eVOj2XErl27NHbsWP3yl79U3759tWjRIlVXVzs9ljG1tbUaP368nnjiCadHMSY/P19dunRRQkJCw0dGRobTYxlx8uRJzZ49Wz179lT37t01depUlZWVOT3WVXnjjTd8nquEhATdeuutuvXWW50ezYiDBw9q3LhxSkpKUr9%2B/bR48WKdP3/e6bGuGgUrSE2fPl2tWrXS%2B%2B%2B/r82bN2vXrl1av36902MZsXfvXqWmpuro0aNOj2JEdXW1Hn74YSUkJOiDDz7Q1q1bdfLkSc2dO9fp0a5aeXm5Hn30Ud13330qLCxUXl6edu/erT/84Q9Oj2ZMdna2CgsLnR7DqM8%2B%2B0wjR47UJ5980vCxfPlyp8cyYtq0aaqqqtI777yjHTt2KCwsTAsWLHB6rKsyYsQIn%2Bdq27ZtiomJUVZWltOjXbW6ujo9%2BuijGjx4sHbv3q3Nmzfrgw8%2B0Nq1a50e7apRsILQ119/rd27dysjI0Nut1s33HCDpk6dqpycHKdHu2p5eXmaNWuWZsyY4fQoxpSWlurmm29WWlqaWrRoIY/Ho9TUVO3Zs8fp0a5a69at9dFHH2n06NEKCQnRyZMn9d1336l169ZOj2bErl27tH37dt15551Oj2LUZ599Zs3Zj//r888/1/79%2B7V06VJFR0crKipKixYt0qxZs5wezZj6%2BnplZGToV7/6lUaOHOn0OFft1KlT8nq9qqur08X/uS80NFRut9vhya4eBSsIFRcXKyYmRu3atWtY69ixo0pLS3X69GkHJ7t6/fr10zvvvKO7777b6VGMuemmm7Ru3TqFhYU1rL399tu65ZZbHJzKnKioKEnS7bffruHDh6tt27YaPXq0w1NdvRMnTmjevHlauXKlFX/ZX1RXV6eDBw/qv/7rv3THHXdowIABWrBggU6dOuX0aFftwIEDiouL0yuvvKLk5GT169dPy5YtU9u2bZ0ezZjXX39dJSUl1lyy9ng8mjhxopYtW6auXbvq9ttvV4cOHTRx4kSnR7tqFKwgVFlZ2egv/IufV1VVOTGSMW3btpXL5XJ6jGZTX1%2Bv5557Tjt27NC8efOcHseo7du367333lNoaKjS09OdHueq1NXVKSMjQw8%2B%2BKBuvvlmp8cxqry8XF26dNHgwYOVn5%2BvTZs26ciRI1bcg3Xq1CkdOnRIR44cUV5env785z/r2LFjmjNnjtOjGVFXV6cXXnhB//qv/9rwi02wq6urU0REhBYsWKBPP/1UW7du1eHDh7Vq1SqnR7tqFKwg1KpVK507d85n7eLnkZGRTowEP5w9e1bp6enasmWLNm7cqM6dOzs9klERERFq166dMjIy9P777wf1GZE1a9aoRYsWGj9%2BvNOjGNemTRvl5OQoJSVFbrdb1113nTIyMvTee%2B/p7NmzTo93VVq0aCFJmjdvnqKiotSmTRtNnz5dO3fuVGVlpcPTXb2CggKVlZUpJSXF6VGMeeedd/T222/r/vvvV4sWLRQfH6%2B0tDT96U9/cnq0q0bBCkLx8fE6efKkjh8/3rB2%2BPBhtW/fXtdcc42Dk%2BGHHD16VGPGjNHZs2e1efNma8rVvn37dNddd/m84uf8%2BfMKDw8P6stqr7/%2Bunbv3q2kpCQlJSVp69at2rp1q5KSkpwe7aoVFRVpxYoVDfe7SN8/Z6GhoQ0FJVjFxcWprq5OFy5caFirq6uTJJ%2B8wertt99WcnKyWrVq5fQoxvzv//5vo1cMulwuhYeHOzSRORSsINShQwclJibqmWee0dmzZ/XNN99o9erVVv1WY5NTp05pwoQJ%2BuUvf6kXX3zRmhvAJalz586qrq7WypUrdf78ef3P//yPli1bppSUlKD%2Bx3rbtm3at2%2BfCgsLVVhYqGHDhmnYsGFWvJowJiZGOTk5WrdunWpqalRaWqrly5frnnvuCernTJL69OmjG264QXPnzlVlZaXKy8v13HPPadCgQVZcUtu7d6%2B6d%2B/u9BhG9evXT16vV//%2B7/%2Bu2tpaffPNN3rhhRc0fPhwp0e7ahSsILVq1SrV1NTo17/%2Bte699171799fU6dOdXosXMJrr72m0tJSvfXWW0pMTPR5L5tgFxkZqXXr1qm4uFh9%2B/bV%2BPHj1adPHyvegsJW7du315o1a/Tuu%2B%2BqR48eGjNmjLp27aqnnnrK6dGuWnh4uDZs2KCwsDANHjxYgwcPVvv27fXMM884PZoR3377rWJjY50ew6i4uDitWbNGf/3rX9WzZ0898MADGjhwoBWvJA%2Bpt%2BG8KQAAQADhDBYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYNj/BwhROT88kKMJAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common6976591126133551184\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">608</td>\n",
       "        <td class=\"number\">68.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">209</td>\n",
       "        <td class=\"number\">23.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:35%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">28</td>\n",
       "        <td class=\"number\">3.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">18</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">16</td>\n",
       "        <td class=\"number\">1.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme6976591126133551184\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">608</td>\n",
       "        <td class=\"number\">68.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">209</td>\n",
       "        <td class=\"number\">23.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:35%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">28</td>\n",
       "        <td class=\"number\">3.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">16</td>\n",
       "        <td class=\"number\">1.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">18</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">28</td>\n",
       "        <td class=\"number\">3.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">16</td>\n",
       "        <td class=\"number\">1.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:57%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">18</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:64%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Survived\">Survived<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.2%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.38384</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable443407277877304264\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 61.6%\">\n",
       "            549\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:62%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 38.4%\">\n",
       "            342\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable443407277877304264, #minifreqtable443407277877304264\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable443407277877304264\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">549</td>\n",
       "        <td class=\"number\">61.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">342</td>\n",
       "        <td class=\"number\">38.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:62%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Ticket\">Ticket<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"alert\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>681</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>76.4%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.0%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-6234017969826828777\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>347082</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.8%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        7\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1601</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.8%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        7\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>CA. 2343</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.8%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        7\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (678)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 97.6%\">\n",
       "            870\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-6234017969826828777, #minifreqtable-6234017969826828777\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-6234017969826828777\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">347082</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1601</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">CA. 2343</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">CA 2144</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">347088</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3101295</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">S.O.C. 14879</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">382652</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">349909</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">LINE</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (671)</td>\n",
       "        <td class=\"number\">834</td>\n",
       "        <td class=\"number\">93.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img 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lEREREbpqmekVERMQ1uNhU761IIygiIiLiJlTxExEREdegip/dlPiJiIiIa1DiZzeNoIiIiIibUMVPREREXIMqfnbTCIqIiIi4CVX8RERExDWo4mc3JX4iIiLiGpT42U0jKCIiIuImVPETERER16CKn900giIiIiJuQhU/ERERcQ2q%2BNlNiZ%2BIiIi4BiV%2BdtMIioiIiLgJVfxERETENajiZzeNoIiIiIibUMVPREREXIMqfnZT4iciIiKuQYmf3W5oBGNiYrj77ruJiooiKiqKyMhIWrVqxcyZMykqKiqrPt5SduzYQYMGDW56vYiIiIhRbrjiN3nyZHr16mV9fvjwYQYMGICvry/x8fEO7ZyIiIiIlSp%2BdrN7BBs0aEDz5s05dOgQZ8%2BeZcSIEcTExNCkSRM6dOjAihUrrNsuW7aMjh07Eh0dTbdu3fjoo4%2Bs6xISEmjbti0tWrTg4YcfZvPmzdZ1Bw8epH///jRv3pxOnTqxePFiLBaLdb/4%2BHjGjBlDdHQ0bdq0Yfbs2dZ9c3JymDhxIi1atKBt27a8%2BeabxMTEsGPHDgDOnTvHmDFjuP/%2B%2B2nVqhUTJkwgMzMTKK7etW3bltGjRxMdHc0///nPa44/JSWFZ599lqZNm9KhQwe2bdtm75CKiIiIlAm7zvHLz89n9%2B7dfPPNN8TFxfHiiy8SGBjIJ598go%2BPD%2B%2B//z5TpkyhS5cunD9/nunTp7N27Vrq1q3L1q1bGTp0KG3btuX48eMkJiayatUqQkJCSExM5IUXXqBNmzacP3%2Bexx9/nJEjR/Luu%2B9y8uRJhgwZgtls5q9//SsAmzZtYsaMGcycOZOvv/6aZ555hg4dOhAZGcm0adM4cOAAa9euJSAggMmTJ3Pq1CkAioqKGDJkCLVr12bjxo3k5%2Bfz/PPPM2HCBF5//XUAfvnlF%2BrWrcuMGTPIzc3l4MGDNmMwcuRIgoKC%2BOqrr8jIyGDw4ME3NZYpKSmkpqbaLAupU4fQwMCbau%2BmNWxo%2ByhlymQyLqYRsX%2BN7ORwxh6wEQWKKzFVHJHbjn6p7XZTU73Tpk2zPg8LC%2BOJJ56gX79%2BdOrUCT8/P7y9vTl9%2BjR%2Bfn7k5OSQnp6Op6cnFouF5cuX07lzZ%2B6991727t2Lh4cHp06dIj09nQ8//JD27dvzyCOPEBsbi8lkYt26dYSHh/Poo48CUK9ePZ566ik%2B%2BOADa%2BJXu3ZtevbsCUDbtm0JCQnhxIkTNGrUiHXr1pGQkECVKlUAmDBhAh9//DEABw4c4ODBg7z33nv4%2BfkBMG7cOP785z/z0ksvWY%2Bxd%2B/eeHt74%2B3tbTMWp06dYteuXWzcuBF/f3/8/f0ZNmwYQ4cOvdFhJTExkblz59osGzZ8OHHDh99wWw6xbJkhYY3KRYxKgsxmY%2BIClCtnVGSDDtqgA65g4M/41z9rbsOwNzOengYFNih2YaHzY16hxM9uN5z4TZw40eYcv6slJyfz6quvcuLECWrXrk2tWrWA4spa9erVWbJkCQsXLuTZZ5%2BlsLCQXr16MXbsWKKiokhISLCuN5vN9O/fn8GDB3Pq1CkOHjxIdHS0NU5RURGeV/2yh4SE2PTD29uboqIiLl68yOXLl6lWrZp1nb%2B/P0FBQQD8/PPPFBYW0rZtW5v9fXx8SE5Otj4PDQ0t8XjPnj0LQNWqVa3Latasef3B%2Bx2xsbHExMTYLAvp1g3%2B9a%2Bbau%2BmNWxYnPT17QtJSc6NDVi%2B2%2B30mCYT/HrmgNPl5jo/pslUnAPl5hpz3GZynBvQ4APOyHd%2B5ufhUZz0ZWWBEdfd%2Bfs7P6aRr2NTkUGJkKensUmYuCSH3c4lPz%2BfZ555hlGjRtG3b19MJhMHDhxg3bp1AKSlpVFYWMi8efMoKipi9%2B7dxMfHU6dOHdq3b09wcDCLFi0iLy%2BP7du3M2zYMBo1akRYWBgtW7Zk0aJF1lgXLlwgKyvrD/sUHByM2Wzm9OnT1K1bF4Ds7GwuXLgAFFcrzWYzO3bssCaSeXl5JCcnU6tWLb777jsATNd5GxkWFgYUJ7zh4eFA8dTwzQgNDb02wfzxx5tqyyGSkmDPHuPiuwmj/lFdiW1MfIMO2qADNvKGB0VFxsYXcThV/OzmsBHMz88nJycHs9mMyWTi9OnTvPbaa9Z1p0%2Bf5sknn2T79u14eHhQuXJlAIKCgti/fz8DBw4kKSkJHx8fgoODreu6devG3r17WbduHQUFBdaLKWbMmPHHB%2BfhQe/evUlISODs2bNcvnyZ6dOnU/jrO6TGjRtTq1YtZsyYQVZWFjk5OUybNo0BAwZYt/k9VatWpVWrVkyfPp309HRSU1Ovma4VERERuVU4rOJXvnx5pk2bxltvvcUrr7xCcHAwffr04ejRo/zwww907tyZCRMmMGnSJFJSUqhQoQJ9%2B/alS5cumEwmTpw4weDBg7lw4QLBwcGMHz%2BeJk2aALBw4UJmzZrFK6%2B8gqenJ%2B3ateOFF14oVb9Gjx7NlClTePDBB/Hz8yM2NhYPDw%2B8vb3x8vLinXfeYebMmXTq1Inc3FwaN27Me%2B%2B9R7lSng80e/ZsJk%2BeTPv27fH396dXr17s27fvpsdRRERErkMVP7uZLBYjJ5vK3rfffkuDBg0ICAgAIDMzk2bNmrFx40Zq165tbOf%2BiBFnKkdFwe7d0LSpIVO9liLn/zoaeW5QjpNPd4Pi4zWbi2Mbcdy%2BXHZuQIMPOD3P1%2BkxPTygQgXIyDBmqvfXP7dOpXP8DIhthEaNHN/mb%2B7Wcbu77VPnd999l6lTp5KTk0Nubi5z5syhTp06t37SJyIiIuJgt33iN2nSJDIyMmjbti33338/J0%2BeLPFGzCIiInKL8/Bw/Jebcdg5freqypUrM3/%2BfKO7ISIiIvZyw0TN0TSCIiIiIm7itq/4iYiIyG1CFT%2B7aQRFRERE3IQqfiIiIuIaVPGzmxI/ERERcQ1K/OymERQRERFxE6r4iYiIiGtQxc9uGkERERERN6GKn4iIiLgGVfzspsRPREREXIMSP7sp8RMRERG5SWlpabz00kvs3LkTT09Punfvzrhx4/Dysk2xBg4cyHfffWezLDs7m9jYWF5%2B%2BWXOnTvH/fffT/ny5a3rg4KC%2BPzzzx3aXyV%2BIiIi4hpuwYrfiBEjqFy5Mlu3buXcuXMMHjyYxYsXM3DgQJvtFi5caPN8xYoVzJ07l2HDhgGwf/9%2BqlWr5vBE77duvREUERERcQEnT55k586djB07Fl9fX2rUqMGQIUNYunTp7%2B53/PhxpkyZwqxZswgNDQWKE7%2B77rqrzPusip%2BIiIi4hjKo%2BKWkpJCammqzLCQkxJqQ/Z4jR44QGBhI5cqVrcvCw8M5ffo0ly5dIiAgoMT9Jk%2BeTM%2BePYmOjrYu279/P%2Bnp6Tz00EOcO3eOu%2B%2B%2Bm3HjxlGvXr2bPLKSKfETERER11AGiV9iYiJz5861WTZs2DDi4uL%2BcN%2BsrCx8fX1tll15np2dXWLit2vXLvbt28esWbNslgcEBFCvXj0GDRqEj48Pb731Fk888QQbNmygQoUKN3pY16XET0RERNxWbGwsMTExNstCQkJKtW/58uW5fPmyzbIrz/38/ErcJzExkS5dulwTY/bs2TbPn3/%2BeVauXMmuXbto3759qfpTGkr8RERExDWUQcUvNDS0VNO6Jalfvz4XL17k3LlzVKpUCYBjx44RFhZWYpWuoKCAzZs3M2/ePJvlmZmZzJs3j379%2BlGtWjUACgsLKSgowGw231TfrkcXd4iIiIjchNq1a9OsWTOmTZtGZmYmycnJzJ8/n969e5e4/eHDh8nNzaVp06Y2y/39/fnvf//LzJkzycjIICsriylTplC9enWb8wAdQYmfiIiIuAYPD8d/2WnOnDkUFBTQoUMH%2BvTpQ%2BvWrRkyZAgAUVFRrFu3zrptcnIyFStWpFy5cte0M3/%2BfIqKiujYsSOtW7cmNTWVBQsW4O3tbXcfr2ayWCwWh7YojmMyOT9mVBTs3g1Nm8KePU4Pbyly/q%2BjyQRGvQpycpwf02QCs7k4thHH7cvlP97IkQw%2B4PQ83z/eyME8PKBCBcjIgKIip4fnOhcylikjX8emokJjAnt6QqGBsY3QubPj29y40fFt3sJ0jt8tzIgkCMAEWL7bbUxsDycnu78muqZmxiS665Y7/2ccFASdOsFXX8GFC04PT8eOzk2EPD0h0AwXc8yG/I%2B86ib8TnPlPaO3tzHJkOlc6h9v5EheXhAUhOniBSgocG5sgAMHnB/T3x%2BaNy9%2Bo56Z6fz4DrzYQJxLiZ%2BIiIi4hlvwkztcjUZQRERExE2o4iciIiKuQRU/uynxExEREdegxM9uGkERERERN6GKn4iIiLgGVfzsphEUERERcROq%2BImIiIhrUMXPbkr8RERExDUo8bObRlBERETETajiJyIiIq5BFT%2B7aQRFRERE3IQqfiIiIuIaVPGzmxI/ERERcQ1K/OymERQRERFxE6r4iYiIiGtQxc9uGkERERERN6GKn4iIiLgGVfzspsRPREREXIMSP7tpBEVERETchCp%2BIiIi4hpU8bObEj8RERFxDUr87KYRFBEREXETqviJiIiIa1DFz24aQRERERE3oYqfiIiIuAZV/OymxE9ERERcgxI/u91SI5iens6kSZNo27YtkZGRtGrVinHjxvHLL784PNY//vEPBg4c6PB2ARo0aMCOHTvKpG0RERGRm3VLVfxGjhxJhQoVWLFiBSEhIZw7d46pU6fyxBNPsH79ery8HNfdZ5991mFtiYiIiBOo4me3W2oEv/vuOx544AFCQkIAqFSpEuPHj6dJkyZcunSJmJgYVq1aZd1%2Bx44dNGjQAICff/6ZBg0aMGPGDJo3b8748eOJiori66%2B/tm5/6dIlGjduzP/%2B9z8SEhLo378/RUVFxMTEkJiYaN2usLCQ1q1b8%2BmnnwLw3//%2Bl969exMdHU3Xrl1Zt26dddv8/HymT59Oy5Ytueeee1i4cGGZjpGIiIjIzbqlKn5du3Zl4sSJ7Nq1ixYtWtCkSROqVavGjBkzSt1GVlYW27ZtIycnB4DVq1fTqlUrAD7%2B%2BGNq1apF48aN2bJlCwAeHh48/PDDrF69mtjYWAC%2B/vpr8vLy6NChA0lJSQwePJjXXnuNDh06sG/fPoYMGUJQUBCtW7dm/vz5fPnll6xYsYLg4GAmTZp0U8eekpJCamqqzbJKlUIIDQ29qfZcVlSUc%2BM1bGj76GRBQc6PWaGC7aOzeXoaE8/Zca8wmYyLaURsABw4O1MqRv%2BQ/f2dH7N8edtHZ8rMdH7MK1Txs9stlfi98sortGzZkg0bNjBhwgQyMjKoWbMmcXFxdO/evVRt9OzZEx8fH3x8fHjkkUd44oknyMzMxN/fn9WrV9O7d%2B9r9unduzfz5s3jp59%2BombNmqxevZoePXrg4%2BPD8uXL6dChA506dQKgadOm9OnTh6VLl9K6dWvWrl3Ls88%2BS40aNQB48cUXbSqCpZWYmMjcuXNtlg0dOoz4%2BLgbbssRDPuHsXu3MXGXLTMkbCdDoha7914DgxvAqETXSOXKGRTYbMA7GoCAAGPiNm9uTFyARo2cH/OLL5wf8wolfna7pRI/Dw8PevToQY8ePbBYLBw7doy1a9fy3HPPWad//8jVFbKoqCiqV6/Oxo0biYyMJCkpiQULFlyzT%2BXKlWndujVr1qxhwIABfP7556xcuRKAU6dO8c033xAdHW3dvrCwkJo1awLFlboqVapY1wUEBFCxYsUbPvbY2FhiYmJsllWqFILFcsNN2c1kwpC4AKZmTZ0bsGHD4qSvb19ISnJubGDTDOcnuhUqFCd927dDRobTw9OihXPjeXoWH3NGBhQWOjc2gNns/JgmU3HSl5trzGvZfPmCcwN6ehYnfZcuGfNDPnrU%2BTHLly9O%2Bg4ehOxs58cXl3XLJH5bt24lPj6eL774gsDAQEwmE/Xq1WP06NFs27aNQ4cO4eHhQX5%2BvnWfCxeu/eNi%2Bk2pqnfv3nz88cecPHmSjh07EhgYWGL8Rx55hFdffZXQ0FAaNmxI/fr1AQgLC%2BMvf/kLL7/8snXblJQULL/%2BNQ0LCyM5Odm6Ljs7m4yb%2BG8aGhp6zbSuUcmXofbsMSZuUpIhsUv4FXaajAxj4hvxf/lKXCNiG/k6tlgMil9QYEBQin/ARsQ2cuozO9vY%2BM6mip/dbpkRbN68OcHBwTz//PMcPnyY/Px8MjMzWbduHSdOnKBdu3aEh4ezefNmcnJySE1N5f333//Ddnv27MnevXtZs2YNjzzyyHW3a9euHdnZ2fzzn/%2B02e5K4vj1119TVFTEiRMn6NevH%2B%2B%2B%2By5QnDAuXLiQY8eOkZuby4wZM6iadR4AACAASURBVCg06j%2BbiIiIyO%2B4ZRI/s9nMsmXLCAkJYfDgwURHR9OuXTvWrVvHe%2B%2B9R3h4OGPGjCErK4v777%2Bfxx57rFTn/QUGBhITE4OXlxf3/s5JTV5eXvTq1YsLFy7QpUsX6/ImTZrw%2Buuv8/rrr9O8eXP69etHTEwMo0ePBmDQoEF0796dfv360apVKypUqHDdqqKIiIjYwcPD8V9uxmSxuOWEoksw7Dw7I8/x83DyVSVRUcUXlDRtashUb%2BJy5w90UBB06gSbNhkz1duxo3PjeXpCYCBcvGjMVK8RF12aTMXnFubkGPNa9s1M/eONHMnLq/gX%2B8IFY6Z6Dxxwfkx//%2BKLSr791pip3vbtnR8TYOxYx7f52muOb/MW5n6proiIiIibumUu7hARERH5XW44NetoGkERERERN6GKn4iIiLgGVfzspsRPREREXIMSP7tpBEVERERuUlpaGkOGDCE6OpqWLVsydepUCq5zdfnAgQO5%2B%2B67iYqKsn599dVXQPGngs2cOZP77ruPqKgoBg8eTEpKisP7q8RPREREXMMteB%2B/ESNGUL58ebZu3cqKFSvYvn07ixcvLnHbAwcOsGjRIvbs2WP9atOmDQBvv/0227ZtY%2BXKlWzduhWz2cyLL75od/9%2BS4mfiIiIyE04efIkO3fuZOzYsfj6%2BlKjRg2GDBnC0qVLr9k2OTmZ9PR07rzzzhLb%2Buijjxg0aBBVqlTB39%2BfF154ga%2B%2B%2BsrmY2EdQef4iYiIiGsog3P8UlJSSE21vel4SEgIoaGhf7jvkSNHCAwMpHLlytZl4eHhnD59mkuXLhEQEGBdvn//fvz8/Bg5ciT79%2B%2BnUqVKDBgwgN69e5ORkcEvv/xCRESEdftKlSpRsWJFDh8%2BTI0aNRxwpMWU%2BImIiIhrKIPELzExkblz59osGzZsGHFxcX%2B4b1ZWFr6%2BvjbLrjzPzs62Sfzy8vKIjIxk5MiR1K9fnx07dhAXF4efnx9RUVEAlP/NR/2YzWaysrJu6riuR4mfiIiIuK3Y2FhiYmJsloWEhJRq3/Lly3P58mWbZVee%2B/n52Szv2bMnPXv2tD5v1aoVPXv25NNPP%2BW%2B%2B%2B6z2feKnJyca9qxlxI/ERERcQ1lUPELDQ0t1bRuSerXr8/Fixc5d%2B4clSpVAuDYsWOEhYVRoUIFm21XrFiBn58fXbp0sS7Ly8ujXLlyVKxYkcqVK3P06FHrdG9qaioXL160mf51BF3cISIiInITateuTbNmzZg2bRqZmZkkJyczf/58evfufc22mZmZTJkyhUOHDlFUVMSXX37Jxx9/TGxsLAC9evXi7bffJjk5mczMTKZNm0aLFi2oWbOmQ/usip%2BIiIi4hlvwBs5z5szh5ZdfpkOHDnh4eNCzZ0%2BGDBkCQFRUFJMnT6Z79%2B48/vjjZGdnM2zYMNLS0qhRowYzZ84kOjoagKFDh1JQUMCjjz5KVlYWLVu25M0333R4f5X4iYiIiGu4BRO/SpUqMWfOnBLX7dmzx/q9yWRiyJAh1qTwt7y9vRkzZgxjxowpk35eceuNoIiIiIiUCVX8RERExDXcghU/V6MRFBEREXETqviJiIiIa1DFz25K/ERERMQ1KPGzm0ZQRERExE2o4iciIiKuQRU/u2kERURERNyEKn63sJwc58c0mcBshtxcsFicH3/dcucGDQqCTsCmGbu5cMGpoQGI/avJ%2BUGjoqDTbjr9vSlcdXNRZ/nXYuf%2BjO%2B4A7p1g61b4fx5p4YG4PEHU50f1MsLzEGYL1%2BAggLnxw8MdH5MAH9/Q8KOWt/e6TGrVYPRzWH2V805dcrp4Xnd%2BYdcTBU/uynxExEREdegxM9uGkERERERN6GKn4iIiLgGVfzspsRPREREXIMSP7tpBEVERETchCp%2BIiIi4hpU8bObRlBERETETajiJyIiIq5BFT%2B7KfETERER16DEz24aQRERERE3oYqfiIiIuAZV/OymERQRERFxE6r4iYiIiGtQxc9uSvxERETENSjxs5tGUERERMRNqOInIiIirkEVP7tpBEVERETchCp%2BIiIi4hpU8bObEj8RERFxDUr87KYRFBEREXETqviJiIiIa1DFz24aQRERERE3oYqfiIiIuAZV/OymxE9ERERcgxI/u2kERURERNyEKn4iIiLiGlTxs5vLJ34xMTGkpqbi5VV8KBaLBX9/f7p168bYsWPx%2BJ1fkpiYGIYNG0avXr2c1V0RERERw7h84gcwefJkm%2BTt8OHDDBgwAF9fX%2BLj4w3smYiIiDiMKn52uy1HsEGDBjRv3pxDhw6RnZ3Nyy%2B/zL333kt0dDSDBg3i1KlT1%2Bxz9uxZRowYQUxMDE2aNKFDhw6sWLHCun7ZsmV07NiR6OhounXrxkcffWRdl5CQQNu2bWnRogUPP/wwmzdvdspxioiIuBUPD8d/uZnbouJ3tfz8fHbv3s0333xDXFwcL7/8MseOHWPVqlUEBwczceJERo0aRWJios1%2BL774IoGBgXzyySf4%2BPjw/vvvM2XKFLp06cL58%2BeZPn06a9eupW7dumzdupWhQ4fStm1bjh8/TmJiIqtWrSIkJITExEReeOEF2rRpg7e3d6n7nZKSQmpqqs2ygIAQQkJCHTIupWUy2T46W1CQc%2BNVqGD76HRRUc6P2bCh7aOT3XGHc%2BNVrGj76HReBvyZ9fS0fZQyVa2a82OGhto%2BOlMJtRNxISaLxWIxuhP2iImJIS0tzSbJCgsLo2vXrjz11FM0a9aMt99%2Bm1atWgFw6dIlTp48yd13321zjt/Zs2fx8/PDbDZz5swZtm/fzksvvcQXX3wBQKdOnejbty%2BdO3emSZMmeHh44OHhwZ49e3jsscd45plnaN%2B%2BPQ0bNsTDwwPTDWZOCQkJzJ0712bZ0KHDiI%2BPs3OEREREHGfUKHj9dYOCf/qp49vs0sXxbd7CbouK38SJE0u8QCM1NZW8vDyqVq1qXRYQEMDdd999zbbJycm8%2BuqrnDhxgtq1a1OrVi0AioqKqF69OkuWLGHhwoU8%2B%2ByzFBYW0qtXL8aOHUtUVBQJCQnW9Wazmf79%2BzN48ODfvbDkt2JjY4mJibFZFhAQQk5OqZtwCJMJypWD3Fww4i3BV185N16FCnDvvbB9O2RkODc2QKe/N3V%2B0IYNYdky6NsXkpKcHn795N1OjVexIrRpU/y7lZ7u1NAAdGt1wflBPT0hIAAuXYLCQufH9/d3fkxvb8jPd35cYPac0s/uOEpoKPTvD0uWQEqK08OLC7stEr/rCQ4OxsfHhzNnzlC3bl0A0tLSWLBgASNGjLBul5%2BfzzPPPMOoUaPo27cvJpOJAwcOsG7dOus%2BhYWFzJs3j6KiInbv3k18fDx16tShffv2BAcHs2jRIvLy8ti%2BfTvDhg2jUaNGtGvXrtR9DQ0NJfQ3NfvLl41JvqA4rhGxLxjwPxKKkz5DYu/ZY0DQXyUlGRL//HmnhwSKkz5DYhcUGBD0V4WFxsZ3E0ZOfaakuNnUqxuek%2Bdot/UIenh40LNnTxISEjh79iy5ubm8%2Beab7N27F7PZbN0uPz%2BfnJwczGYzJpOJ06dP89prr1nXnT59mieffJLt27fj4eFB5cqVAQgKCmL//v0MHDiQpKQkfHx8CA4Otq4TERERB9LFHXa7rSt%2BAH//%2B9954403eOSRR8jJyaFFixa89dZbNtuUL1%2BeadOm8dZbb/HKK68QHBxMnz59OHr0KD/88AOdO3dmwoQJTJo0iZSUFCpUqEDfvn3p0qULJpOJEydOMHjwYC5cuEBwcDDjx4%2BnSZMmBh2xiIiIOEtaWhovvfQSO3fuxNPTk%2B7duzNu3Djr/YWv9u9//5vFixeTkpJCaGgojz32GI8%2B%2BihQfGpZs2bNsFgsNtcJbNu2jfLlyzusvy6f%2BH3%2B%2Bee/u97Pz48XX3yRF1988Xf37dGjBz169LBZ//TTT1u/7927N7179y4xxjPPPMMzzzxzI90WERGRG3ULVuhGjBhB5cqV2bp1K%2BfOnWPw4MEsXryYgQMH2mz3f//3f7z%2B%2BussWLCAJk2asHfvXp5%2B%2BmkqVapE586dOXr0qPXOJD4%2BPmXW31tvBEVERERcwMmTJ9m5cydjx47F19eXGjVqMGTIEJYuXXrNtmfPnmXQoEFERkZiMpmIioqiZcuWfPvttwDs37%2BfBg0alGnSB7dBxU9ERETcRBlU/Eq6j25ISMg1F1yW5MiRIwQGBlrP/QcIDw/n9OnTXLp0iYCAAOvyK1O6V6SlpfHtt9/y/PPPA8WJX25uLg8//DCnTp0iPDyc0aNH07SpY%2B/%2BoMRPREREXEMZJH6JiYnX3Ed32LBhxMX98X10s7Ky8PX1tVl25Xl2drZN4ne11NRUnnnmGe666y4eeughAMxmM40bN2b48OFUrFiRpUuX8tRTT7Fu3Tpq1KhxM4dWIiV%2BIiIi4rZKuo9uSEhIqfYtX748ly9ftll25bmfn1%2BJ%2B%2Bzdu5fhw4cTHR3N9OnTrReB/P3vf7fZ7qmnnmLVqlVs2bKFfv36lao/paHET0RERFxDGVT8SrqPbmnVr1%2Bfixcvcu7cOSpVqgTAsWPHCAsLo0IJnwW6YsUKXnnlFeLj43nyySdt1r3xxht07tyZO%2B%2B807osLy%2BPcuXK3VTfrkcXd4iIiIjchNq1a9OsWTOmTZtGZmYmycnJzJ8/v8S7gGzcuJFJkyaRkJBwTdIH8MMPPzB16lTrp47NnTuXzMxMHnjgAYf2WYmfiIiIuIZb8AbOc%2BbMoaCggA4dOtCnTx9at27NkCFDAIiKirJ%2BCtjcuXMpLCwkPj6eqKgo69eECRMAmD59OjVr1qRHjx60bNmSnTt38t577xEYGGh3H6%2BmqV4RERFxDbfgffwqVarEnDlzSly356qPxVy/fv3vthMYGMj06dMd2reS3HojKCIiIiJlQhU/ERERcQ23YMXP1SjxExEREdegxM9uGkERERERN6GKn4iIiLgGVfzsphEUERERcROq%2BImIiIhrUMXPbkr8RERExDUo8bObRlBERETETajiJyIiIq5BFT%2B7aQRFRERE3IQqfiIiIuIaVPGzmxI/ERERcQ1K/OymERQRERFxE6r4iYiIiGtQxc9uGkERERERN2GyWCwWozsh13H5svNjmkxgNkNODhjwq5GW7evUeJ6eEBgIFy9CYaFTQwPw8cfOj3nHHdCtG6xfD%2BfPOz/%2B4wNMzg0YFQW7d0PTprBnj3NjA2vXOP91VLEitGsHX34J6elOD0/Nms6N5%2BsLDRtCUpIxfzajws44P6iXF4SEQGoqFBQ4P36VKs6PCXDkiOPbrF/f8W3ewjTVKyIiIq5BU7120wiKiIiIuAlV/ERERMQ1qOJnN42giIiIiJtQxU9ERERcgyp%2BdlPiJyIiIq5BiZ/dNIIiIiIibkIVPxEREXENqvjZTSMoIiIi4iZU8RMRERHXoIqf3ZT4iYiIiGtQ4mc3jaCIiIiIm1DFT0RERFyDKn520wiKiIiIuAlV/ERERMQ1qOJnNyV%2BIiIi4hqU%2BNlNIygiIiLiJlTxExEREdegip/dNIIiIiIibkIVPxEREXENqvjZTYmfiIiIuAYlfnbTCIqIiIi4CVX8RERExDWo4mc3jaCIiIiIm1DFT0RERFyDKn52U%2BInIiIirkGJn900gldZunQpDRo0YPHixUZ3RURERFxAWloaQ4YMITo6mpYtWzJ16lQKCgpK3HbLli1069aNyMhIunTpwhdffGGzfsGCBbRp04bIyEj69%2B/P8ePHHd5fJX5XWbp0KX/72994//33r/tDExEREYN4eDj%2By04jRoygfPnybN26lRUrVrB9%2B/YSC0gnTpwgLi6O4cOHs2vXLuLi4hgxYgRnz54FYPXq1SxZsoRFixaxY8cOGjVqRHx8PBaLxe4%2BXk2J36%2B2b99OWloaf//73ykqKmLjxo3WdRcuXGDkyJE0a9aMDh06sGTJEu68805%2B/vlnAH766SeeffZZWrZsSfv27XnjjTfIy8sz6lBERETECU6ePMnOnTsZO3Ysvr6%2B1KhRgyFDhrB06dJrtl29ejXR0dF07NgRLy8vHnzwQZo3b05iYiIAH374IX379qV%2B/fqUK1eO0aNHc/r0aXbs2OHQPuscv18tWbKEPn36YDab6du3L%2B%2B%2B%2By5du3YFYMyYMZhMJjZv3kxRURFjxoyhsLAQgOzsbAYMGEDXrl156623OH/%2BPPHx8RQVFTF69OhSx09JSSE1NdVmWUhAAKEhIY47yNIwmWwfnczT05h4zo57xR13OD9mxYq2j04XFeXceA0b2j46mRHj7O9v%2B%2Bhsvr7OjVeunO2j03kZ8K/0SkwjYhs5I1YG5/iV%2BP83JITQ0NA/3PfIkSMEBgZSuXJl67Lw8HBOnz7NpUuXCAgIsC4/evQoERERNvvXq1ePpKQk6/pBgwZZ13l7e1O7dm2SkpK45557burYSqLEDzh16hRbt25lwoQJAPTp04d58%2Baxc%2BdOatWqxddff82nn35KYGAgAOPHj7cmhV9%2B%2BSV5eXmMGjUKk8lElSpVGD58OPHx8TeU%2BCUmJjJ37lybZcOGDiUuPt5BR3mDDPoLGmg2JCwVKhgTt1s3Y%2BICtGljUOBuu42Ju2yZIWHbGRK1WHS0gcENUKeOUZGd/Ab9akFBzo955ozzY/7KguOLEiX%2B/x02jLi4uD/cNysrC9/fvNO58jw7O9sm8StpW7PZTHZ2dqnWO4oSP2DZsmUUFBTQo0cP67KCggLeffddnn32WQCqV69uXVejRg3r96dOneL8%2BfM0b97cusxisZCfn09aWhrBwcGl6kNsbCwxMTE2y0ICAiAn56aO6aaZTMVJX24uOPi8gtK4mOPczM/Tszjpy8iAX4u4TrV1q/NjVqxYnPR99RWkpzs/freJTZ0bsGHD4qSvb1/49Z21M335uvMTXX//4qRv1y7IzHR6eMLCnBuvXLnipO/HH4v/dDlbw%2BDUP97I0by8ipO%2BCxeMrcDdBkr8/1vK2bby5ctz%2BfJlm2VXnvv5%2Bdks9/X1Jec3/9NzcnKs2/3Rekdx%2B8QvNzeXFStWMHXqVO677z7r8h9%2B%2BIGnn36aZ555BihO8Or8%2Bnby1KlT1u3CwsKoWbMmn332mXVZZmYmaWlp3HED83ihoaHXlpUvXzYk%2BQKK4xoQ24jk60pcI2KfP%2B/8mFekpxsUf88eA4JSnPQZENuI5PqKzExj4ht1GkFubvGfTaczMvEqKHCrxK%2BoyPFtlvj/t5Tq16/PxYsXOXfuHJUqVQLg2LFjhIWFUeE3U0kREREcPHjQZtnRo0e56667rG0dOXKE9u3bA5Cfn8%2BJEyeumR62l9tf3LF%2B/XpMJhPdunUjLCzM%2BtWmTRsiIiJYtWoV7du357XXXiM9PZ309HReffVV6/7t27cnKyuLhQsXkpeXx6VLlxg3bhwjR47EZNB5ciIiIrejoiLHf9mjdu3aNGvWjGnTppGZmUlycjLz58%2Bnd%2B/e12zbvXt3du7cyYYNGygoKGDDhg3s3LnTOtv48MMP88EHH5CUlERubi6zZ8%2BmUqVKRDv4nA23T/yWLVtGt27d8Pb2vmZdbGwsa9euZerUqZhMJtq1a8df/vIX7rzzTqD4xEt/f38WL17Mjh07aNOmDR07dsTDw4O3337b2YciIiIiTjZnzhwKCgro0KEDffr0oXXr1gwZMgSAqKgo1q1bBxRf9DFv3jzeeecdmjdvzvz580lISLDOJvbu3ZsBAwYwdOhQ7rnnHg4dOsQ777xTYn5iD5PF0TeIuQ1t27aNZs2aYTYXn392%2BPBhevbsyd69eylXlhdBGDFnYTKB2Vx8bqEBvxpp2c69HNDTEwID4eJFY6Z6P/7Y%2BTHvuKP4opL1642Z6n18gJMr4VFRsHs3NG1qyFTv2jXOfx1VrAjt2sGXXxoz1VuzpnPj%2BfoWn8qZlGTMn82oMAMudvDygpAQSE01Zqq3ShXnx6RszuE07Gpwg7h9xa80Zs6cydtvv01BQQGZmZm8/fbb3HfffWWb9ImIiIg4mBK/Upg9ezZ79%2B7lnnvuISYmBk9PT5vz/ERERKTs3Wrn%2BLkit7%2BqtzTq16/Pv/71L6O7ISIi4tbcMVFzNFX8RERERNyEKn4iIiLiElTxs58qfiIiIiJuQhU/ERERcQmq%2BNlPiZ%2BIiIi4BCV%2B9tNUr4iIiIibUMVPREREXIIqfvZTxU9ERETETajiJyIiIi5BFT/7KfETERERl6DEz36a6hURERFxE6r4iYiIiEtQxc9%2BqviJiIiIuAlV/ERERMQlqOJnPyV%2BIiIi4hKU%2BNlPU70iIiIibkIVPxEREXEJqvjZTxU/ERERETehip%2BIiIi4BFX87KfET0RERFyCEj/7mSwWi8XoTkjJ0tOdH9PDAypUgIwMY15gPj7OjWcygdkMOTlgxCvBNzPV%2BUG9vCAoCC5cgIICp4df%2B98Qp8arWBHatYMvvzTmNdWjp8n5QaOiYPduaNoU9uxxfvyEBOfGCwmB2FhITIRUA15THTo4P6bZDHXqwI8/Fv8Bc7Y//cn5MYFjxxzfZni449u8laniJyIiIi5BFT/76eIOERERETehip%2BIiIi4BFX87KfET0RERFyCEj/7aapXRERExE2o4iciIiIuQRU/%2B6niJyIiIuImVPETERERl6CKn/2U%2BImIiIhLUOJnP031ioiIiLgJVfxERETEJajiZz9V/ERERETchCp%2BIiIi4hJU8bOfEj8RERFxCUr87KepXhERERE3oYqfiIiIuARV/OynxE9ERERcghI/%2B2mqV0RERMRNqOInIiIiLkEVP/sp8RMREREpI9nZ2UyZMoXPP/%2BcgoICOnTowMSJE/Hz8ytx%2B40bNzJ//nySk5MJDAykV69eDBkyBA%2BP4knaLl26cPr0aetzgBUrVhAeHl6q/ijxExEREZfgihW/KVOmcObMGTZu3EhhYSEjRoxg1qxZTJw48ZptDxw4wHPPPcebb75J27Zt%2BfHHHxk0aBDly5fnySefJDMzkx9//JHNmzdTrVq1m%2BqPzvETERERl1BU5PivsnT58mXWr19PfHw8gYGBBAcHM2bMGFatWsXly5ev2f7UqVP89a9/pX379nh4eBAeHs4DDzzAt99%2BCxQnhoGBgTed9IEqfiIiIuLGUlJSSE1NtVkWEhJCaGhoqfbPycnh7NmzJa67fPky%2Bfn5REREWJeFh4eTk5PDiRMn%2BNOf/mSzfefOnencubNN219%2B%2BSXdunUDYP/%2B/fj6%2BtKvXz%2BOHDlCtWrViIuLo3379qXqKyjxExERERdRFhW6xMRE5s6da7Ns2LBhxMXFlWr/ffv28dhjj5W4bvjw4QCUL1/euszX1xeArKys3203MzOT4cOHYzabGTBgAAAmk4m7776bUaNGUbVqVT777DPi4uL44IMPiIyMLFV/lfiJiIiI24qNjSUmJsZmWUhISKn3b9myJYcPHy5x3aFDh3jrrbe4fPmy9WKOK1O8/v7%2B123z%2BPHjxMfHExwczPvvv2/dduDAgTbbde/enY8//piNGzcq8RMREZHbS1lU/EJDQ0s9rXuj6tSpg7e3N0ePHqVJkyYAHDt2DG9vb2rXrl3iPlu2bGHUqFH06dOH0aNH4%2BX1/1O1RYsWceedd3Lvvfdal%2BXl5VGuXLlS9%2Bm2vLgjPT2dSZMm0bZtWyIjI2nVqhXjxo3jl19%2BAaBr166sW7cOgP79%2B5OQkHDdtvLy8pg9ezYdO3YkKiqKe%2B65h7i4OI4dO%2BaUYxEREZFirnZxh6%2BvL126dGHWrFmcP3%2Be8%2BfPM2vWLB566CHMZvM12%2B/du5ehQ4fy/PPPM27cOJukD%2BDMmTNMnjyZ5ORkCgoKWLFiBXv27OEvf/lLqft0WyZ%2BI0eO5MKFC6xYsYK9e/eyZs0a8vLyeOKJJygoKOCTTz6he/fupWprypQp7Nmzh8WLF7Nnzx42bdpEWFgYjz76KJcuXSrjIxERERFXNnHiRGrXrk23bt3485//TPXq1ZkwYYJ1fdeuXfnHP/4BwD/%2B8Q8KCgqYOnUqUVFR1q8rU7zPPfccbdq0oW/fvkRHR7N8%2BXL%2B%2Bc9/UqtWrVL357ac6v3uu%2B%2BYOnWqdY6%2BUqVKjB8/ntmzZ3Pp0iV69%2B7NsGHD6NWrFwA//fQT/fv3JykpifDwcMaPH0/jxo2tbfXo0YPq1asDEBAQwHPPPUdmZiapqakEBATQv39/GjVqxM6dOzl%2B/Dh169Zl/PjxREdHGzMAIiIityFXvI%2Bfv78/U6ZMYcqUKSWu/%2BSTT6zfX0kAr8fHx4fx48czfvz4m%2B7PbZn4de3alYkTJ7Jr1y5atGhBkyZNqFatGjNmzChx%2B82bN/POO%2B8QGRnJwoULGTRoEP/5z38ICAiga9euzJ07lx9//JF77rmHJk2aUKdOHaZPn27TRmJiIm%2B//TZNmzZl0aJFDB48mE2bNhEUFFSqPpd0ObmvbwghIWVz3sH1XLkRuIdBtWCTyZh4zo5r5WXAS9DT0/bRySpWdG68K%2BdP/8551GUrKsr5MRs2tH10ths4Md4hrvydLeXfW4crYcquzPn42D46U06O82OKw5gsFovF6E44WlFREevXr2fDhg189913ZGRkULNmTeLi4ujevTsxMTHWil///v3505/%2BZM2eLRYLbdu2ZezYsdb75nz%2B%2BeesWbOGb7/9lvPnzxMaGspTTz1lvby6f//%2BNGjQgBdffNGmjREjRlirin8kISHhmsvJhw4dRnx86S4nFxERcYrvv4ff3H/OWT791PFtduni%2BDZvZbdlxc/Dw4MePXrQo0cPLBYLx44dY%2B3atTz33HMlXqJ9ZRoXiu%2BRExYWZnMzxpiYGOul3j/99BObNm1i1qxZ%2BPn58cgjjwDYXJ1zpY3fVvB%2BT0mXk/v6hpCRUeomHMLDA/z8ICvLmJK6t7dz45lMUK4c5OaCEW%2BBzJcvOD%2BopycEBMClS1BY6PTwX%2B5zH3JZTgAAIABJREFUblXG3x%2Bio2HXLsjMdGpoANqNaur8oA0bwrJl0LcvJCU5P/64cc6NFxQEnTrBpk1wwYDXVIsWzo/p4wPVqsGpU5CX5/z4BnHFqd5bzW2X%2BG3dupX4%2BHi%2B%2BOILAgMDMZlM1KtXj9GjR7Nt2zYOHTp0zT4pKSnW74uKijh9%2BjTVqlXj2LFj9OzZk5UrV1rvul2zZk0GDhzIvn37%2BP777637XZ0oXmmjSpUqpe53SZeTp6cb90vujKudSmJU/dliMSh2QYEBQX9VWGhI/PR0p4cEipM%2BQ2Lv2WNA0F8lJRkT/wbe9DrUhQvGxDZy6jMvT1OvckNuu6t6mzdvTnBwMM8//zyHDx8mPz%2BfzMxM1q1bx4kTJ2jXrt01%2B6xYsYJ9%2B/aRl5dHQkICXl5etG3blrp169KoUSMmTJjA//73P3Jzc7l8%2BTJbtmxhx44dPPDAA9Y2PvroIw4cOEBeXh7z5s3DYrHc0EeoiIiIyO9ztdu53Ipuu4qf2Wxm2bJlzJ07l8GDB5OWloa3tzeRkZG89957hIeHX7NPp06dmDhxIj/99BN33XUXixYtsn68yoIFC5g/fz5jx47l7NmzeHh48Kc//YnXXvt/7d15XJT1%2Bv/x1ygIiAsImNpmGUm54kbmjqmZ%2B5J2NNcszS1NPVmZpqTkkn4FLLPUCrNjmpqo35OVWblhuWUmbuXBhIDERFlkkPn94c/5OqlFZ5a7mXk/Hw8eOJ97Zq7rFnUur8/nc99zbS6g2KRJE2bMmMGJEye4//77WbZsGeXLl3fZeYuIiIj8GY8r/ODKtOmMGTNuenzr1q3WXycmJv7he5UvX57nnnuO5/5kzUp4eDhxcXF/LVEREREpMW/s0DmaRxZ%2BIiIi4nlU%2BNnP49b4iYiIiMiNqePnAH82XSwiIiL2U8fPfur4iYiIiHgJdfxERETELajjZz8VfiIiIuIWVPjZT1O9IiIiIl5CHT8RERFxC%2Br42U8dPxEREREvoY6fiIiIuAV1/Oynwk9ERETcggo/%2B2mqV0RERMRLqOMnIiIibkEdP/up4yciIiLiJdTxExEREbegjp/9VPiJiIiIW1DhZz9N9YqIiIh4CXX8RERExC2o42c/dfxEREREvIQ6fiIiIuIW1PGznwo/ERERcQsq/OynqV4RERERL6GOn4iIiLgFdfzsp8JPRERE3IIKP/up8Psbq1DBuNjlyhkT1/RrlmsD%2BviAfzD%2B%2BeegqMi1sQGCglwf8yqDfsh33OHaeAEBV75XqQIVK7o2NgDx8a6PGRZ25ftzz0GWi/9OAYwZ49p4kZHQty/Mng3797s2NsDu3a6PefXfq4sXIS/P9fHFbanwExEREbegjp/9tLlDRERExEuo4yciIiJuQR0/%2B6nwExEREbegws9%2BmuoVERER8RLq%2BImIiIhbUMfPfur4iYiIiHgJdfxERETELajjZz8VfiIiIuIWVPjZT1O9IiIiIl5ChZ%2BIiIi4heJix385W15eHs8//zxRUVE0bNiQf/7zn%2BTm5t70%2BdOmTaN27dpERkZav1atWmU9/tZbb9GyZUvq16/PgAED%2BPHHH/9SPir8RERERJwkJiaG9PR0PvnkE7Zs2UJ6ejrz5s276fMPHTpETEwM%2B/fvt3717dsXgHXr1pGYmMjSpUtJTk6mVq1ajB07FovFUuJ8VPiJiIiIW3C3jl9%2Bfj5JSUmMHTuWoKAgQkJCmDhxImvXriU/P/%2B65xcWFnLs2DFq1659w/f78MMP6devH%2BHh4fj5%2BTFhwgTS0tJITk4ucU7a3CEiIiJu4e%2B4uaOgoICMjIwbHsvPz8dsNnPvvfdax2rUqEFBQQGnTp3ivvvus3l%2BSkoKRUVFxMXFsXfvXsqXL0%2BvXr0YNmwYpUqV4sSJEzz55JPW5/v6%2BlK9enVSUlJ44IEHSpSvCj8RERHxWpmZmWRlZdmMhYWFUbly5RK9/uDBgwwcOPCGx5555hkAypYtax0LCAgAuOE6vwsXLtCkSRMGDBjA/PnzOXLkCKNGjaJUqVIMGzaM3Nxc6%2Buv8vf3Jy8vr0S5ggo/ERERcRPO6PitWrWKhIQEm7HRo0czZsyYEr0%2BKiqKo0eP3vDYDz/8wMKFC8nPzycwMBDAOsVbrly5657frFkzmjVrZn1ct25dBg0axObNmxk2bBgBAQEUFBTYvKagoMD63iWhwk9ERES8Vt%2B%2BfYmOjrYZCwsLc8h733XXXfj6%2BnLixAnq1asHwMmTJ61TtL/32Wef8euvv/LYY49ZxwoLC/H39wcgPDyc48eP06ZNGwDMZjOnTp2ymUr%2BM9rcISIiIm7BGZs7KleuTK1atWy%2BSjrN%2B2cCAgLo2LEj8%2BbNIzs7m%2BzsbObNm0fnzp2txdy1LBYLsbGx7Nq1C4vFwv79%2B3nvvfesu3p79erFihUrSElJ4dKlS7z22muEhobSqFGjEuekjp%2BIiIi4hb/j5o4/M23aNGbPnk2XLl0wm820bduWl156yXq8U6dOdOnShREjRtCuXTuef/55Xn75ZTIyMggNDWXMmDF069YNgN69e3PhwgVGjRpFdnY2derU4c0338TX17fE%2BajwExEREXGScuXKERMTQ0xMzA2Pb9q0yebxY489ZjPVey2TycTQoUMZOnTof52PCj8RERFxC%2B7Y8fu70Ro/ERERES%2Bhjp%2BIiIi4BXX87KfCT0RERNyCCj/7aapXRERExEuo4yciIiJuQR0/%2B3lk4RcdHU1WVhY%2BPldOz2KxUK5cObp06cKkSZMoVcpxjc74%2BHj27NlDYmKiw95TRERExBk8svADmD59Oj179rQ%2BPnr0KIMHDyYgIICxY8camJmIiIj8N9Txs5/XrPGrWbMmjRs35ocffiAjI4Nx48YRHR1NvXr1aNu2LWvWrLF57iuvvEJUVBQjRowAICkpic6dOxMZGUnHjh3ZvHmz9fm5ublMmTKF5s2bExUVxYIFC1x%2BfiIiIp7OGbds8zYe2/G7ltlsZt%2B%2BfezevZsxY8YwZcoUgoKC2LRpE2XKlOG9994jJiaGjh07EhgYCEBqairbtm3DbDaTnJzMCy%2B8QEJCAi1atGD79u2MHDnSelPkH374gUGDBhETE0NycjKDBw%2BmdevWREZGljjHzMxMsrKybMZCQ8Mcdr9At%2BHj4j%2BSpUvbfhenCwhwbTw/P9vvLuegm73/JcHBtt9d7S/82%2BcQERG2312tbFnXx7x6n9cb3O/V6fLyXB9THMZjC7/p06cza9Ys6%2BMqVaowZMgQHn/8cdq3b09gYCC%2Bvr6kpaURGBhIQUEB58%2BftxZ%2BnTt3JiAggICAANavX0/79u1p1aoVAC1btmTlypXccsstAISHh1vvo/fAAw8QGhpKamrqXyr8Vq1aRUJCgs3YqFGjGTt2jF2/D/8tk8mQsMZ9UFWoYExcI/2Fezs6klGfzXfdZUxcIvoaFBho396YuH0NOueVK42Ja6TwcNfHTE52fcz/zxs7dI7msYXftGnTbNb4Xev06dPMmTOHU6dOUb16de68804Aiq/5E3Vtpy0zM5P777/f5j3q1q1r/XVQUJDNsTJlynD58uW/lG/fvn2Jjo62GQsNDcNi%2BUtv4xAmE4bEBTD9ds61AUuXvlL05eTAX/yZOUS5cq6PCVeKPrPZkNApJ11bcPr5XSn6fvoJLl1yaWgAIg6ucn3Q4OArRd%2BWLXDOxX%2BnAGbPdm28iIgrRV%2B/fpCS4trYAO%2B%2B6/qY/v5Xir7jx6GgwPXxxW15bOF3M2azmeHDh/Pss8/Sr18/TCYT33//PRs2bLB5numallfVqlVJS0uzOb5s2TLq16/vsLwqV6583bSuUcWXoYqKjIl7%2BbJxsb1Mfr4xcS9dMij275ZwuNS5c8bE37/f9THhStFnRGwjpz4LCrxq6lUdP/t5zeaOq8xmMwUFBfj7%2B2MymUhLS2Pu3LnWYzfSo0cPPv30U7Zv305xcTFff/018fHxlC9f3pWpi4iIeDVt7rCf1xV%2BZcuWZdasWSxatIjIyEgGDhxIs2bNCA0N5dixYzd8TcOGDZk9ezazZ8%2BmUaNGzJkzh/nz5xNuxNoKERERkf%2BSR071bt269Q%2BPd%2BvWzboZ46qnnnrK%2BuujR49e95qOHTvSsWPH68bHjLl%2B88WfxRcREZG/zhs7dI7mdR0/EREREW/lkR0/ERER8Tzq%2BNlPhZ%2BIiIi4BRV%2B9tNUr4iIiIiXUMdPRERE3II6fvZTx09ERETES6jjJyIiIm5BHT/7qfATERERt6DCz36a6hURERHxEur4iYiIiFtQx89%2BKvxERETELajws5%2BmekVERES8hDp%2BIiIi4hbU8bOfOn4iIiIiXkIdPxEREXEL6vjZT4WfiIiIuAUVfvbTVK%2BIiIiIl1DHT0RERNyCOn72U8dPRERExEuo4yciIiJuQR0/%2B6nwExEREbegws9%2BmuoVERER8RLq%2BImIiIhbUMfPfir8/sZMxZeNCVy6tHGxv//etfHKlYPGjeHECbh40bWxgWeT2rg85q23woQJ8FqcL2fOuDw88yeluzagjw8QRkRIFhQVuTY2QNu2ro/p73/le5MmUFDg%2Bvi7d7s2XtmyV76/%2By7k5bk2NsADD7g%2BZmQk7NsHgwbB/v2uj2%2BxuD6mOIQKPxEREXEL6vjZT4WfiIiIuAUVfvbT5g4RERERL6GOn4iIiLgFd%2Bz45eXlERMTw9atWykqKqJt27ZMmzaNwMDA6547depUkpKSbMYKCgp48MEHWbp0KcXFxTRs2BCLxYLJZLI%2BZ8eOHZS9utb1T6jjJyIiIuIkMTExpKen88knn7BlyxbS09OZN2/eDZ87Y8YM9u/fb/2Kj4%2BnQoUKTJ48GYATJ05gNpvZs2ePzfNKWvSBCj8RERFxE8XFjv9ypvz8fJKSkhg7dixBQUGEhIQwceJE1q5dS35%2B/h%2B%2BNjs7m4kTJ/Liiy8SHh4OwKFDh6hZsyZlypT5r3PSVK%2BIiIi4BWcUapmZmWRlZdmMhYWFUbly5RK9vqCggIyMjBsey8/Px2w2c%2B%2B991rHatSoQUFBAadOneK%2B%2B%2B676fvOmzeP2rVr07VrV%2BvYoUOHuHTpEr169eLMmTPUqFGDCRMm0KBBgxLlCir8RERExIutWrWKhIQEm7HRo0czZsyYEr3%2B4MGDDBw48IbHnnnmGQCbqdiAgAAAcnNzb/qep0%2BfZsOGDaxevdpm3N/fn7p16/LMM89QsWJF3n//fZ544gk2bNjA7bffXqJ8VfiJiIiIW3BGx69v375ER0fbjIWFhZX49VFRURw9evSGx3744QcWLlxIfn6%2BdTPH1SnecuXK3fQ9P/roIyIjI6/rCF5d63fVE088wdq1a/nyyy95/PHHS5SvCj8RERHxWpUrVy7xtO5fddddd%2BHr68uJEyeoV68eACdPnsTX15fq1avf9HVbtmxh6NCh140vWLCADh06cP/991vHCgsL8fPzK3FO2twhIiIibsHdNncEBATQsWNH5s2bR3Z2NtnZ2cybN4/OnTvjf/XWir9z7tw5Tp48SePGja87duzYMWbOnElWVhaFhYUkJCRw8eJF2rVrV%2BKcVPiJiIiIW3C3wg9g2rRpVK9enS5duvDwww9z2223MXXqVOvxTp06sXjxYuvjn3/%2BGYBbbrnluveKjY3ljjvuoFu3bkRFRbFnzx6WL19OUFBQifPRVK%2BIiIiIk5QrV46YmBhiYmJueHzTpk02j%2BvUqXPTNYNBQUHExsbalY8KPxEREXEL7njnjr8bTfWKiIiIeAl1/ERERMQtqONnPxV%2BIiIi4hZU%2BNlPU70iIiIiXkIdPxEREXEL6vjZTx0/ERERES%2Bhjp%2BIiIi4BXX87KfCT0RERNyCCj/7aapXRERExEuo4yciIiJuQR0/%2B6njJyIiIuIl1PETERERt6COn/28svCLjo4mKysLHx/b04%2BMjGTZsmUGZSUiIiJ/RIWf/byy8AOYPn06PXv2NDoNEREREZfRGr/fycjIYNy4cURHR1OvXj3atm3LmjVrrMdr1qzJK6%2B8QlRUFCNGjABg586d9O7dm0aNGtGpUyc2bNhgVPoiIiIeq7jY8V/exms7fjczZcoUgoKC2LRpE2XKlOG9994jJiaGjh07EhgYCEBqairbtm3DbDaTkpLC008/zdy5c2nbti0HDx5k5MiRBAcH06JFixLHzczMJCsry2YsLCSEymFhDj2/v71y5Vwbr2xZ2%2B8uduutro9ZubLtd5fzcfE/O1fjuTruVf7%2Bro9Zpoztd1crKnJtvKu/x0b8XgNERro%2BZkSE7XdX2r/f9TH/P28s1BzNZLFYLEYn4WrR0dGcPXsWX19fm/GvvvqKCxcuEBgYiL%2B/P%2Bnp6ezatYuXXnqJL774gmrVqlGzZk3mzp1L165dAXj55ZfJyclh/vz51veZP38%2Bx44dY/HixSXOKT4%2BnoSEBJux0aNGMWbsWDvOVERExMFMJjCodAgPd/x7Hj/u%2BPf8O/Pajt%2B0adNuuMbvhx9%2BYM6cOZw6dYrq1atz5513AlB8zX8zKl/TKjlz5gy7d%2B%2BmUaNG1rHLly9zxx13/KV8%2BvbtS3R0tM1YWEgIXL78l97HIUqXNiYuwL59ro1XtizUqgWHD0NenmtjA6991djlMStXhgEDIDERMjNdHp4JA7P%2B/EmO5OMDwcFw7pzrO1EAFy%2B6PmaZMlfayWfOQGGh6%2BO7%2Bpz9/a9UBMePQ0GBa2MDDBrk%2BpgREbByJfTrBykpro9vEHX87Oe1hd%2BNmM1mhg8fzrPPPku/fv0wmUx8//33163ZM5lM1l9XqVKFHj16MGPGDOtYZmYmf7WRWrlyZZuCEjCu%2BDKSER%2BScKXoMyD2mTMuD2mVmWlQfCOKr6txjYhtRCFyVWGhMfEN%2BE8UcOVcjYht4NQnKSnGxhe3o80d1zCbzRQUFODv74/JZCItLY25c%2Bdaj91I79692bhxI9u3b6e4uJhTp07x%2BOOP67IwIiIiDqbNHfZT4XeNsmXLMmvWLBYtWkRkZCQDBw6kWbNmhIaGcuzYsRu%2Bpl69esyfP5/58%2BfTuHFjHn/8caKjo5kwYYKLsxcREfFsKvzs55VTvVu3br3psW7dutGtWzebsaeeesr666NHj173mtatW9O6dWuH5SciIiLiDF5Z%2BImIiIj78cYOnaNpqldERETES6jjJyIiIm5BHT/7qfATERERt6DCz36a6hURERHxEur4iYiIiFtQx89%2B6viJiIiIeAl1/ERERMQtqONnPxV%2BIiIi4hZU%2BNlPU70iIiIiXkIdPxEREXEL6vjZTx0/ERERES%2Bhjp%2BIiIi4BXX87KfCT0RERNyCCj/7aapXRERExEuo4yciIiJuQR0/%2B6njJyIiIuIl1PETERERt6COn/1U%2BImIiIhbUOFnP031ioiIiHgJFX4iIiLiFoqLHf/lKvn5%2BfTt25e1a9f%2B4fMOHjzIo48%2BSmRkJNHR0axevdrm%2BLp162jXrh3169enZ8%2Be7N%2B//y/locJPRERExImOHz9O//79OXDgwB8%2B7/z58zz11FN0796db775hpkzZxIbG8t3330HQHJyMjExMbz66qt88803dO3alaeffpr8/PwS56LCT0RERNyCO3b8du3axaBBg%2BjRowfVqlX7w%2Bdu2bKFoKAg%2Bvfvj4%2BPD02bNqVLly68//77AKxevZpOnTrRsGFDfH19GTx4MMHBwWzevLnE%2BWhzh4iIiLgFZxRqmZmZZGVl2YyFhYVRuXLlEr2%2BoKCAjIyMGx4LCwsjIiKCL774Aj8/P5YvX/6H73X8%2BHHuvfdem7F77rmHNWvWAHDixAl69ep13fGUlJQS5Qoq/P7eSpd2ecjMzExWrVpF3759S/yH3qHatHFpuMzMTFbFxxt2vvNde7rAlXOOj1/FgAEG/Yyp6tJoNj/jqq6NDYABMW3O%2Ba67XB7f1Yz%2Be4zF4vKQ1nP%2B978N%2BntsDGf8VsfHryIhIcFmbPTo0YwZM6ZErz948CADBw684bFFixbx0EMPlTiX3NxcAgICbMb8/f3Jy8sr0fGSUOEnNrKyskhISCA6Otor/jHxtvMF7ztnbztf8L5z9rbzBe88Z2fp27cv0dHRNmNhYWElfn1UVBRHjx51SC4BAQFcuHDBZqygoIDAwEDr8YKCguuOBwcHlziGCj8RERHxWpUrV/7bFM/33nsvO3bssBk7ceIE4eHhAISHh3P8%2BPHrjrds2bLEMbS5Q0RERORvoF27dvz666%2B88847mM1mdu/eTVJSknVdX%2B/evUlKSmL37t2YzWbeeecdzp49S7t27UocQ4WfiIiIiEE6derE4sWLAQgODmbZsmX8%2B9//JioqiilTpjBlyhQeeOABAJo2bcq0adN4%2BeWXadKkCZs2beKtt94iKCioxPE01Ss2wsLCGD169F9a3%2BDOvO18wfvO2dvOF7zvnL3tfME7z9kTbN269bqxTZs22TyuU6cO//rXv276Ht26daNbt27/dQ4mi8WA7UgiIiIi4nKa6hURERHxEir8RERERLyECj8RERERL6HCT0RERMRLqPATERER8RIq/ERERES8hAo/ERERES%2Bhwk9ERETES6jwExEREfESKvxExGNlZ2cbnYKIyN%2BKCj8R8ShFRUUsWLCAhg0bEh0dzenTp%2BnVqxeZmZlGp%2BZU58%2BfZ/369SxZsoSNGzdy8eJFo1MSkb8h3atXvMY333zzp89p3LixCzIxxuXLlyldujQAX375JcHBwdStW9fgrBxvwYIF7N69mzFjxjB%2B/Hi%2B/PJLJk2ahI%2BPDwsXLjQ6PafYu3cvTz/9NAEBAVSpUoW0tDQsFgvLly8nPDzc6PRE5G9EhZ8Xi4iIwGQy/eFzjhw54qJsnC8iIgLA5pwrVqzIhQsXKC4uJigoiF27dhmVnlNt3bqVKVOmsHPnTl5//XUWL16MyWTixRdfpE%2BfPkan51DR0dF88MEH3HLLLTRp0oQ9e/aQk5NDu3btSE5ONjo9p%2BjVqxft2rVjxIgRAFgsFhISEtizZw%2BJiYkGZ%2Bc8Bw8eJDU1lcuXL9uMd%2B/e3aCMnG/Hjh0kJiaSmZnJm2%2B%2BybJly5gwYQI%2BPj5GpyZuQn9SvNh7770HXPmH5KuvvmL06NHccccdpKens2jRIpo1a2Zwho6VkpICwNKlSzl27BhTpkyhfPny5OXl8eqrr1KxYkWDM3SeN954g3HjxlFcXMyKFSuIj48nJCSE8ePHe1zhl5eXR6VKlYArBRCAv78/pUp57sqWH3/8kWHDhlkfm0wmRowYwTvvvGNcUk62YMEClixZQmhoKL6%2BvtZxk8nksYVfUlISsbGxPProo9YZjK1bt2IymfjnP/9pcHbiNizi9R566CHLL7/8YjOWmZlpadWqlTEJOVnTpk0t%2Bfn5NmMFBQWWJk2aGJSR8109t8OHD1vq169vMZvNFovFYqlfv76RaTnF8OHDLfPnz7dYLBZL48aNLRaLxfL2229bnnzySSPTcqoePXpY9uzZYzN2%2BPBhS58%2BfQzKyPkeeOABy%2B7du41Ow6U6d%2B5s2b9/v8VisVgaNWpksVgslp9%2B%2BsnSokULI9MSN6OOn5CdnU2FChVsxvz8/Lhw4YJBGTlXcXExZ8%2Be5dZbb7WO/fzzz9b1b54oICCAs2fPsnXrVho2bIiPjw8pKSkEBwcbnZrDvfjiiwwaNIh169aRm5vLI488Qm5uLsuXLzc6NaeJiopixIgR9OrVizvvvJPMzExWr15NkyZNSEhIsD5v9OjRBmbpWKVLlyYqKsroNFzql19%2BoV69esD/LVm58847ycvLMzItcTMq/ITGjRvz3HPPMWnSJKpUqcLp06d59dVXadWqldGpOUW3bt144oknGDZsGFWrVuX06dO8/fbbPPbYY0an5jS9evWie/fu5OTkEBcXx/fff8%2BwYcMYOnSo0ak53O23386mTZv44osvSEtLo0qVKrRu3Zpy5coZnZrTfP/999x///0cOXLEui63Ro0anD17lrNnzwL86Xped9OmTRs2btxI586djU7FZapXr87nn3/OQw89ZB3buXMnd955p4FZibvR5g4hKyuLcePGsXfvXuuHQ7NmzZg/f/51nUBPUFRUxKJFi9iwYQMZGRlUrVqVRx99lCeffNLjPhyvlZycjJ%2BfH/Xr1yc9PZ1Dhw7Rvn17o9NyuLS0tBuO%2B/r6UrFiRcqUKePijMSRBgwYgMlkIjc3lyNHjnDPPfcQFBRk85yr65c9zc6dOxk5ciRt27bls88%2Bo0ePHmzcuJHXXnvNY/%2BjLo6nwk%2Bszpw5Q2ZmJlWqVKFq1apGpyMO9uuvvxIaGkphYSFr1qwhODiYjh07Gp2Ww9WqVYvi4uIbHitVqhQPPvggs2fPtm4AcXcXL14kKyuLu%2B66C4CPPvqII0eO0K5dO4%2BcCr126vpmPGlK%2B/dSUlJYtWoVZ86coUqVKvTu3dsjL8skzqPCT7zSjh07WLFiBRkZGV5xSYTVq1czc%2BZMDhw4wMyZM9m8eTMmk4l%2B/foxcuRIo9NzqBUrVvDFF1/wwgsvcPvtt/Pzzz8zZ84cateuTfv27XnjjTfw8fFh7ty5Rqdqt5MnTzJgwADatGnDzJkzeeedd3jttddo06YNycnJvPbaazRv3tzoNJ3m5MmT3HLLLZQrV479%2B/dToUIFatSoYXRaTvP0008zd%2B5cj162IC5g5M4SMVbNmjUtERERf/jliTZs2GBp2rSpZf78%2BZYGDRpYMjMzLe3bt7fMnj3b6NScpmvXrpbt27dbioqKLA0aNLDs3bvXkpqa6pE7tx966CHLuXPnbMZ%2B%2B%2B03S9u2bS0Wi8Vy4cIFj9nBPWbMGMvMmTMtRUVFFovFYmnRooVl6dKlFovFYtm2bZvl8ccfNzI9p9q8ebOldu3alkOHDlksFotl2bJllsjISMu2bdsMzsx5oqKiLJcuXTI6DXFzntnekBLx1HUwf2bJkiW8/vrr1K9fn5UrVxIWFsabb74MKlaAAAATHklEQVTJwIEDPfZaWOnp6TRr1ox9%2B/bh4%2BNDgwYNAMjJyTE4M8c7d%2B7cdTu0TSaTdZNDQEDATaeC3c23337Lli1bKF26NKdOnSIrK4t27doBV3b6TpgwweAMnSchIYHXX3%2Bd2rVrAzBkyBDuuece5s6d67Hr3Tp37szYsWPp0qULYWFhNmuSPfmuQ%2BJYKvy8WJMmTQB4%2B%2B236devH2XLljU4I9fwxksiVKxYkf/85z988skn1p/77t27CQsLMzgzx2vRogUTJkzgxRdfpFq1aqSlpTFnzhyaNWtGYWEhixYtolatWkan6RAFBQXWab%2BDBw9SqVIlbr/9duDKesbf39HCk6Snp9OiRQubsebNmzN%2B/HiDMnK%2BFStWALBt2zabcZPJ5FF3WRLnUuEnLFmyhCFDhhidhst44yURhgwZQpcuXQBITExk7969DB8%2BnGnTphmcmeNNmzaNCRMm0KFDB2th37p1a2bOnMm3337Ltm3bmD9/vsFZOkZISAjp6elUrVqV3bt323R9UlJSqFy5soHZOdett97K119/bVP87dq1i2rVqhmYlXNdvfuQiD20uUOYMGEC4eHh9OzZ06M/KK7y1ksinD59Gh8fH6pWrUp2djZpaWnWaTJPlJGRwS%2B//ILFYmHt2rVs2LCBAwcOGJ2WQ7322mscOHCAFi1akJCQQFxcHK1bt%2BbEiRNMnTqVyMhIJk2aZHSaTpGUlMSLL75I%2B/btufXWW0lLS%2BPTTz9l9uzZdOjQwej0nCY/P5/z589blyuYzWaOHTtmneIX%2BTMq/ITWrVvzyy%2B/3PAadp46feCNl0Twtg%2BMb7/9lqVLl/Lll18SHh5Onz596N%2B/v9FpOVRhYSExMTHs27ePTp06WXdo161bl9q1a7NkyRKP3gGanJzM%2BvXrycrKomrVqvTo0cO6ftUTffTRR8TExHDp0iWb8ZCQELZv325QVuJuVPgJe/bsuemxq%2BvBPMknn3xC27ZtPfbSLTfiLR8YxcXF/Pvf/2b58uUcP36coqIi3njjjevWgnm6kydPevRlTcA7L23Srl07%2BvfvT2BgIN988w2DBg1i7ty5NGvWjCeffNLo9MRNqPATq/Pnz3P69Gnuv/9%2BioqKPPYOBy1btsRsNtO9e3d69%2B7t8R%2BQ4B0fGO%2B%2B%2By7vvfcexcXF/OMf/6BPnz48/PDDfPzxx9xyyy1Gp%2Bd0Fy9e5MsvvyQjI4PbbruNli1b4u/vb3RaTvPAAw/w1Vdfeey/UzdSv3599u/fz5kzZ5g4cSL/%2Bte/SEtLY/DgwWzZssXo9MRNeE/LQ24qNzeXqVOnsmnTJvz9/Vm7di1Dhgxh%2BfLl3H333Uan53Dbtm3j66%2B/Zv369fTs2ZP77ruP3r1788gjj3jszuasrCwGDRrEmTNn%2BOijj6hVqxazZs1i8ODBHlP4xcbG0q9fPyZPnuxVxQDAoUOHGDZsGP7%2B/lSpUoUzZ85QpkwZ3n77bY/8OwzeeWmTkJAQzGYzVatW5aeffgKgWrVq1ksViZSECj9hzpw55OXl8b//%2B7/06dOH22%2B/3XongKVLlxqdnsOVKlWKVq1a0apVKy5cuMDmzZt5/fXXmTVrFvv27TM6Pafwhg%2BMl156iZUrV9KqVSv69OlDv379PPrey9eKjY1lyJAhjBgxAgCLxUJcXBwzZszgnXfeMTY5J/HGS5vUrVuXqVOn8tJLL1G9enU%2B%2BOAD/P39r7tXscgfUeEnfPHFFyQlJVGxYkVMJhO%2Bvr5MnjyZli1bGp2aU50%2BfZqPP/6YpKQkzGYzAwYMMDolp/GGD4z%2B/fvTv39/du3axYoVK2jXrh2XL19m165ddOnS5bqLOnuSEydOkJiYaH1sMpkYOXIkTZs2NTAr5/LGS5s8//zzTJkyhdzcXCZNmsSIESMoKCggNjbW6NTEjajwE4qLi61TY1eXfF475mlWr17NunXr%2BO6772jevDmTJk2iTZs2Hl0YeNMHRtOmTWnatClnzpxh5cqVvPrqq8yZM4euXbsyefJko9Nzipo1a3LgwAEaNmxoHTty5Ij1Ys6eylt2qj/xxBMsXbqUypUrs2TJEgoKCmjUqBG7d%2B/GbDYTEBBgdIriRrS5Q5g4cSK%2Bvr5MnTqVVq1asWfPHmbNmsWvv/7qMRe6vVbbtm3p1asXvXr18opF/zdSVFTkNR8YhYWFbNiwgZUrV7J27Vqj03GohIQEAFJTU9m6dSu9e/fmtttuIzMzkzVr1tC%2BfXtefvllY5N0Em/ZqQ7QoEEDm2UoTZo0%2BcOrMYj8ERV%2BwtmzZ3n66af54YcfuHz5Mv7%2B/lSvXp3Fixd7ZGFksVi8Zu3X%2BvXr//Q53bt3d0Em4gx/tjzBZDJ57D25vWGn%2BlW/L/waN27MN998Y2BG4s5U%2BAlwpRg6dOiQ9YLGdevW9bipz6eeeoolS5YwYMCAmxZ%2BnvYhGR0d/YfHTSYTn3/%2BuYuyEXEcb7q0iTp%2B4kha4yc2/3MMDQ2lqKiIffv24evrS6VKlbjjjjsMzM5xrq5/ioqKMjgT19m6desNxy9duoSfn5%2BLsxFH27hxI507d/7Dzq6ndnS9Yae6iDOo8BMmT55MWloapUqVIjg4mHPnzlFcXEypUqW4fPkyd999N2%2B%2B%2BabbLxQfPnw4cOUWdZ58j9obSUtL49lnn%2BWll16iVq1a/M///A8HDhwgPj6e0NBQo9OT/9LixYvp3LkzcXFxNzxuMpk8tvCrU6eOx%2B9Uv6qoqMimuDebzdcV%2B576cxbH01SvsHDhQtLS0pg6dSqBgYHk5eURGxtLtWrVGDhwIAsXLiQ1NZXFixcbnapD1KtXj%2BrVq/Poo4/StWtXKlSoYHRKTjd8%2BHBCQkJ44YUXKFeuHNnZ2SxYsIDz58/ftGgQ91BcXMxvv/1GpUqVANi1axcpKSm0atXKYy/eDJCZmcmUKVN45ZVXSE1Ntdmp3qVLF6PTcygt2RBHUuEntGnThs2bN9vs8MzPz6djx45s27aNS5cu0aJFC49ZU3LhwgWSkpJYv349R48e5aGHHqJ3794efc2zJk2asGPHDnx9fa1jly5domXLliQnJxuYmdgjIyODoUOHUrduXWJjY0lKSuK5554jIiKC1NRUli9fTp06dYxO0%2BESEhI4fPgwzZs3p3///oB37VQXsUcpoxMQ4%2BXl5ZGTk2MzduHCBS5evGh97Em7YMuXL0%2B/fv348MMPWbt2LbfddhvPP/%2B8x13761o%2BPj5kZ2fbjJ0/f96j7%2BXqDRYsWEDNmjWZOHEiAPHx8Tz55JOsXbuWqVOnEh8fb3CGjjdnzhxWrlyJr68vcXFxLFmyBLjyZ1xFn8ifU%2BEnPPzww4waNYqdO3dy6tQpdu7cydixY2nfvj0XL15k2rRpNGrUyOg0HS4vL4/vvvuOQ4cOcf78eerWrWt0Sk7z8MMPM3bsWHbt2sWpU6fYtWsXzzzzDB06dDA6NbHDjh07mDJlCiEhIaSlpZGamkrXrl2BK9erPHDggMEZOt7GjRt59913iYuLIy4ujqSkJKNTEnEr2twhvPDCC8ycOZNRo0aRn5%2BPv78/vXv3ZsKECRw%2BfJicnByPugjszp07WbduHZ999hm33XYbvXv3ZsGCBVSsWNHo1Jxm0qRJzJgxg%2BHDh1NYWEiZMmXo3r0748ePNzo1scPFixeta/sOHjxIhQoVqFGjBgB%2Bfn6YzWYj03OKCxcuEB4eDlzZqZ%2BRkWFwRiLuRYWf4Ofnx4wZM5g6dSq//fYbISEh1qndRo0aeVy3b9SoUXTq1Inly5dTv359o9NxumvXQ02fPp2cnBybn7G4r4oVK5KdnU2lSpXYs2cPDRo0sB778ccfCQ4ONjA75yhV6v8mqnx89BEm8lfpb40A8N133/HTTz/x%2B70%2BnniJgEceeYTJkydTrlw5o1Nxujlz5rB%2B/XoaNWpEXFwcubm5PPXUU0anJQ7Spk0bYmJiaNeuHUlJSUybNg2AnJwcFi5cSIsWLQzO0PG0H1HEPtrVK8yfP5%2B33nqLsLAwm/9Be%2BolAqKioti5c6fH3ZnkRlq2bMnSpUsJDw8nOTmZV155RWuiPEhOTg7jxo1j3759dOrUiZkzZwIQGRlJWFgYK1eu9LjrNNatW5cZM2ZYH0%2BfPt1a8F7lif9hFXEUFX5C69atmT59Oq1atTI6FZeYPXs2ubm59OzZk7CwMJspz2rVqhmYmeNFRkayf/9%2B4MrlLh588EGPuSyP3Nz27dtp3LixR96dRde0E7GPCj%2BhcePG7Nmzx2vWfEVERFh/ffWcLRYLJpOJI0eOGJWWUzRs2JC9e/daH%2BsenyIi3k1r/ITWrVuTlJRkvQyEp/OmboD%2BXyciItdS4SdcunSJyZMns3jx4uvWA7333nsGZeU8t956q9EpuIzu8SkiItfSVK%2BQkJBw02OjR492YSauERERcdNpbU%2Bb6tV6KBERuZYKP/E6v1/jlp2dTWJiIt26daNPnz4GZSUiIuJ8KvwEgA8//JDExEQyMzNZt24dr776KrGxsQQGBhqdmktkZWUxePBgNm3aZHQqIiIiTqN79QrvvPMOS5cuZcCAAVy%2BfJnAwEAyMjKIjY01OjWXqVChgm79JCIiHk8dP6FDhw68/vrr1KhRw3q5j8zMTHr06MGOHTuMTs/hfr%2B5wWw28/nnn5Obm0tiYqJBWYmIiDifdvUK586d46677gL%2B7/IfISEhFBUVGZmW08TFxdk8Ll26NDVq1Lju6v8iIiKeRoWfEBERwapVq/jHP/5h3e26efNmwsPDDc7M8YqLi1mzZg2VKlUCYNeuXaSkpNCqVSvuvvtug7MTERFxLk31CocPH2bw4MHUqFGD77//nqZNm3LgwAHefvtt6tWrZ3R6DpORkcHQoUOpW7cusbGxJCUl8dxzzxEREUFqairLly%2BnTp06RqcpIiLiNCr8BLhSFCUlJXHmzBmqVKlCly5dPO6%2BtZMnT6awsJAXX3yRkJAQ2rdvT8eOHRk/fjwbNmxg48aNLFmyxOg0RUREnEaFnwBw%2BfJlSpcujcVi4auvviI4OJi6desanZZDtWjRgo8//phKlSqRlpZGdHQ0mzZtokaNGuTm5tKmTRvdx1ZERDyaLucibN26lRYtWgDwxhtvMGbMGAYMGMCHH35ocGaOdfHiRevavoMHD1KhQgVq1KgBgJ%2BfH2az2cj0REREnE6Fn/DGG28wbtw4iouLSUxMJD4%2Bnvfff5%2B33nrL6NQcqmLFimRnZwNX7t7RoEED67Eff/yR4OBgo1ITERFxCRV%2BQmpqKn369CElJYWCggKaNWtG7dq1%2BfXXX41OzaHatGlDTEwMmzdvJikpiU6dOgGQk5PDwoULrV1PERERT6XCTwgICODs2bNs3bqVhg0b4uPjQ0pKisd1wMaPH8/58%2Bd54YUX6NChA126dAGgVatWHD9%2BnDFjxhicoYiIiHNpc4cQHx/Phx9%2BSE5ODnFxcYSEhDBs2DCGDh3KU089ZXR6Trd9%2B3YaN26Mn5%2Bf0amIiIg4lQo/ASA5ORk/Pz/q169Peno6hw4don379kanJSIiIg6kO3cIADVq1CA0NJTCwkK%2B%2BOILj5vmFRERERV%2BAqxevZqZM2dy4MAB5s6dy%2BbNmzGZTPz000%2BMHDnS6PRERETEQbS5Q1ixYgWLFi3i8uXLrF27lvj4eD744AOPu46fiIiIt1PHT0hPT6dZs2bs27cPHx8f6/XtcnJyDM5MREREHEkdP6FixYr85z//4ZNPPqFJkyYA7N69m7CwMIMzExEREUdSx08YMmSI9Zp2iYmJ7N27l%2BHDhzNt2jSDMxMRERFH0uVcBIDTp0/j4%2BND1apVyc7OJi0tjdq1axudloiIiDiQpnoFgNDQUEwmE2lpaRQUFFC%2BfHk%2B/fRTo9MSERERB1LHT/joo4%2BIiYnh0qVLNuMhISFs377doKxERETE0bTGT1i8eDHjxo0jMDCQb775hkGDBjF37lyaNWtmdGoiIiLiQJrqFbKyshg0aBBNmzYlNTWVWrVqMWvWLFavXm10aiIiIuJAKvyEkJAQzGYzVatW5aeffgKgWrVqnD171uDMRERExJFU%2BAl16tRh6tSpFBQUUL16dT744APWrVtHUFCQ0amJiIiIA2mNn/DCCy8wZcoUcnNzmTRpEiNGjKCgoIDY2FijUxMREREH0q5eL5eQkMDhw4dp3rw5/fv3B6CoqAiz2UxAQIDB2YmIiIgjaarXi82ZM4eVK1fi6%2BtLXFwcS5YsAcDHx0dFn4iIiAdSx8%2BLtWzZkqVLlxIeHk5ycjKvvPIKSUlJRqclIiIiTqKOnxe7cOEC4eHhADRs2JCMjAyDMxIRERFnUuHnxUqV%2Br8fv4%2BP9vmIiIh4OhV%2BXkyz/CIiIt5FbR4vVlRUxPr1662PzWazzWOA7t27uzotERERcRJt7vBi0dHRf3jcZDLx%2BeefuygbERERcTYVfiIiIiJeQmv8RERERLyECj8RERERL6HCT0RERMRLqPATERER8RIq/ERERES8hAo/ERERES%2Bhwk9ERETES6jwExEREfES/w%2BkEre3n0v2ZgAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n",
       "    <img 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y8sugGzjcye9zBv2FDOHIEmjSBn36yeXhTiXPdVjIvz/YxDQaoWhXy88Eed/H0KMm2bUCDATw8IDfXLgd8scjrj1eqZAYD%2BPjApUv2%2BR5X83Wu93FxiX0%2BbcVohMs%2B5dCmqlSxT9zr8XcxfuFCFi1aZNE2ZswYYmJiKrR9QEBAhdbLzs7Gw8PDos3d3Z2cnJwKLa8sSvzEkp9f6Tvaz8/ePbEZg8E%2Bfxztyak%2BFcxg%2BN/DSb7RTnjITnawpZzqfXwdRUdHExkZadFW0WTuz/Dw8CDvN//t5%2BXlmT/z/I%2BWVxYlfiIiIuIYjJV/hlpgYCCBgYGVvt/fCgkJ4eDBgxZtR48e5dZbbwWgSZMmHDlyhIiICAAKCwtJTk6%2BYnrYWjrHT0REROQ669OnD3v27GHbtm0UFRWxbds29uzZQ9%2B%2BfQG4//77efvtt0lKSiI/P58FCxZQo0YNwsPDK7UfqviJiIiIY7gOFb/rKSwsjBkzZtCnTx%2BCg4NZvHgx8%2BfP59lnn6V27drExcXRsGFDAKKiorh06RKjR4/m3LlztGzZkmXLluHq6lqpfTKYKvsGMVJ57HECR1gYJCRA69awf7/Nw9vj4g57nhpkr4s73N1LYzvFxR1G4/8u7rDDmfD2uLjDaPzfxR32OPnfLhd32PGNbK%2BLO6pUgeJiu4S238UdVatW/j7z8yt/nzcwx0qdRUREROSaaapXREREHIODTfXeiDSCIiIiIk5CFT8RERFxDKr4WU2Jn4iIiDgGJX5W0wiKiIiIOAlV/ERERMQxqOJnNY2giIiIiJNQxU9EREQcgyp%2BVlPiJyIiIo5BiZ/VNIIiIiIiTkIVPxEREXEMqvhZTSMoIiIi4iRU8RMRERHHoIqf1ZT4iYiIiGNQ4mc1jaCIiIiIk1DFT0RERByDKn5W0wiKiIiIOAlV/ERERMQxqOJnNSV%2BIiIi4hiU%2BFntT41gZGQkLVu2JCwsjLCwMEJDQ%2BnQoQPz5s2jpKTkevXxhrJ7926aNm16zctFRERE7OVPV/xmzJhB//79za8PHTrE0KFD8fDwIDY2tlI7JyIiImKmip/VrB7Bpk2b0rZtW3744QdSU1MZN24ckZGRtGrViq5du7J%2B/XrzuuvWraNbt26Eh4fTu3dv3nvvPfOyuLg4OnfuTLt27bj//vvZsWOHednBgwcZMmQIbdu2pXv37qxatQqTyWTeLjY2lokTJxIeHk6nTp1YsGCBedu8vDymT59Ou3bt6Ny5M6%2B99hqRkZHs3r0bgLNnzzJx4kTuuusuOnTowLRp08jKygJKq3edO3dmwoQJhIeH8%2Babb15x/GlpaTzxxBO0bt2arl278vXXX1s7pCIiIiLXhVXn%2BBUWFpKQkMA333xDTEwMU6dOxc/Pj61bt%2BLm5sbq1auZOXMmPXv25Ny5c8yZM4fNmzfTqFEjdu7cyejRo%2BncuTPHjx8nPj6ejRs3EhAQQHx8PM8%2B%2ByydOnXi3LlzPPzww4wfP5633nqLEydOMGrUKNzd3fn73/8OwMcff8zcuXOZN28eX331FSNGjKBr166EhoYye/Zsvv/%2BezZv3oyvry8zZszg1KlTAJSUlDBq1CgaNGjA9u3bKSws5JlnnmHatGm88sorAPzyyy80atSIuXPnkp%2Bfz8GDBy3GYPz48VSvXp0vv/ySS5cuMXLkyGsay7S0NNLT0y3aAho2JNDP75r2d82aNbN8luvKYLBfTHvEBmz/H/vlB2yHaoE9ChRlMVUckb8c/VBb7ZqmemfPnm1%2BHRQUxCOPPMLgwYPp3r07Xl5euLq6cvr0aby8vMjLy%2BPixYtUqVIFk8nEO%2B%2B8Q48ePbjjjjtITEzEaDRy6tQpLl68yLvvvktERAQPPPAA0dHRGAwGtmzZQnBwMIMGDQKgcePGPPbYY7z99tvmxK9Bgwb069cPgM6dOxMQEEBycjItWrRgy5YtxMXFcfPNNwMwbdo0PvzwQwC%2B//57Dh48yD/%2B8Q%2B8vLwAmDx5Mn/729947rnnzMcYFRWFq6srrq6uFmNx6tQpvv32W7Zv3463tzfe3t6MGTOG0aNH/9lhJT4%2BnkWLFlm0jRk7lpixY//0virFunV2CWuvXMReSZC7u33iAlStaq/IHvYJa6fB9rFL1FK//lqzAzu9oez0Rq5SxS5h7Ra7uNj2Mc2U%2BFntTyd%2B06dPtzjH73IpKSm89NJLJCcn06BBA%2BrXrw%2BUVtbq1KnDmjVrWLFiBU888QTFxcX079%2BfSZMmERYWRlxcnHm5u7s7Q4YMYeTIkZw6dYqDBw8SHh5ujlNSUkKVy37aAwICLPrh6upKSUkJFy5cIDc3l9q1a5uXeXt7U716dQBOnjxJcXExnTt3ttjezc2NlJQU8%2BvAwMByjzc1NRWAWrVqmdvq1at39cH7HdHR0URGRlq0BfTuDf/85zXt75o1a1aa9A0cCElJto0NmPYl2DymwQC/njlgc/n5to9pMJQmffn59jlud1OubQMaDKVJX16eXQ74UpHtE12jsTTpy84Ge1x35%2BNthx8sO76Ri0vsl3DaNQkTh1Rpt3MpLCxkxIgRPPnkkwwcOBCDwcD333/Pli1bAMjIyKC4uJjFixdTUlJCQkICsbGxNGzYkIiICPz9/Vm5ciUFBQXs2rWLMWPG0KJFC4KCgmjfvj0rV640xzp//jzZ2dl/2Cd/f3/c3d05ffo0jRo1AiAnJ4fz588DpdVKd3d3du/ebU4kCwoKSElJoX79%2Buzbtw8Aw1X%2BiwwKCgJKE97g4GCgdGr4WgQGBl6ZYP700zXtq1IkJcH%2B/faL7yTslXCWxbZLfFtnImUVApPJLlmQPW94UFJi3/gilU4VP6tV2ggWFhaSl5eHu7s7BoOB06dP8/LLL5uXnT59mkcffZRdu3ZhNBqpWbMmANWrV%2Be7775j2LBhJCUl4ebmhr%2B/v3lZ7969SUxMZMuWLRQVFZkvppg7d%2B4fH5zRSFRUFHFxcaSmppKbm8ucOXMo/vVfpNtuu4369eszd%2B5csrOzycvLY/bs2QwdOtS8zu%2BpVasWHTp0YM6cOVy8eJH09PQrpmtFREREbhSVVvHz9PRk9uzZvP7667z44ov4%2B/szYMAAjh49yuHDh%2BnRowfTpk3j%2BeefJy0tDR8fHwYOHEjPnj0xGAwkJyczcuRIzp8/j7%2B/P1OmTKFVq1YArFixgvnz5/Piiy9SpUoVunTpwrPPPluhfk2YMIGZM2dyzz334OXlRXR0NEajEVdXV1xcXFi2bBnz5s2je/fu5Ofnc9ttt/GPf/yDqhU8AWrBggXMmDGDiIgIvL296d%2B/PwcOHLjmcRQREZGrUMXPagaTyZ6TTdff3r17adq0Kb6%2BvgBkZWXRpk0btm/fToMGDezbuT9ijxOVw8IgIQFat7bLVK%2BpxPY/jvY8xy8vz/Yx7XzKGx4lf3yaRqUyGsHDA3Jz7TLvebHI9ldYGI3g4wOXLtlnqrear87xswV7nuNntwtaWrSo/H3%2B5m4df3V/%2BdT5rbfeYtasWeTl5ZGfn8/ChQtp2LDhjZ/0iYiIiFSyv3zi9/zzz3Pp0iU6d%2B7MXXfdxYkTJ8q9EbOIiIjc4IzGyn84mUo7x%2B9GVbNmTZYsWWLvboiIiIi1nDBRq2waQREREREn8Zev%2BImIiMhfhCp%2BVtMIioiIiDgJVfxERETEMajiZzUlfiIiIuIYlPhZTSMoIiIi4iRU8RMRERHHoIqf1TSCIiIiIk5CFT8RERFxDKr4WU2Jn4iIiDgGJX5WU%2BInIiIico0yMjJ47rnn2LNnD1WqVKFPnz5MnjwZFxfLFGvYsGHs27fPoi0nJ4fo6GheeOEFzp49y1133YWnp6d5efXq1fn0008rtb9K/ERERMQx3IAVv3HjxlGzZk127tzJ2bNnGTlyJKtWrWLYsGEW661YscLi9fr161m0aBFjxowB4LvvvqN27dqVnuj91o03giIiIiIO4MSJE%2BzZs4dJkybh4eFB3bp1GTVqFGvXrv3d7Y4fP87MmTOZP38%2BgYGBQGnid%2Butt173PqviJyIiIo7hOlT80tLSSE9Pt2gLCAgwJ2S/58iRI/j5%2BVGzZk1zW3BwMKdPnyYzMxNfX99yt5sxYwb9%2BvUjPDzc3Pbdd99x8eJF7r33Xs6ePUvLli2ZPHkyjRs3vsYjK58SPxEREXEM1yHxi4%2BPZ9GiRRZtY8aMISYm5g%2B3zc7OxsPDw6Kt7HVOTk65id%2B3337LgQMHmD9/vkW7r68vjRs3Zvjw4bi5ufH666/zyCOPsG3bNnx8fP7sYV2VEj8RERFxWtHR0URGRlq0BQQEVGhbT09PcnNzLdrKXnt5eZW7TXx8PD179rwixoIFCyxeP/PMM2zYsIFvv/2WiIiICvWnIpT4iYiIiGO4DhW/wMDACk3rlqdJkyZcuHCBs2fPUqNGDQCOHTtGUFBQuVW6oqIiduzYweLFiy3as7KyWLx4MYMHD6Z27doAFBcXU1RUhLu7%2BzX17Wp0cYeIiIjINWjQoAFt2rRh9uzZZGVlkZKSwpIlS4iKiip3/UOHDpGfn0/r1q0t2r29vfnvf//LvHnzuHTpEtnZ2cycOZM6depYnAdYGZT4iYiIiGMwGiv/YaWFCxdSVFRE165dGTBgAB07dmTUqFEAhIWFsWXLFvO6KSkpVKtWjapVq16xnyVLllBSUkK3bt3o2LEj6enpLF%2B%2BHFdXV6v7eDmDyWQyVeoepfIYDLaPGRYGCQnQujXs32/z8KYS2/84Ggxgr3dBXp7tYxoM4O5eGtsex%2B1Rkm3bgEYjeHhAbi6UlNg2NnCxqPzzfK4noxF8fODSJbscMtV87fCDZcc3cnGJHX5XA1WqQHGxXUJTpYp94tKjR%2BXvc/v2yt/nDUzn%2BN3A7JEEARgA074E%2B8Q22vgX6K%2BJrqGNfRLdLe/Y/ntcvTp07w5ffgnnz9s8PPfcY9tEyAh4AdklHvZJgvJSbR/UxQXwx6cgA4qKbB//bJZt47m5Qd26cPIkFBTYNjZQJTHR5jHx84OuXany%2BQ64cMH28e%2B/3/YxpVIo8RMRERHHcAN%2Bcoej0QiKiIiIOAlV/ERERMQxqOJnNSV%2BIiIi4hiU%2BFlNIygiIiLiJFTxExEREcegip/VNIIiIiIiTkIVPxEREXEMqvhZTYmfiIiIOAYlflbTCIqIiIg4CVX8RERExDGo4mc1jaCIiIiIk1DFT0RERByDKn5WU%2BInIiIijkGJn9U0giIiIiJOQhU/ERERcQyq%2BFlNIygiIiLiJFTxExEREcegip/VlPiJiIiIY1DiZzWNoIiIiIiTUMVPREREHIMqflZT4iciIiKOQYmf1TSCIiIiIk5CFT8RERFxDKr4WU0jKCIiIuIkVPETERERx6CKn9WU%2BImIiIhjUOJntRtqBC9evMjzzz9P586dCQ0NpUOHDkyePJlffvml0mO98cYbDBs2rNL3C9C0aVN27959XfYtIiIicq1uqIrf%2BPHj8fHxYf369QQEBHD27FlmzZrFI488wgcffICLS%2BV194knnqi0fYmIiIgNqOJntRtqBPft28fdd99NQEAAADVq1GDKlCm0atWKzMxMIiMj2bhxo3n93bt307RpUwBOnjxJ06ZNmTt3Lm3btmXKlCmEhYXx1VdfmdfPzMzktttu4//%2B7/%2BIi4tjyJAhlJSUEBkZSXx8vHm94uJiOnbsyEcffQTAf//7X6KioggPD6dXr15s2bLFvG5hYSFz5syhffv23H777axYseK6jpGIiIjItbqhKn69evVi%2BvTpfPvtt7Rr145WrVpRu3Zt5s6dW%2BF9ZGdn8/XXX5OXlwfApk2b6NChAwAffvgh9evX57bbbuOLL74AwGg0cv/997Np0yaio6MB%2BOqrrygoKKBr164kJSUxcuRIXn75Zbp27cqBAwcYNWoU1atXp2PHjixZsoTPP/%2Bc9evX4%2B/vz/PPP39Nx56WlkZ6erpFW40aAQQGBl7T/hxWWJht4zVrZvlsY9Wr2z6mj4/ls63Z%2Bh/2snh2KxRU4kxFhVWpYvlsa25uto3n6mr5bGt%2BfraPac838oULto9ZRhU/q91Qid%2BLL75I%2B/bt2bZtG9OmTePSpUvUq1ePmJgY%2BvTpU6F99OvXDzc3N9zc3HjggQd45JFHyMrKwtvbm02bNhEVFXXFNlFRUSxevJiff/6ZevXqsWnTJvr27YubmxvvvPMOXbt2pXv37gC0bt2aAQMGsHbtWjp27MjmzZt54oknqFu3LgBTp061qAhWVHx8PIsWLbJoGz16DLGxMX96X5XBYLBLWEhIsE/cdevsEra7XaKWuuMOOwa3Aw8POwX28rdTYOyTkAD42%2BmYg4LsE/fX39tpsmUAACAASURBVP920a6d7WNu2GD7mGWU%2BFnthkr8jEYjffv2pW/fvphMJo4dO8bmzZt56qmnzNO/f%2BTyCllYWBh16tRh%2B/bthIaGkpSUxPLly6/YpmbNmnTs2JH333%2BfoUOH8umnn7Lh1x/sU6dO8c033xAeHm5ev7i4mHr16gGllbqbb77ZvMzX15dq1ar96WOPjo4mMjLSoq1GjQBMpj%2B9K6sZDNglLoChTWvbBmzWrDTpGzgQkpJsGxv4eK7tE10fn9Kkb9cuuHTJ5uG56y7bxjMaS5O%2B3FwoKbFtbACvvAzbB61SpTTpu3ABiottHz8nx7bxXF1Lk75ffoHCQtvGBjh82PYxfXxKk749e%2BzzRhaHdcMkfjt37iQ2NpbPPvsMPz8/DAYDjRs3ZsKECXz99df88MMPGI1GCi97U58/f/6K/Rh%2BU6qKioriww8/5MSJE3Tr1g2/q/wH/MADD/DSSy8RGBhIs2bNaNKkCQBBQUHcd999vPDCC%2BZ109LSMP2aGQUFBZGSkmJelpOTw6VreBMGBgZeMa1rr%2BTLrvbvt0/cpCS7xC7nR9hmLl2yT3x7JF9lce0Su6jIDkF/VVxsn/gFBbaPCaVJnz1i23Pq89Il%2B8a3NVX8rHbDjGDbtm3x9/fnmWee4dChQxQWFpKVlcWWLVtITk6mS5cuBAcHs2PHDvLy8khPT2f16tV/uN9%2B/fqRmJjI%2B%2B%2B/zwMPPHDV9bp06UJOTg5vvvmmxXplieNXX31FSUkJycnJDB48mLfeegsoTRhXrFjBsWPHyM/PZ%2B7cuRTb4z9sERERkT9wwyR%2B7u7urFu3joCAAEaOHEl4eDhdunRhy5Yt/OMf/yA4OJiJEyeSnZ3NXXfdxUMPPVSh8/78/PyIjIzExcWFO37npCYXFxf69%2B/P%2BfPn6dmzp7m9VatWvPLKK7zyyiu0bduWwYMHExkZyYQJEwAYPnw4ffr0YfDgwXTo0AEfH5%2BrVhVFRETECkZj5T%2BcjMFkcsoJRYdgt/Ps7HmOn9HGV5WEhZVeUNK6tV2meuPfsf1AV68O3bvDxx/bZ6r3nntsG89oBC8vyM62z1SvT06q7YO6uJReYJGRYZ%2Bp3qws28Zzcyu9wCIlxT5TvYmJto/p5wddu8KOHfaZ6r3/ftvHBJg0qfL3%2BfLLlb/PG5jzpboiIiIiTuqGubhDRERE5Hc54dRsZdMIioiIiDgJVfxERETEMajiZzUlfiIiIuIYlPhZTSMoIiIico0yMjIYNWoU4eHhtG/fnlmzZlF0lavphw0bRsuWLQkLCzM/vvzyS6D0U8HmzZvHnXfeSVhYGCNHjiQtLa3S%2B6vET0RERBzDDXgfv3HjxuHp6cnOnTtZv349u3btYtWqVeWu%2B/3337Ny5Ur2799vfnTq1AmApUuX8vXXX7NhwwZ27tyJu7s7U6dOtbp/v6XET0REROQanDhxgj179jBp0iQ8PDyoW7cuo0aNYu3atVesm5KSwsWLF2nevHm5%2B3rvvfcYPnw4N998M97e3jz77LN8%2BeWXFh8LWxl0jp%2BIiIg4hutwjl9aWhrp6ekWbQEBAQQGBv7htkeOHMHPz4%2BaNWua24KDgzl9%2BjSZmZn4%2Bvqa27/77ju8vLwYP3483333HTVq1GDo0KFERUVx6dIlfvnlF0JCQszr16hRg2rVqnHo0CHq1q1bCUdaSomfiIiIOIbrkPjFx8ezaNEii7YxY8YQExPzh9tmZ2fj4eFh0Vb2OicnxyLxKygoIDQ0lPHjx9OkSRN2795NTEwMXl5ehIWFAeDp6WmxL3d3d7Kzs6/puK5GiZ%2BIiIg4rejoaCIjIy3aAgICKrStp6cnubm5Fm1lr728vCza%2B/XrR79%2B/cyvO3ToQL9%2B/fjoo4%2B48847LbYtk5eXd8V%2BrKXET0RERBzDdaj4BQYGVmhatzxNmjThwoULnD17lho1agBw7NgxgoKC8PHxsVh3/fr1eHl50bNnT3NbQUEBVatWpVq1atSsWZOjR4%2Bap3vT09O5cOGCxfRvZdDFHSIiIiLXoEGDBrRp04bZs2eTlZVFSkoKS5YsISoq6op1s7KymDlzJj/88AMlJSV8/vnnfPjhh0RHRwPQv39/li5dSkpKCllZWcyePZt27dpRr169Su2zKn4iIiLiGG7AGzgvXLiQF154ga5du2I0GunXrx%2BjRo0CICwsjBkzZtCnTx8efvhhcnJyGDNmDBkZGdStW5d58%2BYRHh4OwOjRoykqKmLQoEFkZ2fTvn17XnvttUrvrxI/ERERcQw3YOJXo0YNFi5cWO6y/fv3m782GAyMGjXKnBT%2BlqurKxMnTmTixInXpZ9lbrwRFBEREZHrQhU/ERERcQw3YMXP0WgERURERJyEKn4iIiLiGFTxs5oSPxEREXEMSvysphEUERERcRKq%2BImIiIhjUMXPahpBERERESehit8NLC/P9jENBnB3h/x8MJlsH3/LO7YNWr06dAc%2BnpvA%2BfM2DQ1A9N8Ntg8aFgbdE%2Bj%2BdGu47OaitrJmtW2/xzfdBL16weefw7lzNg0NwH331bR5TKMRPIEcD39KSmwenqo32f6YXYHCoLo2jwsQ9WSwzWM2agSvdoXxH3bl%2BHGbh2fz/baPCajiVwmU%2BImIiIhjUOJnNY2giIiIiJNQxU9EREQcgyp%2BVlPiJyIiIo5BiZ/VNIIiIiIiTkIVPxEREXEMqvhZTSMoIiIi4iRU8RMRERHHoIqf1ZT4iYiIiGNQ4mc1jaCIiIiIk1DFT0RERByDKn5W0wiKiIiIOAlV/ERERMQxqOJnNSV%2BIiIi4hiU%2BFlNIygiIiLiJFTxExEREcegip/VNIIiIiIiTkIVPxEREXEMqvhZTYmfiIiIOAYlflbTCIqIiIg4CVX8RERExDGo4mc1jaCIiIiIk1DFT0RERByDKn5WU%2BInIiIijkGJn9U0giIiIiJOQhU/ERERcQyq%2BFnN4RO/yMhI0tPTcXEpPRSTyYS3tze9e/dm0qRJGH/nhyQyMpIxY8bQv39/W3VXRERExG4cPvEDmDFjhkXydujQIYYOHYqHhwexsbF27JmIiIhUGlX8rPaXHMGmTZvStm1bfvjhB3JycnjhhRe44447CA8PZ/jw4Zw6deqKbVJTUxk3bhyRkZG0atWKrl27sn79evPydevW0a1bN8LDw%2BnduzfvvfeeeVlcXBydO3emXbt23H///ezYscMmxykiIuJUjMbKfziZv0TF73KFhYUkJCTwzTffEBMTwwsvvMCxY8fYuHEj/v7%2BTJ8%2BnSeffJL4%2BHiL7aZOnYqfnx9bt27Fzc2N1atXM3PmTHr27Mm5c%2BeYM2cOmzdvplGjRuzcuZPRo0fTuXNnjh8/Tnx8PBs3biQgIID4%2BHieffZZOnXqhKura4X7nZaWRnp6ukWbr28AAQGBlTIuFWUwWD7bWvXqto3n42P5bHNhYbaP2ayZ5bON3XSTbeP5%2Blo%2B25o9/q5c/j52wr9rNteoke1j1qlj%2BWxLx4/bPqZUHoPJZDLZuxPWiIyMJCMjwyLJCgoKolevXjz22GO0adOGpUuX0qFDBwAyMzM5ceIELVu2tDjHLzU1FS8vL9zd3Tlz5gy7du3iueee47PPPgOge/fuDBw4kB49etCqVSuMRiNGo5H9%2B/fz0EMPMWLECCIiImjWrBlGoxHDn8yc4uLiWLRokUXb6NFjiI2NsXKEREREKk/fvrB5s52Cf/RR5e%2BzZ8/K3%2BcN7C9R8Zs%2BfXq5F2ikp6dTUFBArVq1zG2%2Bvr60bNnyinVTUlJ46aWXSE5OpkGDBtSvXx%2BAkpIS6tSpw5o1a1ixYgVPPPEExcXF9O/fn0mTJhEWFkZcXJx5ubu7O0OGDGHkyJG/e2HJb0VHRxMZGWnR5usbQF5ehXdRKQwGqFoV8vPBHv8SfPmlbeP5%2BMAdd8CuXXDpkm1jA3R/urXtgzZrBuvWwcCBkJRk8/BbZybYNJ6vL3TsCDt3QmamTUMDEBFh%2B5gGA3h4QG6ufd7Hf2Kyo1JjFhbaPi7AU0/ZPmadOjBhAixYACdP2j6%2BOK6/ROJ3Nf7%2B/ri5uXHmzBka/VqLz8jIYPny5YwbN868XmFhISNGjODJJ59k4MCBGAwGvv/%2Be7Zs2WLepri4mMWLF1NSUkJCQgKxsbE0bNiQiIgI/P39WblyJQUFBezatYsxY8bQokULunTpUuG%2BBgYGEhhoOa1rr1/aUBrXHrHPn7d9TChN%2BuwSe/9%2BOwT9VVKSXeKfO2fzkEBp0meP2CUlto9Z9j%2BnyWSf%2BM7GnlOfJ0862dSrzl2w2l96BI1GI/369SMuLo7U1FTy8/N57bXXSExMxN3d3bxeYWEheXl5uLu7YzAYOH36NC%2B//LJ52enTp3n00UfZtWsXRqORmjVrAlC9enW%2B%2B%2B47hg0bRlJSEm5ubvj7%2B5uXiYiISCXSxR1W%2B0tX/ACefvppXn31VR544AHy8vJo164dr7/%2BusU6np6ezJ49m9dff50XX3wRf39/BgwYwNGjRzl8%2BDA9evRg2rRpPP/886SlpeHj48PAgQPp2bMnBoOB5ORkRo4cyfnz5/H392fKlCm0atXKTkcsIiIitpKRkcFzzz3Hnj17qFKlCn369GHy5Mnm%2Bwtf7l//%2BherVq0iLS2NwMBAHnroIQYNGgSUnlrWpk0bTCaTxXUCX3/9NZ6enpXWX4dP/D799NPfXe7l5cXUqVOZOnXq727bt29f%2Bvbta7H88ccfN38dFRVFVFRUuTFGjBjBiBEj/ky3RURE5M%2B6ASt048aNo2bNmuzcuZOzZ88ycuRIVq1axbBhwyzW%2B%2BSTT3jllVdYvnw5rVq1IjExkccff5waNWrQo0cPjh49ar4ziZub23Xr7403giIiIiIO4MSJE%2BzZs4dJkybh4eFB3bp1GTVqFGvXrr1i3dTUVIYPH05oaCgGg4GwsDDat2/P3r17Afjuu%2B9o2rTpdU364C9Q8RMREREncR0qfuXdRzcgIOCKCy7Lc%2BTIEfz8/Mzn/gMEBwdz%2BvRpMjMz8b3sBqJlU7plMjIy2Lt3L8888wxQmvjl5%2Bdz//33c%2BrUKYKDg5kwYQKtW1fu3R%2BU%2BImIiIhjuA6JX3x8/BX30R0zZgwxMX98H93s7Gw8PDws2spe5%2BTkWCR%2Bl0tPT2fEiBHceuut3HvvvQC4u7tz2223MXbsWKpVq8batWt57LHH2LJlC3Xr1r2WQyuXEj8RERFxWuXdRzcgIKBC23p6epKbm2vRVvbay8ur3G0SExMZO3Ys4eHhzJkzx3wRyNNPP22x3mOPPcbGjRv54osvGDx4cIX6UxFK/ERERMQxXIeKX3n30a2oJk2acOHCBc6ePUuNGjUAOHbsGEFBQfiU81mg69ev58UXXyQ2NpZHH33UYtmrr75Kjx49aN68ubmtoKCAqlWrXlPfrkYXd4iIiIhcgwYNGtCmTRtmz55NVlYWKSkpLFmypNy7gGzfvp3nn3%2BeuLi4K5I%2BgMOHDzNr1izzp44tWrSIrKws7r777krtsxI/ERERcQw34A2cFy5cSFFREV27dmXAgAF07NiRUaNGARAWFmb%2BFLBFixZRXFxMbGwsYWFh5se0adMAmDNnDvXq1aNv3760b9%2BePXv28I9//AM/Pz%2Br%2B3g5TfWKiIiIY7gB7%2BNXo0YNFi5cWO6y/Zd9LOYHH3zwu/vx8/Njzpw5ldq38tx4IygiIiIi14UqfiIiIuIYbsCKn6NR4iciIiKOQYmf1TSCIiIiIk5CFT8RERFxDKr4WU0jKCIiIuIkVPETERERx6CKn9WU%2BImIiIhjUOJnNY2giIiIiJNQxU9EREQcgyp%2BVtMIioiIiDgJVfxERETEMajiZzUlfiIiIuIYlPhZTSMoIiIi4iRU8RMRERHHoIqf1TSCIiIiIk7CYDKZTPbuhFxFdrbtYxqN4OEBublQUmLz8JdKvGwaz2gEL6/SobbD4fL%2B%2B7aPedNN0KsXbN0K587ZPv6Qhwy2DRgWBgkJ0Lo17N9v29jAgUTb/4r18ICQEDh8uPStbGu2/rny9oa2bWHvXsjKsm1sgIh6x2wf1M0N6taFlBQoKLB9/OBg28cEOHKk8vfZpEnl7/MGpqleERERcQya6rWaRlBERETESajiJyIiIo5BFT%2BraQRFREREnIQqfiIiIuIYVPGzmhI/ERERcQxK/KymERQRERFxEqr4iYiIiGNQxc9qGkERERERJ6GKn4iIiDgGVfyspsRPREREHIMSP6tpBEVERESchCp%2BIiIi4hhU8bOaRlBERETESajiJyIiIo5BFT%2BrKfETERERx6DEz2oaQREREREnoYqfiIiIOAZV/KymERQRERFxEqr4iYiIiGNQxc9qSvxERETEMSjxs5pGUERERMRJqOInIiIijkEVP6tpBEVERESchCp%2BIiIi4hhU8bOaEj8RERFxDEr8rKYRvMzatWtp2rQpq1atsndXRERExAFkZGQwatQowsPDad%2B%2BPbNmzaKoqKjcdb/44gt69%2B5NaGgoPXv25LPPPrNYvnz5cjp16kRoaChDhgzh%2BPHjld5fJX6XWbt2LQ8%2B%2BCCrV6%2B%2B6jdNRERE7MRorPyHlcaNG4enpyc7d%2B5k/fr17Nq1q9wCUnJyMjExMYwdO5Zvv/2WmJgYxo0bR2pqKgCbNm1izZo1rFy5kt27d9OiRQtiY2MxmUxW9/FySvx%2BtWvXLjIyMnj66acpKSlh%2B/bt5mXnz59n/PjxtGnThq5du7JmzRqaN2/OyZMnAfj555954oknaN%2B%2BPREREbz66qsUFBTY61BERETEBk6cOMGePXuYNGkSHh4e1K1bl1GjRrF27dor1t20aRPh4eF069YNFxcX7rnnHtq2bUt8fDwA7777LgMHDqRJkyZUrVqVCRMmcPr0aXbv3l2pfdY5fr9as2YNAwYMwN3dnYEDB/LWW2/Rq1cvACZOnIjBYGDHjh2UlJQwceJEiouLAcjJyWHo0KH06tWL119/nXPnzhEbG0tJSQkTJkyocPy0tDTS09Mt2gK8vQkMDKy8g6wIg%2BF/z3Y4l8LWEcsO0V6njdx0k%2B1j%2BvpaPttcWJht4zVrZvlsYx4eto9Ztarls615e9s2nqen5bPNubnZPqarq%2BWzLdmzsHEdflmX%2B/c3IKBCf3%2BPHDmCn58fNWvWNLcFBwdz%2BvRpMjMz8b3sF%2B3Ro0cJCQmx2L5x48YkJSWZlw8fPty8zNXVlQYNGpCUlMTtt99%2BTcdWHiV%2BwKlTp9i5cyfTpk0DYMCAASxevJg9e/ZQv359vvrqKz766CP8/PwAmDJlijkp/PzzzykoKODJJ5/EYDBw8803M3bsWGJjY/9U4hcfH8%2BiRYss2saMHk1MbGwlHeWf5O5ul7Bedolqnz/OAL/%2BGNlFx452CtwrwT5x162zS9iQP17luqlf347B7aBFC3tFrmuvwBAUZPuYx47ZPuavTBgqfZ/l/v0dM4aYmJg/3DY7OxuP3/wBKXudk5NjkfiVt667uzs5OTkVWl5ZlPgB69ato6ioiL59%2B5rbioqKeOutt3jiiScAqFOnjnlZ3br/e5OfOnWKc%2BfO0bZtW3ObyWSisLCQjIwM/P39K9SH6OhoIiMjLdoCvL0hN/eajumaGQylSV9eHlTyeQUVkV1i2wzMaCxN%2BnJzoaTEpqEB%2BPxz28f09S1N%2BnbuhMxM28fv9Vxr2wZs1qw06Rs4EH79z9qWDr9j%2B0S3atXSpO/ECcjPt3l4Ll60bTxPz9Kk7%2BBBqOS/kRXSNijF9kFdXUuTvl9%2BgcJC28f/Cyn3729AQIW29fT0JPc3f6fLXnt5WZYyPDw8yMvLs2jLy8szr/dHyyuL0yd%2B%2Bfn5rF%2B/nlmzZnHnnXea2w8fPszjjz/OiBEjgNIEr2HDhuavywQFBVGvXj3%2B/e9/m9uysrLIyMjgpj8xjxcYGHhlWTk72/bZSFkZ3WSySyZkj%2BSrLK49Yp87Z/uYZTIz7RR//347BKU06bNDbFv/73a5/Hz7xM/Ksn1MKE367BLbnlOfhYX2jW9j1%2BP3dLl/fyuoSZMmXLhwgbNnz1KjRg0Ajh07RlBQED4%2BPhbrhoSEcPDgQYu2o0ePcuutt5r3deTIESIiIgAoLCwkOTn5iulhazn9xR0ffPABBoOB3r17ExQUZH506tSJkJAQNm7cSEREBC%2B//DIXL17k4sWLvPTSS%2BbtIyIiyM7OZsWKFRQUFJCZmcnkyZMZP348BkPll6RFREScVdk/6ZX5sEaDBg1o06YNs2fPJisri5SUFJYsWUJUVNQV6/bp04c9e/awbds2ioqK2LZtG3v27DHPNt5///28/fbbJCUlkZ%2Bfz4IFC6hRowbh4eHWdfI3nD7xW7duHb1798a1nBNko6Oj2bx5M7NmzcJgMNClSxfuu%2B8%2BmjdvDpSeeOnt7c2qVavYvXs3nTp1olu3bhiNRpYuXWrrQxEREREbW7hwIUVFRXTt2pUBAwbQsWNHRo0aBUBYWBhbtmwBSi/6WLx4McuWLaNt27YsWbKEuLg482xiVFQUQ4cOZfTo0dx%2B%2B%2B388MMPLFu2rNz8xBoGU2XfIOYv6Ouvv6ZNmza4/3rBw6FDh%2BjXrx%2BJiYlUvZ6XzWVnX799X42dT3q7VGLbyzuMRvDyss%2BsOsD779s%2B5k03lV5UsnWrfaZ6hzxk40p4WBgkJEDr1naZ6j2QaPtfsR4eEBIChw/bZ6rX1j9X3t7Qti3s3Wufqd6Iena42MHNDerWhZQU%2B0z1BgfbPibX55xVe139bi9OX/GriHnz5rF06VKKiorIyspi6dKl3Hnnndc36RMRERGpZEr8KmDBggUkJiZy%2B%2B23ExkZSZUqVSzO8xMREZHr70Y7x88ROf1VvRXRpEkT/vnPf9q7GyIiIk7NGRO1yqaKn4iIiIiTUMVPREREHIIqftZTxU9ERETESajiJyIiIg5BFT/rKfETERERh6DEz3qa6hURERFxEqr4iYiIiENQxc96qviJiIiIOAlV/ERERMQhqOJnPSV%2BIiIi4hCU%2BFlPU70iIiIiTkIVPxEREXEIqvhZTxU/ERERESehip%2BIiIg4BFX8rKfET0RERByCEj/raapXRERExEmo4iciIiIOQRU/66niJyIiIuIkVPETERERh6CKn/WU%2BImIiIhDUOJnPYPJZDLZuxNSvosXbR/TaAQfH7h0yT5vsGp5qbYN6OIC/v6QkQFFRbaNDWR51bR5TKMRPD0hJ8c%2B3%2BNjx2wbz8MDQkLg8GHIzbVtbIBWoQbbBw0Lg4QEaN0a9u%2B3ffz5820bLzAQhgyBNWsgLc22se3F3sc8YYLtY3J9fn8EB1f%2BPm9kqviJiIiIQ1DFz3q6uENERETESajiJyIiIg5BFT/rKfETERERh6DEz3qa6hURERFxEqr4iYiIiENQxc96qviJiIiIOAlV/ERERMQhqOJnPSV%2BIiIi4hCU%2BFlPU70iIiIiTkIVPxEREXEIqvhZTxU/ERERESehip%2BIiIg4BFX8rKfET0RERByCEj/raapXRERExEmo4iciIiIOQRU/6ynxExEREYegxM96muoVERERcRKq%2BImIiIhDUMXPekr8RERERK6TnJwcZs6cyaeffkpRURFdu3Zl%2BvTpeHl5lbv%2B9u3bWbJkCSkpKfj5%2BdG/f39GjRqF0Vg6SduzZ09Onz5tfg2wfv16goODK9QfJX4iIiLiEByx4jdz5kzOnDnD9u3bKS4uZty4ccyfP5/p06dfse7333/PU089xWuvvUbnzp356aefGD58OJ6enjz66KNkZWXx008/sWPHDmrXrn1N/dE5fiIiIuIQSkoq/3E95ebm8sEHHxAbG4ufnx/%2B/v5MnDiRjRs3kpube8X6p06d4u9//zsREREYjUaCg4O5%2B%2B672bt3L1CaGPr5%2BV1z0geq%2BImIiIgTS0tLIz093aItICCAwMDACm2fl5dHampquctyc3MpLCwkJCTE3BYcHExeXh7JycnccsstFuv36NGDHj16WOz7888/p3fv3gB89913eHh4MHjwYI4cOULt2rWJiYkhIiKiQn0FJX4iIiLiIK5HhS4%2BPp5FixZZtI0ZM4aYmJgKbX/gwAEeeuihcpeNHTsWAE9PT3Obh4cHANnZ2b%2B736ysLMaOHYu7uztDhw4FwGAw0LJlS5588klq1arFv//9b2JiYnj77bcJDQ2tUH%2BV%2BImIiIjTio6OJjIy0qItICCgwtu3b9%2BeQ4cOlbvshx9%2B4PXXXyc3N9d8MUfZFK%2B3t/dV93n8%2BHFiY2Px9/dn9erV5nWHDRtmsV6fPn348MMP2b59uxI/ERER%2BWu5HhW/wMDACk/r/lkNGzbE1dWVo0eP0qpVKwCOHTuGq6srDRo0KHebL774gieffJIBAwYwYcIEXFz%2Bl6qtXLmS5s2bc8cdd5jbCgoKqFq1aoX79Je8uOPixYs8//zzdO7cmdDQUDp06MDkyZP55ZdfAOjVqxdbtmwBYMiQIcTFxV11XwUFBSxYsIBu3boRFhbG7bffTkxMDMeOHbPJsYiIiEgpR7u4w8PDg549ezJ//nzOnTvHuXPnmD9/Pvfeey/u7u5XrJ%2BYmMjo0aN55plnmDx5skXSB3DmzBlmzJhBSkoKRUVFrF%2B/nv3793PfffdVuE9/ycRv/PjxnD9/nvXr15OYmMj7779PQUEBjzzyCEVFRWzdupU%2BffpUaF8zZ85k//79rFq1iv379/Pxxx8TFBTEoEGDyMzMvM5HIiIiIo5s%2BvTpNGjQgN69e/O3v/2NOnXqMG3aNPPyXr168cYbbwDwxhtvUFRUxKxZswgLCzM/yqZ4n3rqKTp16sTAgQMJDw/nnXfe4c0336R%2B/foV7s9fcqp33759zJo1yzxHX6NGDaZMmcKCBQvIzMwkKiqKMWPG0L9/fwB%2B/vlnhgwZQlJSEsHBwUyZMoXbbrvNvK%2B%2BfftSp04dAHx9fXnqqafIysoiPT0dX19fhgwZQosWLdizdPHd3wAAIABJREFUZw/Hjx%2BnUaNGTJkyhfDwcPsMgIiIyF%2BQI97Hz9vbm5kzZzJz5sxyl2/dutX8dVkCeDVubm5MmTKFKVOmXHN//pKJX69evZg%2BfTrffvst7dq1o1WrVtSuXZu5c%2BeWu/6OHTtYtmwZoaGhrFixguHDh/Of//wHX19fevXqxaJFi/jpp5%2B4/fbbadWqFQ0bNmTOnDkW%2B4iPj2fp0qW0bt2alStXMnLkSD7%2B%2BGOqV69eoT6Xdzm5h0cAAQHX57yDqym7EbjRXrVgFxv/SFapYvlsY/YYZ4Phf8/2iP/rBW02U3bqy584BaZyhYXZPmazZpbPtnadzpe6qptusnx2BvY85rQ028eUSmMwmUwme3eispWUlPDBBx%2Bwbds29u3bx6VLl6hXrx4xMTH06dOHyMhIc8VvyJAh3HLLLebs2WQy0blzZyZNmmS%2Bb86nn37K%2B%2B%2B/z969ezl37hyBgYE89thj5surhwwZQtOmTZk6darFPsaNG2euKv6RuLi4Ky4nHz16DLGxFbucXERExCYWLID/b%2B/O46Ks1/%2BPv0ZBQVxQwNS0LCItV1Qkj7lhWB73NO1orlnupqmlZVKSkkv6E9DMMjvpsWOamqjnZCe1csOOW6biVh5MDFBSkUUGmd8ffp3j5BKdWe5m5v18PHiM87ln7uu6g5iL63N/7nvcOENC/%2BMfjt9n%2B/aO3%2BcfmUd2/EqUKEGXLl3o0qULFouFkydP8tlnn/HSSy/dcon29WlcuHaNnCpVqthcjDEqKsq61Ds1NZVNmzYxe/ZsAgICeOqppwBsVudc38evO3h3cqvl5P7%2BIWRnF3sXDlGiBAQEQE6OMS31cgXnXRuwZEkIDIQLF%2BDqVdfGBnL9g1we02S61nXLywMj/uz76SfXxitdGu69F/7zH7hyxbWxAR58upHrg9auDcuXQ%2B/ekJLi%2Bvhjx7o2XqVK0KEDbNgAWVmujW0Ubzxm3HOq94/G4wq/b775htGjR7NlyxYCAwMxmUw88MADjBs3ju3bt3P48OGb3pNxQ9u6qKiItLQ07r77bk6ePEnXrl359NNPrVfdvueeexg8eDAHDhzgyJEj1vfdWChe30fVqlWLnfetlpNfvGjcD7krVjvdUmGhAUG5VvQZENuI/8bXp3ctFmPi3%2BIuRS5x5YpBsfftMyDo/0lJMSa%2BUVOBWVneNw3pjccsdvG4Vb0REREEBQUxadIkjh49itls5vLly6xbt45Tp07RunXrm96zatUqDhw4QEFBAQkJCfj4%2BNCqVSvuv/9%2B6tSpw5QpU/juu%2B%2B4cuUKeXl5fPXVVyQnJxMdHW3dx8qVK/n%2B%2B%2B8pKChg/vz5WCyW33ULFREREbkzd7ucyx%2BRx3X8/Pz8WL58OYmJiQwbNozz58/j6%2BtLw4YNWbJkCaGhoTe9p127dsTExJCamkrdunVZvHix9fYq7733HgsWLGDChAmkp6dTokQJHnroIWbNmmVzAcWmTZsydepUTpw4wcMPP8wHH3xAuXLlXHbcIiIiIr/F4wo/uDZtOnXq1Ntu37x5s/XfS5cuveO%2BypUrx8svv8zLL798x9eFhYURHx//%2BxIVERGRYvPGDp2jeWThJyIiIp5HhZ/9PO4cPxERERG5NXX8HOC3potFRETEfur42U8dPxEREREvoY6fiIiIuAV1/Oynwk9ERETcggo/%2B2mqV0RERMRLqOMnIiIibkEdP/up4yciIiLiJdTxExEREbegjp/9VPiJiIiIW1DhZz9N9YqIiIh4CXX8RERExC2o42c/dfxEREREvIQ6fiIiIuIW1PGznwo/ERERcQsq/OynqV4RERERL6GOn4iIiLgFdfzsp46fiIiIiJdQx09ERETcgjp%2B9lPhJyIiIm5BhZ/9NNUrIiIi4iXU8RMRERG3oI6f/VT4iYiIiFtQ4Wc/FX5/YBXKWwyKbKJcWYNin7vs2nilSkFQEOTmQkGBa2MDpSvd5fKY1/n6GhM3K8u18cqWvfZ48SJcdvGPFwCzZ7s%2BZuXK1x7HjoWMDNfHHz/etfHCw6FvX5g7F/btc21sgAULXB/Tz%2B%2B/j2XKuD6%2BuC0VfiIiIuIW1PGznxZ3iIiIiHgJdfxERETELajjZz8VfiIiIuIWVPjZT1O9IiIiIl5CHT8RERFxC%2Br42U8dPxEREREvoY6fiIiIuAV1/Oynwk9ERETcggo/%2B2mqV0RERMRLqPATERERt1BU5PgvZ8vNzWXSpElERkbSuHFjXnrpJXJycm77%2BpiYGOrWrUt4eLj1a8WKFdbt7733Hi1btqRhw4b07duXH3744Xflo8JPRERExEliY2M5e/Ysn3/%2BOZs2beLs2bPMvsM9vA8ePEhsbCz79u2zfvXq1QuANWvWsHTpUhYvXkxycjJ16tRh9OjRWCyWYuejwk9ERETcgrt1/PLy8khKSmL06NEEBgYSFBTE%2BPHjWb16NXl5eTe9vqCggGPHjlG3bt1b7u%2BTTz6hd%2B/ehIWFUbp0acaNG0daWhrJycnFzkmLO0RERMQt/BEXd%2BTn55Oenn7LbXl5eZjNZh588EHrWGhoKPn5%2BZw6dYqHHnrI5vUpKSkUFhYSHx/Pnj17KFeuHN27d2fw4MGUKFGCEydO8Nxzz1lf7%2BvrS82aNUlJSeGRRx4pVr4q/ERERMRrZWRkkJmZaTMWEhJC5cqVi/X%2BAwcO0K9fv1tue%2BGFFwAoU6aMdczf3x/gluf5ZWdn07RpU/r27cucOXM4cuQII0aMoESJEgwePJicnBzr%2B6/z8/MjNze3WLmCCj8RERFxE87o%2BK1YsYLExESbsZEjRzJq1KhivT8yMpKjR4/ectvhw4eZN28eeXl5BAQEAFineMuWLXvT65s3b07z5s2tz%2BvXr0///v3ZuHEjgwcPxt/fn/z8fJv35OfnW/ddHCr8RERExGv16tWLqKgom7GQkBCH7Pu%2B%2B%2B7D19eXEydO0KBBAwBOnjxpnaL9tX/961%2BcO3eOp59%2B2jpWUFCAn58fAGFhYRw/fpw2bdoAYDabOXXqlM1U8m/R4g4RERFxC85Y3FG5cmXq1Klj81Xcad7f4u/vT/v27Zk9ezZZWVlkZWUxe/ZsOnbsaC3mbmSxWIiLi2Pnzp1YLBb27dvHRx99ZF3V2717d5YtW0ZKSgpXrlzh7bffJjg4mCZNmhQ7J3X8RERExC38ERd3/JaYmBhmzJhBp06dMJvNtG3bltdee826vUOHDnTq1ImhQ4cSHR3NpEmTeP3110lPTyc4OJhRo0bRpUsXAHr06EF2djYjRowgKyuLevXq8e677%2BLr61vsfFT4iYiIiDhJ2bJliY2NJTY29pbbN2zYYPP86aeftpnqvZHJZGLQoEEMGjTof85HhZ%2BIiIi4BXfs%2BP3R6Bw/ERERES%2Bhjp%2BIiIi4BXX87KfCT0RERNyCCj/7aapXRERExEuo4yciIiJuQR0/%2B3lk4RcVFUVmZiY%2BPtcOz2KxULZsWTp16sSECRMoUcJxjc6EhAR2797N0qVLHbZPEREREWfwyMIP4I033uDJJ5%2B0Pj969CgDBgzA39%2Bf0aNHG5iZiIiI/C/U8bOf15zjV6tWLSIiIjh8%2BDDp6emMGTOGqKgoGjRoQNu2bVm1apXNa998800iIyMZOnQoAElJSXTs2JHw8HDat2/Pxo0bra/Pyclh8uTJPProo0RGRjJ37lyXH5%2BIiIinc8Yt27yNx3b8bmQ2m9m7dy%2B7du1i1KhRTJ48mcDAQDZs2ECpUqX46KOPiI2NpX379gQEBACQmprK1q1bMZvNJCcn88orr5CYmEiLFi3Ytm0bw4cPt94U%2BfDhw/Tv35/Y2FiSk5MZMGAArVu3Jjw8vNg5ZmRkkJmZaTMWEhzssPsFuo1SpVwb7/ptbn7H7W7EPmXLujZemTK2jy5nxP/DlSrZPrra7/jd5xC1a9s%2BulpwsOtjBgbaPrrSuXOujykO47GF3xtvvMH06dOtz6tUqcLAgQN55plnaNeuHQEBAfj6%2BpKWlkZAQAD5%2BflcvHjRWvh17NgRf39//P39Wbt2Le3ataNVq1YAtGzZkuXLl3PXXXcBEBYWZr2P3iOPPEJwcDCpqam/q/BbsWIFiYmJNmMjR4xglFHT0iaTMXFr1DAmbpUqhoQ1stw0qtaNiDAmbp06xsQloq9BgYEOHYyJ29egY16%2B3Ji4RoqOdn3Md95xfcz/440dOkfz2MIvJibG5hy/G50%2BfZqZM2dy6tQpatasyb333gtA0Q0/UTd22jIyMnj44Ydt9lG/fn3rvwN/9RdXqVKluHr16u/Kt1evXkRFRdmMhQQHg8Xyu/bjECaTMXEBfvrJtfF8fa8VfT//DGaza2MD5irGFLq%2BvoYcLgD797s2Xpky14q%2BQ4cgN9e1sQEiUgxY%2BFWp0rWib8MGyMpyfXxXn%2B5Su/a1oq93b0hJcW1sgEmTXB8zMPBa0ffFF3Dhguvji9vy2MLvdsxmM0OGDOHFF1%2Bkd%2B/emEwmvv/%2Be9atW2fzOtMNHa%2BqVauSlpZms/2DDz6gYcOGDsurcuXKN0/rGlV8GamgwJi4ZrNxsb3M5cvGxM3NNSh2RoYBQf9PVpYx8fftc31MuFb0GRHbyKnPCxe8aupVHT/7ec3ijuvMZjP5%2Bfn4%2BflhMplIS0tj1qxZ1m230q1bN7744gu2bdtGUVER33zzDQkJCZQrV86VqYuIiHg1Le6wn9cVfmXKlGH69OnMnz%2Bf8PBw%2BvXrR/PmzQkODubYsWO3fE/jxo2ZMWMGM2bMoEmTJsycOZM5c%2BYQFhbm4uxFRERE/nceOdW7efPmO27v0qWLdTHGdc8//7z130ePHr3pPe3bt6d9%2B/Y3jY8aNep3xxcREZHfzxs7dI7mdR0/EREREW/lkR0/ERER8Tzq%2BNlPhZ%2BIiIi4BRV%2B9tNUr4iIiIiXUMdPRERE3II6fvZTx09ERETES6jjJyIiIm5BHT/7qfATERERt6DCz36a6hURERHxEur4iYiIiFtQx89%2BKvxERETELajws5%2BmekVERES8hDp%2BIiIi4hbU8bOfOn4iIiIiXkIdPxEREXEL6vjZT4WfiIiIuAUVfvbTVK%2BIiIiIl1DHT0RERNyCOn72U8dPRERExEuo4yciIiJuQR0/%2B6nwExEREbegws9%2BmuoVERER8RLq%2BImIiIhbUMfPfir8/sCuFpkMiVuypIGx9%2B93bcDAQKhRA44dgwsXXBsb6PFiqMtj3n8/zJ0LL70EP/zg8vB8NuekawOWKgXUIKLKaSgocG1sgL2uD2m4BQtcGy84%2BNrjpElw7pxrYwMMH%2B76mOHh8NRTEBcH%2B/a5Pv6wYa6PKQ6hwk9ERETcgjp%2B9lPhJyIiIm5BhZ/9tLhDRERExEuo4yciIiJuwR07frm5ucTGxrJ582YKCwtp27YtMTExBAQE3PTaKVOmkJSUZDOWn5/Pn/70JxYvXkxRURGNGzfGYrFgMv33XPzt27dTpkyZYuWjjp%2BIiIiIk8TGxnL27Fk%2B//xzNm3axNmzZ5k9e/YtXzt16lT27dtn/UpISKB8%2BfJMnDgRgBMnTmA2m9m9e7fN64pb9IEKPxEREXETRUWO/3KmvLw8kpKSGD16NIGBgQQFBTF%2B/HhWr15NXl7eHd%2BblZXF%2BPHjefXVVwkLCwPg4MGD1KpVi1KlSv3POWmqV0RERNyCMwq1jIwMMjMzbcZCQkKoXLlysd6fn59Penr6Lbfl5eVhNpt58MEHrWOhoaHk5%2Bdz6tQpHnroodvud/bs2dStW5fOnTtbxw4ePMiVK1fo3r07Z86cITQ0lHHjxtGoUaNi5Qoq/ERERMSLrVixgsTERJuxkSNHMmrUqGK9/8CBA/Tr1%2B%2BW21544QUAm6lYf39/AHJycm67z9OnT7Nu3TpWrlxpM%2B7n50f9%2BvV54YUXqFChAn/729949tlnWbduHTVq1ChWvir8RERExC04o%2BPXq1cvoqKibMZCQkKK/f7IyEiOHj16y22HDx9m3rx55OXlWRdzXJ/iLVu27G33%2BemnnxIeHn5TR/D6uX7XPfvss6xevZqvvvqKZ555plj5qvATERERr1W5cuViT%2Bv%2BXvfddx%2B%2Bvr6cOHGCBg0aAHDy5El8fX2pWbPmbd%2B3adMmBg0adNP43Llzefzxx3n44YetYwUFBZQuXbrYOWlxh4iIiLgFd1vc4e/vT/v27Zk9ezZZWVlkZWUxe/ZsOnbsiJ%2Bf3y3f88svv3Dy5EkiIiJu2nbs2DGmTZtGZmYmBQUFJCYmcvnyZaKjo4udkwo/ERERcQvuVvgBxMTEULNmTTp16sQTTzxB9erVmTJlinV7hw4dWLhwofX5Tz/9BMBdd911077i4uK455576NKlC5GRkezevZslS5YQGBhY7Hw01SsiIiLiJGXLliU2NpbY2Nhbbt%2BwYYPN83r16t32nMHAwEDi4uLsykeFn4iIiLgFd7xzxx%2BNpnpFREREvIQ6fiIiIuIW1PGznwo/ERERcQsq/OynqV4RERERL6GOn4iIiLgFdfzsp46fiIiIiJdQx09ERETcgjp%2B9lPhJyIiIm5BhZ/9NNUrIiIi4iXU8RMRERG3oI6f/dTxExEREfES6viJiIiIW1DHz35eWfhFRUWRmZmJj4/t4YeHh/PBBx8YlJWIiIjciQo/%2B3ll4Qfwxhtv8OSTTxqdhoiIiIjL6By/X0lPT2fMmDFERUXRoEED2rZty6pVq6zba9WqxZtvvklkZCRDhw4FYMeOHfTo0YMmTZrQoUMH1q1bZ1T6IiIiHquoyPFf3sZrO363M3nyZAIDA9mwYQOlSpXio48%2BIjY2lvbt2xMQEABAamoqW7duxWw2k5KSwrBhw5g1axZt27blwIEDDB8%2BnIoVK9KiRYtix83IyCAzM9NmLCgohJCQyg49vj%2B8wEDXxitXzvbRxe6/3/Uxq1e3fXS5UqVcG8/X1/bR1Sob8P9wpUq2j67m5%2BfaeNd/b7j698d14eGuj1m7tu2jK%2B3b5/qY/8cbCzVHM1ksFovRSbhaVFQU58%2Bfx/dXHwRff/012dnZBAQE4Ofnx9mzZ9m5cyevvfYaW7ZsoVq1atSqVYtZs2bRuXNnAF5//XUuXbrEnDlzrPuZM2cOx44dY%2BHChcXOKSEhgcTERJuxESNGMnr0KDuOVERExMFMJjCodAgLc/w%2Bjx93/D7/yLy24xcTE3PLc/wOHz7MzJkzOXXqFDVr1uTee%2B8FoOiGPzMq3/AX/JkzZ9i1axdNmjSxjl29epV77rnnd%2BXTq1cvoqKibMaCgkK4evV37cYhSpbEkLgAJbd%2B6dqA5cpB06awezdkZ7s2NjB2fVuXx6xeHcaNg7ffhp9%2Bcnl45r542rUBfX2hShX4%2BWcwm10bG2DrVtfHrFQJOnSADRsgK8v18Y3o%2BEVHwxdfwIULro0NEBfn%2Bpi1a8Py5dC7N6SkuD6%2BQdTxs5/XFn63YjabGTJkCC%2B%2B%2BCK9e/fGZDLx/fff33TOnslksv67SpUqdOvWjalTp1rHMjIy%2BL2N1MqVK9sUlGBc8WUoI35pw7Wiz4DYP/zg8pBWP/1kUPyCAgOCcq3oMyJ2RobrY16XlWVM/DJlXB8Trv0/fO6c6%2BMaOPVJSoqx8cXtaHHHDcxmM/n5%2Bfj5%2BWEymUhLS2PWrFnWbbfSo0cP1q9fz7Zt2ygqKuLUqVM888wzuiyMiIiIg2lxh/1U%2BN2gTJkyTJ8%2Bnfnz5xMeHk6/fv1o3rw5wcHBHDt27JbvadCgAXPmzGHOnDlERETwzDPPEBUVxbhx41ycvYiIiGdT4Wc/r5zq3bx58223denShS5dutiMPf/889Z/Hz169Kb3tG7dmtatWzssPxERERFn8MrCT0RERNyPN3boHE1TvSIiIiJeQh0/ERERcQvq%2BNlPhZ%2BIiIi4BRV%2B9tNUr4iIiIiXUMdPRERE3II6fvZTx09ERETES6jjJyIiIm5BHT/7qfATERERt6DCz36a6hURERHxEur4iYiIiFtQx89%2B6viJiIiIeAl1/ERERMQtqONnPxV%2BIiIi4hZU%2BNlPU70iIiIiXkIdPxEREXEL6vjZTx0/ERERES%2Bhjp%2BIiIi4BXX87KfCT0RERNyCCj/7aapXRERExEuo8BMRERG3UFTk%2BC9XycvLo1evXqxevfqOrztw4ABPPfUU4eHhREVFsXLlSpvta9asITo6moYNG/Lkk0%2Byb9%2B%2B35WHCj8RERERJzp%2B/Dh9%2BvRh//79d3zdxYsXef755%2BnatSvffvst06ZNIy4uju%2B%2B%2Bw6A5ORkYmNjeeutt/j222/p3Lkzw4YNIy8vr9i5qPATERERt%2BCOHb%2BdO3fSv39/unXrRrVq1e742k2bNhEYGEifPn3w8fGhWbNmdOrUib/97W8ArFy5kg4dOtC4cWN8fX0ZMGAAFStWZOPGjcXOR4s7RERExC04o1DLyMggMzPTZiwkJITKlSsX6/35%2Bfmkp6ffcltISAi1a9dmy5YtlC5dmiVLltxxX8ePH%2BfBBx%2B0GXvggQdYtWoVACdOnKB79%2B43bU9JSSlWrqDC7w%2BtZEnXx8zIyGDFihX06tWr2D/0DvWrH2hny8jIYEVCgmHH%2B5lrDxe4dswJCSuYNMmg7zGhLo1m8z2uUcOlsQEYN87lIY3%2BuXY1w4932DCXh7Qe8z//6RXf4%2BssFsfvMyFhBYmJiTZjI0eOZNSoUcV6/4EDB%2BjXr98tt82fP5/HHnus2Lnk5OTg7%2B9vM%2Bbn50dubm6xtheHCj%2BxkZmZSWJiIlFRUV7xy8Tbjhe875i97XjB%2B47Z244XvPOYnaVXr15ERUXZjIWEhBT7/ZGRkRw9etQhufj7%2B5OdnW0zlp%2BfT0BAgHV7fn7%2BTdsrVqxY7Bgq/ERERMRrVa5c%2BQ9TPD/44INs377dZuzEiROEhYUBEBYWxvHjx2/a3rJly2LH0OIOERERkT%2BA6Ohozp07x4cffojZbGbXrl0kJSVZz%2Bvr0aMHSUlJ7Nq1C7PZzIcffsj58%2BeJjo4udgwVfiIiIiIG6dChAwsXLgSgYsWKfPDBB/zzn/8kMjKSyZMnM3nyZB555BEAmjVrRkxMDK%2B//jpNmzZlw4YNvPfeewQGBhY7nqZ6xUZISAgjR478Xec3uDNvO17wvmP2tuMF7ztmbzte8M5j9gSbN2%2B%2BaWzDhg02z%2BvVq8ff//732%2B6jS5cudOnS5X/OwWSxOGONjIiIiIj80WiqV0RERMRLqPATERER8RIq/ERERES8hAo/ERERES%2Bhwk9ERETES6jwExEREfESKvxEREREvIQKPxEREREvocJPRERExEuo8BMRj5WVlWV0CiIifygq/ETEoxQWFjJ37lwaN25MVFQUp0%2Bfpnv37mRkZBidmlNdvHiRtWvXsmjRItavX8/ly5eNTklE/oB0r17xGt9%2B%2B%2B1vviYiIsIFmRjj6tWrlCxZEoCvvvqKihUrUr9%2BfYOzcry5c%2Beya9cuRo0axdixY/nqq6%2BYMGECPj4%2BzJs3z%2Bj0nGLPnj0MGzYMf39/qlSpQlpaGhaLhSVLlhAWFmZ0eiLyB6LCz4vVrl0bk8l0x9ccOXLERdk4X%2B3atQFsjrlChQpkZ2dTVFREYGAgO3fuNCo9p9q8eTOTJ09mx44dLFiwgIULF2IymXj11Vfp2bOn0ek5VFRUFB9//DF33XUXTZs2Zffu3Vy6dIno6GiSk5ONTs8punfvTnR0NEOHDgXAYrGQmJjI7t27Wbp0qcHZOc%2BBAwdITU3l6tWrNuNdu3Y1KCPn2759O0uXLiUjI4N3332XDz74gHHjxuHj42N0auIm9JPixT766CPg2i%2BSr7/%2BmpEjR3LPPfdw9uxZ5s%2BfT/PmzQ3O0LFSUlIAWLx4MceOHWPy5MmUK1eO3Nxc3nrrLSpUqGBwhs7zzjvvMGbMGIqKili2bBkJCQkEBQUxduxYjyv8cnNzqVSpEnCtAALw8/OjRAnPPbPlhx9%2BYPDgwdbnJpOJoUOH8uGHHxqXlJPNnTuXRYsWERwcjK%2Bvr3XcZDJ5bOGXlJREXFwcTz31lHUGY/PmzZhMJl566SWDsxO3YRGv99hjj1l%2B/vlnm7GMjAxLq1atjEnIyZo1a2bJy8uzGcvPz7c0bdrUoIyc7/qxHTp0yNKwYUOL2Wy2WCwWS8OGDY1MyymGDBlimTNnjsVisVgiIiIsFovF8v7771uee%2B45I9Nyqm7dull2795tM3bo0CFLz549DcrI%2BR555BHLrl27jE7DpTp27GjZt2%2BfxWKxWJo0aWKxWCyWH3/80dKiRQsj0xI3o46fkJWVRfny5W3GSpcuTXZ2tkEZOVdRURHnz5/n7rvvto799NNP1vPfPJG/vz/nz59n8%2BbNNG7cGB8fH1JSUqhYsaLRqTncq6%2B%2BSv/%2B/VmzZg05OTn8%2Bc9/JicnhyVLlhidmtNERkYydOhQunfvzr333ktGRgYrV66kadOmJCYmWl83cuRIA7N0rJIlSxIZGWl0Gi71888/06BBA%2BC/p6zce%2B%2B95ObmGpmWuBkVfkJERAQvv/wyEyZMoEqVKpw%2BfZq33nqLVq1aGZ2aU3Tp0oVnn32WwYMHU7VqVU6fPs3777/P008/bXRqTtO9e3e6du3KpUuXiI%2BP5/vvv2fw4MEMGjTI6NQcrkaNGmzYsIEtW7aQlpZGlSpVaN26NWXLljU6Naf5/vvvefjhhzly5Ij1vNzQ0FDOnz/P%2BfPnAX7zfF5306ZNG9avX0/Hjh2NTsVlatasyZdffsljjz1mHduxYwf33nuvgVmJu9HiDiEzM5MxY8awZ88e64dD8%2BbNmTNnzk2dQE9QWFjI/PnzWbduHenp6VStWpWnnnqK5557zuM%2BHG%2BUnJxM6dKladiwIWfPnuXgwYO0a9fO6LQcLi0t7Zbjvr6%2BVKhQgVKlSrk4I3Gkvn37YjKZyMnJ4ciRIzzwwAMEBgbavOb6%2BcueZseOHQwfPpy2bdvyr3/9i27durF%2B/Xrefvttj/1DXRy19kn4AAAVN0lEQVRPhZ9YnTlzhoyMDKpUqULVqlWNTkcc7Ny5cwQHB1NQUMCqVauoWLEi7du3Nzoth6tTpw5FRUW33FaiRAn%2B9Kc/MWPGDOsCEHd3%2BfJlMjMzue%2B%2B%2BwD49NNPOXLkCNHR0R45FXrj1PXteNKU9q%2BlpKSwYsUKzpw5Q5UqVejRo4dHXpZJnEeFn3il7du3s2zZMtLT073ikggrV65k2rRp7N%2B/n2nTprFx40ZMJhO9e/dm%2BPDhRqfnUMuWLWPLli288sor1KhRg59%2B%2BomZM2dSt25d2rVrxzvvvIOPjw%2BzZs0yOlW7nTx5kr59%2B9KmTRumTZvGhx9%2ByNtvv02bNm1ITk7m7bff5tFHHzU6Tac5efIkd911F2XLlmXfvn2UL1%2Be0NBQo9NymmHDhjFr1iyPPm1BXMDIlSVirFq1allq1659xy9PtG7dOkuzZs0sc%2BbMsTRq1MiSkZFhadeunWXGjBlGp%2BY0nTt3tmzbts1SWFhoadSokWXPnj2W1NRUj1y5/dhjj1l%2B%2BeUXm7ELFy5Y2rZta7FYLJbs7GyPWcE9atQoy7Rp0yyFhYUWi8ViadGihWXx4sUWi8Vi2bp1q%2BWZZ54xMj2n2rhxo6Vu3bqWgwcPWiwWi%2BWDDz6whIeHW7Zu3WpwZs4TGRlpuXLlitFpiJvzzPaGFIunngfzWxYtWsSCBQto2LAhy5cvJyQkhHfffZd%2B/fp57LWwzp49S/Pmzdm7dy8%2BPj40atQIgEuXLhmcmeP98ssvN63QNplM1kUO/v7%2Bt50Kdjf//ve/2bRpEyVLluTUqVNkZmYSHR0NXFvpO27cOIMzdJ7ExEQWLFhA3bp1ARg4cCAPPPAAs2bN8tjz3Tp27Mjo0aPp1KkTISEhNucke/Jdh8SxVPh5saZNmwLw/vvv07t3b8qUKWNwRq7hjZdEqFChAv/5z3/4/PPPrd/3Xbt2ERISYnBmjteiRQvGjRvHq6%2B%2BSrVq1UhLS2PmzJk0b96cgoIC5s%2BfT506dYxO0yHy8/Ot034HDhygUqVK1KhRA7h2PuOv72jhSc6ePUuLFi1sxh599FHGjh1rUEbOt2zZMgC2bt1qM24ymTzqLkviXCr8hEWLFjFw4ECj03AZb7wkwsCBA%2BnUqRMAS5cuZc%2BePQwZMoSYmBiDM3O8mJgYxo0bx%2BOPP24t7Fu3bs20adP497//zdatW5kzZ47BWTpGUFAQZ8%2BepWrVquzatcum65OSkkLlypUNzM657r77br755hub4m/nzp1Uq1bNwKyc6/rdh0TsocUdwrhx4wgLC%2BPJJ5/06A%2BK67z1kginT5/Gx8eHqlWrkpWVRVpamnWazBOlp6fz888/Y7FYWL16NevWrWP//v1Gp%2BVQb7/9Nvv376dFixYkJiYSHx9P69atOXHiBFOmTCE8PJwJEyYYnaZTJCUl8eqrr9KuXTvuvvtu0tLS%2BOKLL5gxYwaPP/640ek5TV5eHhcvXrSermA2mzl27Jh1il/kt6jwE1q3bs3PP/98y2vYeer0gTdeEsHbPjD%2B/e9/s3jxYr766ivCwsLo2bMnffr0MTothyooKCA2Npa9e/fSoUMH6wrt%2BvXrU7duXRYtWuTRK0CTk5NZu3YtmZmZVK1alW7dulnPX/VEn376KbGxsVy5csVmPCgoiG3bthmUlbgbFX7C7t27b7vt%2BvlgnuTzzz%2Bnbdu2Hnvpllvxlg%2BMoqIi/vnPf7JkyRKOHz9OYWEh77zzzk3ngnm6kydPevRlTcA7L20SHR1Nnz59CAgI4Ntvv6V///7MmjWL5s2b89xzzxmdnrgJFX5idfHiRU6fPs3DDz9MYWGhx97hoGXLlpjNZrp27UqPHj08/gMSvOMD469//SsfffQRRUVF/OUvf6Fnz5488cQTfPbZZ9x1111Gp%2Bd0ly9f5quvviI9PZ3q1avTsmVL/Pz8jE7LaR555BG%2B/vprj/09dSsNGzZk3759nDlzhvHjx/P3v/%2BdtLQ0BgwYwKZNm4xOT9yE97Q85LZycnKYMmUKGzZswM/Pj9WrVzNw4ECWLFnC/fffb3R6Drd161a%2B%2BeYb1q5dy5NPPslDDz1Ejx49%2BPOf/%2ByxK5szMzPp378/Z86c4dNPP6VOnTpMnz6dAQMGeEzhFxcXR%2B/evZk4caJXFQMABw8eZPDgwfj5%2BVGlShXOnDlDqVKleP/99z3y/2HwzkubBAUFYTabqVq1Kj/%2B%2BCMA1apVs16qSKQ4VPgJM2fOJDc3l3/84x/07NmTGjVqWO8EsHjxYqPTc7gSJUrQqlUrWrVqRXZ2Nhs3bmTBggVMnz6dvXv3Gp2eU3jDB8Zrr73G8uXLadWqFT179qR3794efe/lG8XFxTFw4ECGDh0KgMViIT4%2BnqlTp/Lhhx8am5yTeOOlTerXr8%2BUKVN47bXXqFmzJh9//DF%2Bfn433atY5E5U%2BAlbtmwhKSmJChUqYDKZ8PX1ZeLEibRs2dLo1Jzq9OnTfPbZZyQlJWE2m%2Bnbt6/RKTmNN3xg9OnThz59%2BrBz506WLVtGdHQ0V69eZefOnXTq1Ommizp7khMnTrB06VLrc5PJxPDhw2nWrJmBWTmXN17aZNKkSUyePJmcnBwmTJjA0KFDyc/PJy4uzujUxI2o8BOKioqsU2PXT/m8cczTrFy5kjVr1vDdd9/x6KOPMmHCBNq0aePRhYE3fWA0a9aMZs2acebMGZYvX85bb73FzJkz6dy5MxMnTjQ6PaeoVasW%2B/fvp3HjxtaxI0eOWC/m7Km8ZaX6s88%2By%2BLFi6lcuTKLFi0iPz%2BfJk2asGvXLsxmM/7%2B/kanKG5EizuE8ePH4%2Bvry5QpU2jVqhW7d%2B9m%2BvTpnDt3zmMudHujtm3b0r17d7p37%2B4VJ/3fSmFhodd8YBQUFLBu3TqWL1/O6tWrjU7HoRITEwFITU1l8%2BbN9OjRg%2BrVq5ORkcGqVato164dr7/%2BurFJOom3rFQHaNSokc1pKE2bNr3j1RhE7kSFn3D%2B/HmGDRvG4cOHuXr1Kn5%2BftSsWZOFCxd6ZGFksVi85tyvtWvX/uZrunbt6oJMxBl%2B6/QEk8nksffk9oaV6tf9uvCLiIjg22%2B/NTAjcWcq/AS4VgwdPHjQekHj%2BvXre9zU5/PPP8%2BiRYvo27fvbQs/T/uQjIqKuuN2k8nEl19%2B6aJsRBzHmy5too6fOJLO8RObvxyDg4MpLCxk7969%2BPr6UqlSJe655x4Ds3Oc6%2Bc/RUZGGpyJ62zevPmW41euXKF06dIuzkYcbf369XTs2PGOnV1P7eh6w0p1EWdQ4SdMnDiRtLQ0SpQoQcWKFfnll18oKiqiRIkSXL16lfvvv593333X7U8UHzJkCHDtFnWefI/aW0lLS%2BPFF1/ktddeo06dOvy///f/2L9/PwkJCQQHBxudnvyPFi5cSMeOHYmPj7/ldpPJ5LGFX7169Tx%2Bpfp1hYWFNsW92Wy%2Bqdj31O%2BzOJ6meoV58%2BaRlpbGlClTCAgIIDc3l7i4OKpVq0a/fv2YN28eqampLFy40OhUHaJBgwbUrFmTp556is6dO1O%2BfHmjU3K6IUOGEBQUxCuvvELZsmXJyspi7ty5XLx48bZFg7iHoqIiLly4QKVKlQDYuXMnKSkptGrVymMv3gyQkZHB5MmTefPNN0lNTbVZqd6pUyej03MonbIhjqTCT2jTpg0bN260WeGZl5dH%2B/bt2bp1K1euXKFFixYec05JdnY2SUlJrF27lqNHj/LYY4/Ro0cPj77mWdOmTdm%2BfTu%2Bvr7WsStXrtCyZUuSk5MNzEzskZ6ezqBBg6hfvz5xcXEkJSXx8ssvU7t2bVJTU1myZAn16tUzOk2HS0xM5NChQzz66KP06dMH8K6V6iL2KGF0AmK83NxcLl26ZDOWnZ3N5cuXrc89aRVsuXLl6N27N5988gmrV6%2BmevXqTJo0yeOu/XUjHx8fsrKybMYuXrzo0fdy9QZz586lVq1ajB8/HoCEhASee%2B45Vq9ezZQpU0hISDA4Q8ebOXMmy5cvx9fXl/j4eBYtWgRc%2BxlX0Sfy21T4CU888QQjRoxgx44dnDp1ih07djB69GjatWvH5cuXiYmJoUmTJkan6XC5ubl89913HDx4kIsXL1K/fn2jU3KaJ554gtGjR7Nz505OnTrFzp07eeGFF3j88ceNTk3ssH37diZPnkxQUBBpaWmkpqbSuXNn4Nr1Kvfv329who63fv16/vrXvxIfH098fDxJSUlGpyTiVrS4Q3jllVeYNm0aI0aMIC8vDz8/P3r06MG4ceM4dOgQly5d8qiLwO7YsYM1a9bwr3/9i%2BrVq9OjRw/mzp1LhQoVjE7NaSZMmMDUqVMZMmQIBQUFlCpViq5duzJ27FijUxM7XL582Xpu34EDByhfvjyhoaEAlC5dGrPZbGR6TpGdnU1YWBhwbaV%2Benq6wRmJuBcVfkLp0qWZOnUqU6ZM4cKFCwQFBVmndps0aeJx3b4RI0bQoUMHlixZQsOGDY1Ox%2BluPB/qjTfe4NKlSzbfY3FfFSpUICsri0qVKrF7924aNWpk3fbDDz9QsWJFA7NzjhIl/jtR5eOjjzCR30v/1wgA3333HT/%2B%2BCO/XuvjiZcI%2BPOf/8zEiRMpW7as0ak43cyZM1m7di1NmjQhPj6enJwcnn/%2BeaPTEgdp06YNsbGxREdHk5SURExMDACXLl1i3rx5tGjRwuAMHU/rEUXso1W9wpw5c3jvvfcICQmx%2BQvaUy8REBkZyY4dOzzuziS30rJlSxYvXkxYWBjJycm8%2BeabOifKg1y6dIkxY8awd%2B9eOnTowLRp0wAIDw8nJCSE5cuXe9x1GuvXr8/UqVOtz9944w1rwXudJ/7BKuIoKvyE1q1b88Ybb9CqVSujU3GJGTNmkJOTw5NPPklISIjNlGe1atUMzMzxwsPD2bdvH3Dtchd/%2BtOfPOayPHJ727ZtIyIiwiPvzqJr2onYR4WfEBERwe7du73mnK/atWtb/339mC0WCyaTiSNHjhiVllM0btyYPXv2WJ/rHp8iIt5N5/gJrVu3JikpyXoZCE/nTd0A/V0nIiI3UuEnXLlyhYkTJ7Jw4cKbzgf66KOPDMrKee6%2B%2B26jU3AZ3eNTRERupKleITEx8bbbRo4c6cJMXKN27dq3ndb2tKlenQ8lIiI3UuEnXufX57hlZWWxdOlSunTpQs%2BePQ3KSkRExPlU%2BAkAn3zyCUuXLiUjI4M1a9bw1ltvERcXR0BAgNGpuURmZiYDBgxgw4YNRqciIiLiNLpXr/Dhhx%2ByePFi%2Bvbty9WrVwkICCA9PZ24uDijU3OZ8uXL69ZPIiLi8dTxEx5//HEWLFhAaGio9XIfGRkZdOvWje3btxudnsP9enGD2Wzmyy%2B/JCcnh6VLlxqUlYiIiPNpVa/wyy%2B/cN999wH/vfxHUFAQhYWFRqblNPHx8TbPS5YsSWho6E1X/xcREfE0KvyE2rVrs2LFCv7yl79YV7tu3LiRsLAwgzNzvKKiIlatWkWlSpUA2LlzJykpKbRq1Yr777/f4OxEREScS1O9wqFDhxgwYAChoaF8//33NGvWjP379/P%2B%2B%2B/ToEEDo9NzmPT0dAYNGkT9%2BvWJi4sjKSmJl19%2Bmdq1a5OamsqSJUuoV6%2Be0WmKiIg4jQo/Aa4VRUlJSZw5c4YqVarQqVMnj7tv7cSJEykoKODVV18lKCiIdu3a0b59e8aOHcu6detYv349ixYtMjpNERERp1HhJwBcvXqVkiVLYrFY%2BPrrr6lYsSL169c3Oi2HatGiBZ999hmVKlUiLS2NqKgoNmzYQGhoKDk5ObRp00b3sRUREY%2Bmy7kImzdvpkWLFgC88847jBo1ir59%2B/LJJ58YnJljXb582Xpu34EDByhfvjyhoaEAlC5dGrPZbGR6IiIiTqfCT3jnnXcYM2YMRUVFLF26lISEBP72t7/x3nvvGZ2aQ1WoUIGsrCzg2t07GjVqZN32ww8/ULFiRaNSExERcQkVfkJqaio9e/YkJSWF/Px8mjdvTt26dTl37pzRqTlUmzZtiI2NZePGjSQlJdGhQwcALl26xLx586xdTxEREU%2Blwk/w9/fn/PnzbN68mcaNG%2BPj40NKSorHdcDGjh3LxYsXeeWVV3j88cfp1KkTAK1ateL48eOMGjXK4AxFREScS4s7hISEBD755BMuXbpEfHw8QUFBDB48mEGDBvH8888bnZ7Tbdu2jYiICEqXLm10KiIiIk6lwk8ASE5OpnTp0jRs2JCzZ89y8OBB2rVrZ3RaIiIi4kC6c4cAEBoaSnBwMAUFBWzZssXjpnlFREREhZ8AK1euZNq0aezfv59Zs2axceNGTCYTP/74I8OHDzc6PREREXEQLe4Qli1bxvz587l69SqrV68mISGBjz/%2B2OOu4yciIuLt1PETzp49S/Pmzdm7dy8%2BPj7W69tdunTJ4MxERETEkdTxEypUqMB//vMfPv/8c5o2bQrArl27CAkJMTgzERERcSR1/ISBAwdar2m3dOlS9uzZw5AhQ4iJiTE4MxEREXEkXc5FADh9%2BjQ%2BPj5UrVqVrKws0tLSqFu3rtFpiYiIiANpqlcACA4OxmQykZaWRn5%2BPuXKleOLL74wOi0RERFxIHX8hE8//ZTY2FiuXLliMx4UFMS2bdsMykpEREQcTef4CQsXLmTMmDEEBATw7bff0r9/f2bNmkXz5s2NTk1EREQcSFO9QmZmJv3796dZs2akpqZSp04dpk%2BfzsqVK41OTURERBxIhZ8QFBSE2WymatWq/PjjjwBUq1aN8%2BfPG5yZiIiIOJIKP6FevXpMmTKF/Px8atasyccff8yaNWsIDAw0OjURERFxIJ3jJ7zyyitMnjyZnJwcJkyYwNChQ8nPzycuLs7o1ERERMSBtKrXyyUmJnLo0CEeffRR%2BvTpA0BhYSFmsxl/f3%2BDsxMRERFH0lSvF5s5cybLly/H19eX%2BPh4Fi1aBICPj4%2BKPhEREQ%2Bkjp8Xa9myJYsXLyYsLIzk5GTefPNNkpKSjE5LREREnEQdPy%2BWnZ1NWFgYAI0bNyY9Pd3gjERERMSZVPh5sRIl/vvt9/HROh8RERFPp8LPi2mWX0RExLuozePFCgsLWbt2rfW52Wy2eQ7QtWtXV6clIiIiTqLFHV4sKirqjttNJhNffvmli7IRERERZ1PhJyIiIuIldI6fiIiIiJdQ4SciIiLiJVT4iYiIiHgJFX4iIiIiXkKFn4iIiIiXUOEnIiIi4iVU%2BImIiIh4CRV%2BIiIiIl7i/wM1tIxU/bh5HgAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x7fbd3065d630>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_profiling.ProfileReport(titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Want more tricks? Watch [21 more pandas tricks](https://www.youtube.com/watch?v=tWFQqaRtSQA&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=36) or [Read the notebook](https://nbviewer.org/github/justmarkham/pandas-videos/blob/master/21_more_pandas_tricks.ipynb)\n",
    "\n",
    "© 2019 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}