Data Science Tools- Pandas Summary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Science Tools\n",
    "### Pandas Summary \n",
    "+ An extension to pandas dataframes describe function.\n",
    "\n",
    "#### Installation\n",
    "+ pip install pandas-summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Pkgs\n",
    "import pandas as pd\n",
    "import pandas_summary as ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "df = pd.read_csv(\"iris.csv\")"
   ]
  },
  {
   "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",
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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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive Stats\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>150.0</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.80</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>150.0</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count      mean       std  min  25%   50%  75%  max\n",
       "sepal_length  150.0  5.843333  0.828066  4.3  5.1  5.80  6.4  7.9\n",
       "sepal_width   150.0  3.054000  0.433594  2.0  2.8  3.00  3.3  4.4\n",
       "petal_length  150.0  3.758667  1.764420  1.0  1.6  4.35  5.1  6.9\n",
       "petal_width   150.0  1.198667  0.763161  0.1  0.3  1.30  1.8  2.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DataFrameSummary',\n",
       " 'OrderedDict',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__warningregistry__',\n",
       " 'division',\n",
       " 'np',\n",
       " 'pd',\n",
       " 'string_types',\n",
       " 'types']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Features of Pandas Summary\n",
    "dir(ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize DataFrameSummary/usage\n",
    "dfs = ps.DataFrameSummary(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALL',\n",
       " 'EXCLUDE',\n",
       " 'INCLUDE',\n",
       " 'TYPE_BOOL',\n",
       " 'TYPE_CATEGORICAL',\n",
       " 'TYPE_CONSTANT',\n",
       " 'TYPE_DATE',\n",
       " 'TYPE_NUMERIC',\n",
       " 'TYPE_UNIQUE',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_clean_column',\n",
       " '_get_bool_summary',\n",
       " '_get_categorical_summary',\n",
       " '_get_column_summary',\n",
       " '_get_columns_info',\n",
       " '_get_constant_summary',\n",
       " '_get_date_summary',\n",
       " '_get_deviation_of_mean',\n",
       " '_get_median_absolute_deviation',\n",
       " '_get_missing',\n",
       " '_get_numeric_summary',\n",
       " '_get_stats',\n",
       " '_get_top_correlations',\n",
       " '_get_unique_summary',\n",
       " '_get_uniques',\n",
       " '_number_format',\n",
       " '_percent',\n",
       " 'bools',\n",
       " 'categoricals',\n",
       " 'columns_stats',\n",
       " 'columns_types',\n",
       " 'constants',\n",
       " 'corr',\n",
       " 'df',\n",
       " 'get_columns',\n",
       " 'length',\n",
       " 'missing_frac',\n",
       " 'numerics',\n",
       " 'summary',\n",
       " 'uniques']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dfs \n",
    "dir(dfs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numeric        4\n",
       "categorical    1\n",
       "Name: types, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Column Data Types\n",
    "dfs.columns_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['species'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To Find Categorical Column\n",
    "dfs.categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To Find Numerical columns\n",
    "dfs.numerics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>counts</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniques</th>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>43</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing_perc</th>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>types</th>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sepal_length sepal_width petal_length petal_width      species\n",
       "counts                150         150          150         150          150\n",
       "uniques                35          23           43          22            3\n",
       "missing                 0           0            0           0            0\n",
       "missing_perc           0%          0%           0%          0%           0%\n",
       "types             numeric     numeric      numeric     numeric  categorical"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column Statistics \n",
    "# Similar to df.describe()\n",
    "dfs.columns_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.109369      0.871754     0.817954\n",
       "sepal_width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "petal_length      0.871754    -0.420516      1.000000     0.962757\n",
       "petal_width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation\n",
    "dfs.corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.84333</td>\n",
       "      <td>3.054</td>\n",
       "      <td>3.75867</td>\n",
       "      <td>1.19867</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.76442</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.8</td>\n",
       "      <td>3</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>counts</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniques</th>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>43</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing_perc</th>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>types</th>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sepal_length sepal_width petal_length petal_width      species\n",
       "count                 150         150          150         150          NaN\n",
       "mean              5.84333       3.054      3.75867     1.19867          NaN\n",
       "std              0.828066    0.433594      1.76442    0.763161          NaN\n",
       "min                   4.3           2            1         0.1          NaN\n",
       "25%                   5.1         2.8          1.6         0.3          NaN\n",
       "50%                   5.8           3         4.35         1.3          NaN\n",
       "75%                   6.4         3.3          5.1         1.8          NaN\n",
       "max                   7.9         4.4          6.9         2.5          NaN\n",
       "counts                150         150          150         150          150\n",
       "uniques                35          23           43          22            3\n",
       "missing                 0           0            0           0            0\n",
       "missing_perc           0%          0%           0%          0%           0%\n",
       "types             numeric     numeric      numeric     numeric  categorical"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary \n",
    "# Similar to df.describe plus more\n",
    "dfs.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean                                                          5.84333\n",
       "std                                                          0.828066\n",
       "variance                                                     0.685694\n",
       "min                                                               4.3\n",
       "max                                                               7.9\n",
       "5%                                                                4.6\n",
       "25%                                                               5.1\n",
       "50%                                                               5.8\n",
       "75%                                                               6.4\n",
       "95%                                                             7.255\n",
       "iqr                                                               1.3\n",
       "kurtosis                                                    -0.552064\n",
       "skewness                                                     0.314911\n",
       "sum                                                             876.5\n",
       "mad                                                          0.687556\n",
       "cv                                                           0.141711\n",
       "zeros_num                                                           0\n",
       "zeros_perc                                                         0%\n",
       "deviating_of_mean                                                   0\n",
       "deviating_of_mean_perc                                             0%\n",
       "deviating_of_median                                                 1\n",
       "deviating_of_median_perc                                        0.67%\n",
       "top_correlations            petal_length: 87.18%, petal_width: 81.80%\n",
       "counts                                                            150\n",
       "uniques                                                            35\n",
       "missing                                                             0\n",
       "missing_perc                                                       0%\n",
       "types                                                         numeric\n",
       "Name: sepal_length, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d57f0b9e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Summary of A Column\n",
    "dfs['sepal_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}