Lecture2_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the Exploratory Data Analysis (EDA)? \n",
    " \n",
    "`What you should do when a new, unknown dataset is given.`\n",
    "\n",
    "Here we are going to review the most important techniques in order to perform a successful exploration of data. If you get meaningful insights, it often means that you even have a good advantage over other competitors. If the intuition about data contnent is clear it will be easier to develop appropriate hypotesis with which buil a competitive model. A good EDA can help you to decide in which way you should spent more or less time in preprocessing, modeling ecc.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One important conterpart is data visualization. If there is a pattern in the data it can be found, with some good luck, with an appropriate visualization. Here we will show a few visualization tecniques, EDA oriented. It means that those visualization will be mid-quality but very easy to implement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually, we don't need to go too deep inside the competition subject but it's preferable to understand what our aim is, what data we have, and how people usually build a baseline.\n",
    "\n",
    "Given a problem which you know nothing about a good strategy is to make up our mind and know what we should do. Mostly, we don't need to know that much about the topic. Just a couple of tips: \n",
    "\n",
    "1. search other people solution to similar problems\n",
    "2. googling and searching on wikipedia, to get the features meaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook structure:\n",
    "\n",
    "The notebook is divided in three part, one for each case study.\n",
    "\n",
    "1. Is the number of our feature manageable? If the answer is yes we can prceed with some plot like histograms and scatterplot. Furthermore we can create some new features from scratch and understand something more about dataset structure. \n",
    "\n",
    "2. Here we will do some as dirty as useful work. If data are anonymized (or, more technically speaking, encoded) how can we decode them? Can we understand if there are more important features than other?\n",
    "\n",
    "3. Some good practices, that will help you to improve the quality of your dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disclaimer\n",
    "\n",
    "Keep in mind that EDA is not a straight procedure. You will never be given a recipe or a list of steps to follow. Here we are in the realm of intuition and the reader should interpretate this notebook as a collection of tips, tecniques, good practices.\n",
    "\n",
    "Some lines of code are not extensively explained and some arguments may seem dead leaf.. they are suggestions and is up to you to devlop what you may fin interesting. \n",
    "\n",
    "We strongly ancourage to use your creativity!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LET'S START: CASE STUDY 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will never use just one library, but rather a combination multiple libraries.\n",
    "Be bright, and develop your intuition about how to combine at least the most famous python libraries.\n",
    "\n",
    "We are going to extensively use *Pandas*. If you would like to do a brief recap of *Pandas* basic functions see:\n",
    "\n",
    "- https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html\n",
    "- https://www.geeksforgeeks.org/introduction-to-pandas-in-python/\n",
    "- https://www.geeksforgeeks.org/python-pandas-series/\n",
    "- https://www.geeksforgeeks.org/python-pandas-dataframe/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we set an environment variable, path that will tel the computer where data are stored.\n",
    "\n",
    "Train and test are stored as variables through two pandas objects, namely dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://www.kaggle.com/fayomi/advertising/download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ex my path was ---> '/home/jacopo/Desktop/MLJC/PROX/data'\n",
    "DATA_FOLDER = # your path here\n",
    "adv = pd.read_csv(os.path.join(DATA_FOLDER, 'advertising.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's give a look at the data, and check what the main features are.\n",
    "This step may be very helpful, because if we already know the research field features are about we could start to figure out how to go on with our analysis. It often useful to give to the same question different answers ie understand different commands can help us to solve tha same problem. Maybe, sometimes further, this little differencies in the output will be helpful!\n",
    "\n",
    "What can we expect to see when exploring the basic features of a dataframe?\n",
    "How many entries are there in the dataset?\n",
    "Which features are contained?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If the dataframe were a matrix, how many rows and columns would have? (1000, 10)\n",
      "\n",
      " *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *   \n",
      "\n",
      "How long the dataset is? \n",
      " RangeIndex(start=0, stop=1000, step=1)\n",
      "\n",
      " *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *   \n",
      "\n",
      " Index(['Daily Time Spent on Site', 'Age', 'Area Income',\n",
      "       'Daily Internet Usage', 'Ad Topic Line', 'City', 'Male', 'Country',\n",
      "       'Timestamp', 'Clicked on Ad'],\n",
      "      dtype='object')\n",
      "\n",
      " *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *  *   \n",
      "\n",
      "Daily Time Spent on Site    float64\n",
      "Age                           int64\n",
      "Area Income                 float64\n",
      "Daily Internet Usage        float64\n",
      "Ad Topic Line                object\n",
      "City                         object\n",
      "Male                          int64\n",
      "Country                      object\n",
      "Timestamp                    object\n",
      "Clicked on Ad                 int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"If the dataframe were a matrix, how many rows and columns would have?\",adv.shape)\n",
    "\n",
    "print('\\n',\"*  \"*30,'\\n')\n",
    "\n",
    "print(\"How long the dataset is? \\n\",adv.index)\n",
    "\n",
    "print('\\n',\"*  \"*30,'\\n')\n",
    "\n",
    "print(\"\",adv.columns)\n",
    "\n",
    "print('\\n',\"*  \"*30,'\\n')\n",
    "\n",
    "print(adv.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can have easier access, in the format `dataframe.featurename` if we slightly change the column names removing all the spaces in their nam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = list(adv.columns)\n",
    "\n",
    "for f in features:\n",
    "    \n",
    "    phi = f.replace(' ','')\n",
    "    adv = adv.rename(columns = {f:phi})\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The follwoing command just shows the first rows of our dataset. In the first column we have the features names. The remaining columns are the entries of the dataset."
   ]
  },
  {
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>DailyTimeSpentonSite</td>\n",
       "      <td>68.95</td>\n",
       "      <td>80.23</td>\n",
       "      <td>69.47</td>\n",
       "      <td>74.15</td>\n",
       "      <td>68.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AreaIncome</td>\n",
       "      <td>61833.9</td>\n",
       "      <td>68441.9</td>\n",
       "      <td>59785.9</td>\n",
       "      <td>54806.2</td>\n",
       "      <td>73890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DailyInternetUsage</td>\n",
       "      <td>256.09</td>\n",
       "      <td>193.77</td>\n",
       "      <td>236.5</td>\n",
       "      <td>245.89</td>\n",
       "      <td>225.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AdTopicLine</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Triple-buffered reciprocal time-frame</td>\n",
       "      <td>Robust logistical utilization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>City</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>West Terrifurt</td>\n",
       "      <td>South Manuel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Country</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Timestamp</td>\n",
       "      <td>2016-03-27 00:53:11</td>\n",
       "      <td>2016-04-04 01:39:02</td>\n",
       "      <td>2016-03-13 20:35:42</td>\n",
       "      <td>2016-01-10 02:31:19</td>\n",
       "      <td>2016-06-03 03:36:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ClickedonAd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       0  \\\n",
       "DailyTimeSpentonSite                               68.95   \n",
       "Age                                                   35   \n",
       "AreaIncome                                       61833.9   \n",
       "DailyInternetUsage                                256.09   \n",
       "AdTopicLine           Cloned 5thgeneration orchestration   \n",
       "City                                         Wrightburgh   \n",
       "Male                                                   0   \n",
       "Country                                          Tunisia   \n",
       "Timestamp                            2016-03-27 00:53:11   \n",
       "ClickedonAd                                            0   \n",
       "\n",
       "                                                       1  \\\n",
       "DailyTimeSpentonSite                               80.23   \n",
       "Age                                                   31   \n",
       "AreaIncome                                       68441.9   \n",
       "DailyInternetUsage                                193.77   \n",
       "AdTopicLine           Monitored national standardization   \n",
       "City                                           West Jodi   \n",
       "Male                                                   1   \n",
       "Country                                            Nauru   \n",
       "Timestamp                            2016-04-04 01:39:02   \n",
       "ClickedonAd                                            0   \n",
       "\n",
       "                                                     2  \\\n",
       "DailyTimeSpentonSite                             69.47   \n",
       "Age                                                 26   \n",
       "AreaIncome                                     59785.9   \n",
       "DailyInternetUsage                               236.5   \n",
       "AdTopicLine           Organic bottom-line service-desk   \n",
       "City                                          Davidton   \n",
       "Male                                                 0   \n",
       "Country                                     San Marino   \n",
       "Timestamp                          2016-03-13 20:35:42   \n",
       "ClickedonAd                                          0   \n",
       "\n",
       "                                                          3  \\\n",
       "DailyTimeSpentonSite                                  74.15   \n",
       "Age                                                      29   \n",
       "AreaIncome                                          54806.2   \n",
       "DailyInternetUsage                                   245.89   \n",
       "AdTopicLine           Triple-buffered reciprocal time-frame   \n",
       "City                                         West Terrifurt   \n",
       "Male                                                      1   \n",
       "Country                                               Italy   \n",
       "Timestamp                               2016-01-10 02:31:19   \n",
       "ClickedonAd                                               0   \n",
       "\n",
       "                                                  4  \n",
       "DailyTimeSpentonSite                          68.37  \n",
       "Age                                              35  \n",
       "AreaIncome                                    73890  \n",
       "DailyInternetUsage                           225.58  \n",
       "AdTopicLine           Robust logistical utilization  \n",
       "City                                   South Manuel  \n",
       "Male                                              0  \n",
       "Country                                     Iceland  \n",
       "Timestamp                       2016-06-03 03:36:18  \n",
       "ClickedonAd                                       0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adv.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another important dataframe method os the info, which summarize tha dataframe content.\n",
    "With this command we can even see that there are not missing values in the data.. it's a lot less work to do for us!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      "DailyTimeSpentonSite    1000 non-null float64\n",
      "Age                     1000 non-null int64\n",
      "AreaIncome              1000 non-null float64\n",
      "DailyInternetUsage      1000 non-null float64\n",
      "AdTopicLine             1000 non-null object\n",
      "City                    1000 non-null object\n",
      "Male                    1000 non-null int64\n",
      "Country                 1000 non-null object\n",
      "Timestamp               1000 non-null object\n",
      "ClickedonAd             1000 non-null int64\n",
      "dtypes: float64(3), int64(3), object(4)\n",
      "memory usage: 78.2+ KB\n"
     ]
    }
   ],
   "source": [
    "adv.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's focus on a single feature, *Timestamp*. It contains the date and time of the day when the entry was recorded. But it is obvious that feature type is something like: *date&hour*. It is likely that, if we split this one in two new features, like *day* and *hour* we may be able to observe different kind of pattern occuring in the two new features. With a physics analogy, it is like if we are separating the different scales of our phenomena. First, we give a look at this *Mother* feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      2016-03-27 00:53:11\n",
      "1      2016-04-04 01:39:02\n",
      "2      2016-03-13 20:35:42\n",
      "3      2016-01-10 02:31:19\n",
      "4      2016-06-03 03:36:18\n",
      "              ...         \n",
      "995    2016-02-11 21:49:00\n",
      "996    2016-04-22 02:07:01\n",
      "997    2016-02-01 17:24:57\n",
      "998    2016-03-24 02:35:54\n",
      "999    2016-06-03 21:43:21\n",
      "Name: Timestamp, Length: 1000, dtype: object\n",
      "\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print ( adv.Timestamp )\n",
    "print()\n",
    "print( type(adv.Timestamp[0]) )"
   ]
  },
  {
   "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",
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>DailyTimeSpentonSite</td>\n",
       "      <td>68.95</td>\n",
       "      <td>80.23</td>\n",
       "      <td>69.47</td>\n",
       "      <td>74.15</td>\n",
       "      <td>68.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AreaIncome</td>\n",
       "      <td>61833.9</td>\n",
       "      <td>68441.9</td>\n",
       "      <td>59785.9</td>\n",
       "      <td>54806.2</td>\n",
       "      <td>73890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DailyInternetUsage</td>\n",
       "      <td>256.09</td>\n",
       "      <td>193.77</td>\n",
       "      <td>236.5</td>\n",
       "      <td>245.89</td>\n",
       "      <td>225.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AdTopicLine</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Triple-buffered reciprocal time-frame</td>\n",
       "      <td>Robust logistical utilization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>City</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>West Terrifurt</td>\n",
       "      <td>South Manuel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Country</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Timestamp</td>\n",
       "      <td>2016-03-27 00:53:11</td>\n",
       "      <td>2016-04-04 01:39:02</td>\n",
       "      <td>2016-03-13 20:35:42</td>\n",
       "      <td>2016-01-10 02:31:19</td>\n",
       "      <td>2016-06-03 03:36:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ClickedonAd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       0  \\\n",
       "DailyTimeSpentonSite                               68.95   \n",
       "Age                                                   35   \n",
       "AreaIncome                                       61833.9   \n",
       "DailyInternetUsage                                256.09   \n",
       "AdTopicLine           Cloned 5thgeneration orchestration   \n",
       "City                                         Wrightburgh   \n",
       "Male                                                   0   \n",
       "Country                                          Tunisia   \n",
       "Timestamp                            2016-03-27 00:53:11   \n",
       "ClickedonAd                                            0   \n",
       "\n",
       "                                                       1  \\\n",
       "DailyTimeSpentonSite                               80.23   \n",
       "Age                                                   31   \n",
       "AreaIncome                                       68441.9   \n",
       "DailyInternetUsage                                193.77   \n",
       "AdTopicLine           Monitored national standardization   \n",
       "City                                           West Jodi   \n",
       "Male                                                   1   \n",
       "Country                                            Nauru   \n",
       "Timestamp                            2016-04-04 01:39:02   \n",
       "ClickedonAd                                            0   \n",
       "\n",
       "                                                     2  \\\n",
       "DailyTimeSpentonSite                             69.47   \n",
       "Age                                                 26   \n",
       "AreaIncome                                     59785.9   \n",
       "DailyInternetUsage                               236.5   \n",
       "AdTopicLine           Organic bottom-line service-desk   \n",
       "City                                          Davidton   \n",
       "Male                                                 0   \n",
       "Country                                     San Marino   \n",
       "Timestamp                          2016-03-13 20:35:42   \n",
       "ClickedonAd                                          0   \n",
       "\n",
       "                                                          3  \\\n",
       "DailyTimeSpentonSite                                  74.15   \n",
       "Age                                                      29   \n",
       "AreaIncome                                          54806.2   \n",
       "DailyInternetUsage                                   245.89   \n",
       "AdTopicLine           Triple-buffered reciprocal time-frame   \n",
       "City                                         West Terrifurt   \n",
       "Male                                                      1   \n",
       "Country                                               Italy   \n",
       "Timestamp                               2016-01-10 02:31:19   \n",
       "ClickedonAd                                               0   \n",
       "\n",
       "                                                  4  \n",
       "DailyTimeSpentonSite                          68.37  \n",
       "Age                                              35  \n",
       "AreaIncome                                    73890  \n",
       "DailyInternetUsage                           225.58  \n",
       "AdTopicLine           Robust logistical utilization  \n",
       "City                                   South Manuel  \n",
       "Male                                              0  \n",
       "Country                                     Iceland  \n",
       "Timestamp                       2016-06-03 03:36:18  \n",
       "ClickedonAd                                       0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adv.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the first things to do "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "sns.distplot(adv['Age'], hist=False, color='r', rug=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff64bdd7c90>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.kdeplot(adv.Age, adv.DailyTimeSpentonSite, color=\"b\", ax=ax)\n",
    "sns.rugplot(adv.Age, color=\"r\", ax=ax)\n",
    "sns.rugplot(adv.DailyTimeSpentonSite, vertical=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7ff64bc52150>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"iris\")\n",
    "sns.pairplot(df, hue=\"species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b7bfd90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b5f3410>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b55a790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b50bf90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b4cc7d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b480fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b441810>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b473f90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b47eb90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b43d550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b3aa890>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b369c10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b3218d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b2dfc50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b295910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff64b253c90>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(adv[['DailyTimeSpentonSite', 'Age','AreaIncome', 'DailyInternetUsage']], alpha=0.3, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>AdTopicLine</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>1000</td>\n",
       "      <td>969</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>Vision-oriented system-worthy forecast</td>\n",
       "      <td>Lisamouth</td>\n",
       "      <td>Czech Republic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   AdTopicLine       City         Country\n",
       "count                                     1000       1000            1000\n",
       "unique                                    1000        969             237\n",
       "top     Vision-oriented system-worthy forecast  Lisamouth  Czech Republic\n",
       "freq                                         1          3               9"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_features = ['AdTopicLine', 'City', 'Country']\n",
    "adv[object_features].describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the table above that all the values in column \"Ad Topic Line\" is unique, while the \"City\" column contains 969 unique values out of 1000. There are too many unique elements within these two categorical columns and it is generally difficult to perform a prediction without the existence of a data pattern. Because of that, they will be omitted from further analysis. The third categorical variable, i.e \"Country\", has a unique element (France) that repeats 9 times. Additionally, we can determine countries with the highest number of visitors:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table below shows the 20 most represented countries in our DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>France</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Turkey</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>South Africa</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Senegal</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Peru</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Micronesia</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Greece</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cyprus</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Liberia</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Taiwan</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bahamas</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Burundi</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cambodia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Venezuela</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fiji</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                   count\n",
       "Country                      \n",
       "France                      9\n",
       "Czech Republic              9\n",
       "Afghanistan                 8\n",
       "Australia                   8\n",
       "Turkey                      8\n",
       "South Africa                8\n",
       "Senegal                     8\n",
       "Peru                        8\n",
       "Micronesia                  8\n",
       "Greece                      8\n",
       "Cyprus                      8\n",
       "Liberia                     8\n",
       "Albania                     7\n",
       "Bosnia and Herzegovina      7\n",
       "Taiwan                      7\n",
       "Bahamas                     7\n",
       "Burundi                     7\n",
       "Cambodia                    7\n",
       "Venezuela                   7\n",
       "Fiji                        7"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=adv['Country'], columns='count').sort_values(['count'], ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have already seen, there are 237 different unique countries in our dataset and no single country is too dominant. A large number of unique elements will not allow a model to establish valuable relationships. For that reason, this variable will be excluded too. It is too difficult to learn from (almost) flat distribution that is almost equivalent to white noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = adv.drop(['AdTopicLine', 'City', 'Country'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our purpose now is to create three new features because we are splitting one column in three columns. If we split features and properly operate on them we can get something useful. We are going to get two tipes for our feature: two with datetime, one with categorical. \n",
    "\n",
    "The \"Day of the week\" variable contains values from 0 to 6, where each number represents a specific day of the week (from Monday to Sunday). The categories here are the days of the week, which may be useful to detect periodic pattern in our data.\n",
    "\n",
    "All the other variables are in dtime format.\n",
    "\n",
    "1. convert the timestamp column (string format) into pandas datetime format\n",
    "2. trought the dt accessor split time and date and day of the week and assign them to new columns\n",
    "3. remove (drop) the timestamp column. There is no information loss here\n",
    "4. check the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "adv['Timestamp'] = pd.to_datetime(adv['Timestamp'])\n",
    "\n",
    "adv['WDay']  = adv['Timestamp'].dt.weekday\n",
    "adv['Day']   = adv['Timestamp'].dt.date\n",
    "adv['Hour']  = adv['Timestamp'].dt.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "adv = adv.drop(['Timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate how much time our dataset spans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train min/max date: 2016-01-01 2016-07-24\n",
      "Dataset time span:  205 days, 0:00:00\n"
     ]
    }
   ],
   "source": [
    "print('Train min/max date:', adv.Day.min(), adv.Day.max() )\n",
    "print('Dataset time span: ',adv.Day.max()-adv.Day.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, one very simple histogram: which day f the week carry us more information? No one is more informative than the other: the plot is practically flat, meaning uniform sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff64ad44890>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adv['WDay'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trick: there is no direct way to visualize a dt.time column in a histogram format. But if we perform the right transformation of our data, we can easily visualize our data, even in a dirty format. Here what we want to obtain is just the shape of the distribution, something that can help us to decide how to proceed in our EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45695a7810>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A66rqvmnXMw2zHPxODTGoqi3D6zbgakbDYL26N8kqgOF125TrmYqqureqHqyqnwOX08lnIslhjEL/I1X16aG5u8/ELAe/U0MASR6T5KjFZeA5wG173mqmXQusHZbXAtdMsZapWQy6wYvo4DORJMAVwOaqeteSb3X3mZjpO3eHS9TezUNTQ7x9yiVNXJInMurlw2iKjo/2chySfAx4OqNpd+8F3gJ8BrgKWA3cDVxYVTN94nM3x+HpjIZ5CrgLeNXiOPesSvI04N+AW4GfD82XMhrn7+szMcvBL0na1SwP9UiSlmHwS1JnDH5J6ozBL0mdMfglqTMGvyR1xuCXpM78P4dcCUUFNN8TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adv[\"NHour\"] = adv[\"Hour\"].astype(str)\n",
    "adv['NHour'] = adv['NHour'].map(lambda x: int(x.replace(':','')))\n",
    "adv[\"NHour\"].plot.hist(bins=100, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even if the plots are not informative, we can conclude that the sample is uniform and that we should do some feature engineering in order to extract meaningful informations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE ART OF FEATURE DECODING: CASE STUDY 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous axemple we were working with data had a clear meaning. We knew what the features were and ment and data were given to us without particolar modifications. In daily life this setting is not the rule, but the exception: often data are algorithmically encoded in order to be anonymized.\n",
    "If one data provider wants to anonymize geolocalization data it could replace the gps coordinates of each entry with hash values and so on\n",
    "\n",
    "If the applied encoding is one-to-one the features are preserved and the models will be able to to learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First of all, download the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://habrastorage.org/storage/stuff/special/beeline/00.beeline_bigdata.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature names are replaced with some aliases x1, x2, ... and one can observe some hash values. This is a very common technique to anonymize data and this hide the information that need to be retrived. \n",
    "\n",
    "### First we can try to decode or de-anonymize data. \n",
    "\n",
    "Guessing the meaning of the data is allways a good starting point. Deanonymization is not allways possible but we can do this to reconstruct the type of the features, separating them in numeric, categorical ecc..\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.541</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.782346</td>\n",
       "      <td>1.322409</td>\n",
       "      <td>0.011647</td>\n",
       "      <td>0.397671</td>\n",
       "      <td>0.239601</td>\n",
       "      <td>0.249584</td>\n",
       "      <td>0.068220</td>\n",
       "      <td>0.033278</td>\n",
       "      <td>601.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>190436e528</td>\n",
       "      <td>16a14a2d17</td>\n",
       "      <td>06330986ed</td>\n",
       "      <td>ca63304de0</td>\n",
       "      <td>b7584c2d52</td>\n",
       "      <td>1746600cb0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.437655</td>\n",
       "      <td>5624b8f759</td>\n",
       "      <td>152af2cb2f</td>\n",
       "      <td>91bb549494</td>\n",
       "      <td>e33c63cf35</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>cc69cbe29a</td>\n",
       "      <td>617a4ad3f9</td>\n",
       "      <td>e8a040423a</td>\n",
       "      <td>c82c3dbd33</td>\n",
       "      <td>ee3501282b</td>\n",
       "      <td>199ce7c484</td>\n",
       "      <td>5f17dedd5c</td>\n",
       "      <td>5c5025bd0a</td>\n",
       "      <td>9aba4d7f51</td>\n",
       "      <td>24.943933</td>\n",
       "      <td>-0.814660</td>\n",
       "      <td>-0.708308</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>-0.512422</td>\n",
       "      <td>-0.733967</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>14.837728</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.835</td>\n",
       "      <td>-0.586540</td>\n",
       "      <td>0.672436</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606061</td>\n",
       "      <td>0.121212</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>43859085bc</td>\n",
       "      <td>16a14a2d17</td>\n",
       "      <td>06330986ed</td>\n",
       "      <td>ca63304de0</td>\n",
       "      <td>a62168d626</td>\n",
       "      <td>1746600cb0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004439</td>\n",
       "      <td>f67f142e40</td>\n",
       "      <td>c4dd2197c3</td>\n",
       "      <td>91bb549494</td>\n",
       "      <td>e33c63cf35</td>\n",
       "      <td>14559.0</td>\n",
       "      <td>6e40247e69</td>\n",
       "      <td>617a4ad3f9</td>\n",
       "      <td>718c61545b</td>\n",
       "      <td>c26d08129a</td>\n",
       "      <td>9e166b965d</td>\n",
       "      <td>466f8951b0</td>\n",
       "      <td>fde72a6d5c</td>\n",
       "      <td>acfadc5c01</td>\n",
       "      <td>9aba4d7f51</td>\n",
       "      <td>41.576860</td>\n",
       "      <td>-0.907833</td>\n",
       "      <td>-0.761736</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.627525</td>\n",
       "      <td>-0.805801</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.92</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.600326</td>\n",
       "      <td>-1.838680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>a4c3095b75</td>\n",
       "      <td>16a14a2d17</td>\n",
       "      <td>06330986ed</td>\n",
       "      <td>ca63304de0</td>\n",
       "      <td>b7584c2d52</td>\n",
       "      <td>1746600cb0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.480977</td>\n",
       "      <td>7e5c97705a</td>\n",
       "      <td>e071d01df5</td>\n",
       "      <td>91bb549494</td>\n",
       "      <td>e33c63cf35</td>\n",
       "      <td>5777.0</td>\n",
       "      <td>6e40247e69</td>\n",
       "      <td>617a4ad3f9</td>\n",
       "      <td>4b9480aa42</td>\n",
       "      <td>e84655292c</td>\n",
       "      <td>527b6ca8cc</td>\n",
       "      <td>dd9c9e0da2</td>\n",
       "      <td>17c99905b6</td>\n",
       "      <td>0fc56ea1f0</td>\n",
       "      <td>9aba4d7f51</td>\n",
       "      <td>31.080282</td>\n",
       "      <td>-0.371787</td>\n",
       "      <td>-0.367616</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.271307</td>\n",
       "      <td>0.013112</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>1713.439128</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>8.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.032</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.517</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.960991</td>\n",
       "      <td>0.790990</td>\n",
       "      <td>0.020161</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.036290</td>\n",
       "      <td>0.040323</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>248.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x0          x1          x2          x3          x4          x5  x6  \\\n",
       "0  b4d8a653ea  16a14a2d17  06330986ed  ca63304de0  a62168d626  1746600cb0   1   \n",
       "1  467f9617a3  16a14a2d17  06330986ed  ca63304de0  b7584c2d52  1746600cb0   1   \n",
       "2  190436e528  16a14a2d17  06330986ed  ca63304de0  b7584c2d52  1746600cb0   1   \n",
       "3  43859085bc  16a14a2d17  06330986ed  ca63304de0  a62168d626  1746600cb0   1   \n",
       "4  a4c3095b75  16a14a2d17  06330986ed  ca63304de0  b7584c2d52  1746600cb0   1   \n",
       "\n",
       "   x7        x8          x9         x10         x11         x12      x13  \\\n",
       "0   1 -0.688706  7e5c97705a  e5df3eff9b  91bb549494  e33c63cf35   3694.0   \n",
       "1   1  0.870871  5624b8f759  fa0b797a92  669ea3d319  f178803074  18156.0   \n",
       "2   1  0.437655  5624b8f759  152af2cb2f  91bb549494  e33c63cf35   1178.0   \n",
       "3   1  0.004439  f67f142e40  c4dd2197c3  91bb549494  e33c63cf35  14559.0   \n",
       "4   1  0.480977  7e5c97705a  e071d01df5  91bb549494  e33c63cf35   5777.0   \n",
       "\n",
       "          x14         x15         x16         x17         x18         x19  \\\n",
       "0  6e40247e69  617a4ad3f9  718c61545b  c26d08129a  634e3cf3ac  dd9c9e0da2   \n",
       "1  01ede04b4b  617a4ad3f9  718c61545b  d342e2765f  bb20e1ca06  8a6c8cef83   \n",
       "2  cc69cbe29a  617a4ad3f9  e8a040423a  c82c3dbd33  ee3501282b  199ce7c484   \n",
       "3  6e40247e69  617a4ad3f9  718c61545b  c26d08129a  9e166b965d  466f8951b0   \n",
       "4  6e40247e69  617a4ad3f9  4b9480aa42  e84655292c  527b6ca8cc  dd9c9e0da2   \n",
       "\n",
       "          x20         x21         x22        x23       x24       x25  \\\n",
       "0  17c99905b6  513a3e3f36  9aba4d7f51  40.579612 -0.112693 -0.172191   \n",
       "1  1b02793146  992153ed65  9aba4d7f51  28.765503  2.612285  2.159091   \n",
       "2  5f17dedd5c  5c5025bd0a  9aba4d7f51  24.943933 -0.814660 -0.708308   \n",
       "3  fde72a6d5c  acfadc5c01  9aba4d7f51  41.576860 -0.907833 -0.761736   \n",
       "4  17c99905b6  0fc56ea1f0  9aba4d7f51  31.080282 -0.371787 -0.367616   \n",
       "\n",
       "        x26       x27       x28        x29          x30    x31       x32  \\\n",
       "0  1.166667  1.674538  0.630889  37.000000     1.294922   55.0  0.166667   \n",
       "1  4.000000  1.710714  1.713538   0.166667     0.027669  109.0  0.000000   \n",
       "2  1.500000 -0.512422 -0.733967   0.333333    14.837728   11.0  0.000000   \n",
       "3  0.500000 -0.627525 -0.805801   1.166667     0.004395    0.0  0.500000   \n",
       "4  1.666667  0.271307  0.013112  17.333333  1713.439128   33.0  0.000000   \n",
       "\n",
       "    x33  x34       x35  x36    x37  x38  x39   x40    x41   x42    x43  x44  \\\n",
       "0  10.0  0.0  0.000000  1.0    9.0  0.0  1.0  23.0   3.67  0.12  1.935  2.2   \n",
       "1  31.0  0.0  0.000000  1.0  244.0  1.0  1.0  68.0  17.25  0.57  3.452  4.0   \n",
       "2  24.0  0.0  0.000000  1.0   29.0  0.0  3.0  11.0   4.42  0.15  0.161  0.2   \n",
       "3   0.0  0.0  0.000000  7.0    7.0  0.0  3.0  15.0   8.92  0.29  0.226  0.8   \n",
       "4   6.0  1.0  0.666667  8.0  108.0  1.0  4.0  86.0   1.58  0.05  2.032  2.4   \n",
       "\n",
       "     x45    x46    x47    x48    x49    x50    x51    x52       x53       x54  \\\n",
       "0  0.625  0.250  0.125  0.000  0.813  0.074  0.634  0.548  0.235333  0.264952   \n",
       "1  0.409  0.619  0.579  0.248  0.346  0.541  0.522  0.000  1.782346  1.322409   \n",
       "2  1.000  1.000  1.000  1.000  1.000  0.520  0.533  0.835 -0.586540  0.672436   \n",
       "3  0.000  0.000  0.000  0.000  0.000  1.000  0.000  0.000 -1.600326 -1.838680   \n",
       "4  0.348  0.762  0.550  0.392  0.489  0.517  1.000  0.642  0.960991  0.790990   \n",
       "\n",
       "        x55       x56       x57       x58       x59       x60    x61  y  \n",
       "0  0.000000  0.333333  0.333333  0.333333  0.000000  0.000000    9.0  2  \n",
       "1  0.011647  0.397671  0.239601  0.249584  0.068220  0.033278  601.0  4  \n",
       "2  0.000000  0.606061  0.121212  0.212121  0.060606  0.000000   33.0  3  \n",
       "3  0.000000  1.000000  0.000000  0.000000  0.000000  0.000000    1.0  4  \n",
       "4  0.020161  0.645161  0.258065  0.036290  0.040323  0.000000  248.0  3  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_FOLDER = # your path here\n",
    "anon_train = pd.read_csv(os.path.join(DATA_FOLDER, 'anon_train.csv'))\n",
    "anon_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this can help us to explore numeric fatures statistics.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>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",
       "      <td>x6</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.992960</td>\n",
       "      <td>0.083610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x7</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.975860</td>\n",
       "      <td>0.153485</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x8</td>\n",
       "      <td>48793.0</td>\n",
       "      <td>-0.000252</td>\n",
       "      <td>1.023282</td>\n",
       "      <td>-85.252444</td>\n",
       "      <td>-0.255490</td>\n",
       "      <td>0.221047</td>\n",
       "      <td>0.567620</td>\n",
       "      <td>3.426844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x13</td>\n",
       "      <td>45512.0</td>\n",
       "      <td>4428.915253</td>\n",
       "      <td>10943.884658</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1817.000000</td>\n",
       "      <td>5582.000000</td>\n",
       "      <td>776759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x23</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>35.961299</td>\n",
       "      <td>6.819859</td>\n",
       "      <td>15.226228</td>\n",
       "      <td>30.789115</td>\n",
       "      <td>36.597900</td>\n",
       "      <td>41.096434</td>\n",
       "      <td>135.063367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x24</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>-0.006380</td>\n",
       "      <td>0.968346</td>\n",
       "      <td>-1.310465</td>\n",
       "      <td>-0.606190</td>\n",
       "      <td>-0.239545</td>\n",
       "      <td>0.284950</td>\n",
       "      <td>34.899154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x25</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>-0.005182</td>\n",
       "      <td>0.947373</td>\n",
       "      <td>-0.817005</td>\n",
       "      <td>-0.540656</td>\n",
       "      <td>-0.223776</td>\n",
       "      <td>0.242333</td>\n",
       "      <td>38.146887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x26</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>3.652688</td>\n",
       "      <td>3.427215</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>59.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x27</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>0.006831</td>\n",
       "      <td>1.012709</td>\n",
       "      <td>-0.791959</td>\n",
       "      <td>-0.497977</td>\n",
       "      <td>-0.300303</td>\n",
       "      <td>0.133800</td>\n",
       "      <td>28.814227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x28</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>1.012566</td>\n",
       "      <td>-0.908422</td>\n",
       "      <td>-0.558758</td>\n",
       "      <td>-0.267042</td>\n",
       "      <td>0.246062</td>\n",
       "      <td>32.699880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x29</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>42.403973</td>\n",
       "      <td>115.348318</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>10.833333</td>\n",
       "      <td>39.833333</td>\n",
       "      <td>9124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x30</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>579.852204</td>\n",
       "      <td>1801.837511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997070</td>\n",
       "      <td>440.343140</td>\n",
       "      <td>64304.962728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x31</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>43.258414</td>\n",
       "      <td>67.114470</td>\n",
       "      <td>-170.424427</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x32</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>0.389604</td>\n",
       "      <td>0.666991</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x33</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>12.779810</td>\n",
       "      <td>10.329016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x34</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>0.437430</td>\n",
       "      <td>0.496075</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x35</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>0.277237</td>\n",
       "      <td>0.378817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x36</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>3.571173</td>\n",
       "      <td>2.917735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x37</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>49.567537</td>\n",
       "      <td>58.887573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1724.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x38</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>0.770832</td>\n",
       "      <td>2.364021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x39</td>\n",
       "      <td>48122.0</td>\n",
       "      <td>2.463073</td>\n",
       "      <td>2.016375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x40</td>\n",
       "      <td>44392.0</td>\n",
       "      <td>34.221346</td>\n",
       "      <td>25.403449</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x41</td>\n",
       "      <td>44392.0</td>\n",
       "      <td>6.597968</td>\n",
       "      <td>4.054533</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>6.420000</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>19.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x42</td>\n",
       "      <td>44392.0</td>\n",
       "      <td>0.216892</td>\n",
       "      <td>0.133338</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x43</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>1.220218</td>\n",
       "      <td>1.185721</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>0.935000</td>\n",
       "      <td>1.677000</td>\n",
       "      <td>29.774000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x44</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>1.320074</td>\n",
       "      <td>1.422262</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>36.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x45</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.340594</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.286000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>0.833000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x46</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.521335</td>\n",
       "      <td>0.352398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x47</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.504619</td>\n",
       "      <td>0.361136</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.191500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.818000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x48</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.470327</td>\n",
       "      <td>0.381034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468000</td>\n",
       "      <td>0.841000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x49</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.460443</td>\n",
       "      <td>0.381774</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.454000</td>\n",
       "      <td>0.822000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x50</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.464305</td>\n",
       "      <td>0.385583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.461000</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x51</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.461837</td>\n",
       "      <td>0.388054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.457000</td>\n",
       "      <td>0.851000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x52</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>0.464293</td>\n",
       "      <td>0.390805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.457000</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x53</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>-0.000686</td>\n",
       "      <td>0.999981</td>\n",
       "      <td>-1.600355</td>\n",
       "      <td>-0.697021</td>\n",
       "      <td>-0.080427</td>\n",
       "      <td>0.624234</td>\n",
       "      <td>3.473768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x54</td>\n",
       "      <td>47844.0</td>\n",
       "      <td>-0.003266</td>\n",
       "      <td>1.000475</td>\n",
       "      <td>-1.838682</td>\n",
       "      <td>-0.731589</td>\n",
       "      <td>-0.011984</td>\n",
       "      <td>0.804853</td>\n",
       "      <td>2.242850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x55</td>\n",
       "      <td>46126.0</td>\n",
       "      <td>0.046325</td>\n",
       "      <td>0.130912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018349</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x56</td>\n",
       "      <td>46126.0</td>\n",
       "      <td>0.298044</td>\n",
       "      <td>0.300326</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.193182</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x57</td>\n",
       "      <td>46126.0</td>\n",
       "      <td>0.286040</td>\n",
       "      <td>0.291669</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.481013</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x58</td>\n",
       "      <td>46126.0</td>\n",
       "      <td>0.201557</td>\n",
       "      <td>0.256582</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.086207</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x59</td>\n",
       "      <td>46126.0</td>\n",
       "      <td>0.116578</td>\n",
       "      <td>0.203317</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.129900</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x60</td>\n",
       "      <td>46126.0</td>\n",
       "      <td>0.051234</td>\n",
       "      <td>0.139306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025157</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x61</td>\n",
       "      <td>46126.0</td>\n",
       "      <td>161.143910</td>\n",
       "      <td>186.923802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>5462.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>y</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>3.108880</td>\n",
       "      <td>1.217479</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count         mean           std         min        25%          50%  \\\n",
       "x6   50000.0     0.992960      0.083610    0.000000   1.000000     1.000000   \n",
       "x7   50000.0     0.975860      0.153485    0.000000   1.000000     1.000000   \n",
       "x8   48793.0    -0.000252      1.023282  -85.252444  -0.255490     0.221047   \n",
       "x13  45512.0  4428.915253  10943.884658  -99.000000   0.000000  1817.000000   \n",
       "x23  48122.0    35.961299      6.819859   15.226228  30.789115    36.597900   \n",
       "x24  48122.0    -0.006380      0.968346   -1.310465  -0.606190    -0.239545   \n",
       "x25  48122.0    -0.005182      0.947373   -0.817005  -0.540656    -0.223776   \n",
       "x26  48122.0     3.652688      3.427215    0.000000   1.333333     2.833333   \n",
       "x27  48122.0     0.006831      1.012709   -0.791959  -0.497977    -0.300303   \n",
       "x28  48122.0     0.002051      1.012566   -0.908422  -0.558758    -0.267042   \n",
       "x29  48122.0    42.403973    115.348318    0.000000   1.666667    10.833333   \n",
       "x30  48122.0   579.852204   1801.837511    0.000000   0.000000     0.997070   \n",
       "x31  48122.0    43.258414     67.114470 -170.424427   0.000000    28.000000   \n",
       "x32  48122.0     0.389604      0.666991    0.000000   0.000000     0.000000   \n",
       "x33  48122.0    12.779810     10.329016    0.000000   6.000000    12.000000   \n",
       "x34  48122.0     0.437430      0.496075    0.000000   0.000000     0.000000   \n",
       "x35  48122.0     0.277237      0.378817    0.000000   0.000000     0.000000   \n",
       "x36  48122.0     3.571173      2.917735    0.000000   2.000000     3.000000   \n",
       "x37  48122.0    49.567537     58.887573    0.000000  17.000000    33.000000   \n",
       "x38  48122.0     0.770832      2.364021    0.000000   0.000000     0.000000   \n",
       "x39  48122.0     2.463073      2.016375    0.000000   1.000000     2.000000   \n",
       "x40  44392.0    34.221346     25.403449    0.000000  19.000000    29.000000   \n",
       "x41  44392.0     6.597968      4.054533    0.080000   3.580000     6.420000   \n",
       "x42  44392.0     0.216892      0.133338    0.000000   0.120000     0.210000   \n",
       "x43  47844.0     1.220218      1.185721    0.000000   0.387000     0.935000   \n",
       "x44  47844.0     1.320074      1.422262    0.000000   0.400000     1.000000   \n",
       "x45  47844.0     0.540541      0.340594    0.000000   0.286000     0.538000   \n",
       "x46  47844.0     0.521335      0.352398    0.000000   0.250000     0.500000   \n",
       "x47  47844.0     0.504619      0.361136    0.000000   0.191500     0.500000   \n",
       "x48  47844.0     0.470327      0.381034    0.000000   0.000000     0.468000   \n",
       "x49  47844.0     0.460443      0.381774    0.000000   0.000000     0.454000   \n",
       "x50  47844.0     0.464305      0.385583    0.000000   0.000000     0.461000   \n",
       "x51  47844.0     0.461837      0.388054    0.000000   0.000000     0.457000   \n",
       "x52  47844.0     0.464293      0.390805    0.000000   0.000000     0.457000   \n",
       "x53  47844.0    -0.000686      0.999981   -1.600355  -0.697021    -0.080427   \n",
       "x54  47844.0    -0.003266      1.000475   -1.838682  -0.731589    -0.011984   \n",
       "x55  46126.0     0.046325      0.130912    0.000000   0.000000     0.000000   \n",
       "x56  46126.0     0.298044      0.300326    0.000000   0.037037     0.193182   \n",
       "x57  46126.0     0.286040      0.291669    0.000000   0.035714     0.181818   \n",
       "x58  46126.0     0.201557      0.256582    0.000000   0.008065     0.086207   \n",
       "x59  46126.0     0.116578      0.203317    0.000000   0.000000     0.022727   \n",
       "x60  46126.0     0.051234      0.139306    0.000000   0.000000     0.000000   \n",
       "x61  46126.0   161.143910    186.923802    1.000000  37.000000   105.000000   \n",
       "y    50000.0     3.108880      1.217479    0.000000   2.000000     3.000000   \n",
       "\n",
       "             75%            max  \n",
       "x6      1.000000       1.000000  \n",
       "x7      1.000000       1.000000  \n",
       "x8      0.567620       3.426844  \n",
       "x13  5582.000000  776759.000000  \n",
       "x23    41.096434     135.063367  \n",
       "x24     0.284950      34.899154  \n",
       "x25     0.242333      38.146887  \n",
       "x26     5.000000      59.833333  \n",
       "x27     0.133800      28.814227  \n",
       "x28     0.246062      32.699880  \n",
       "x29    39.833333    9124.000000  \n",
       "x30   440.343140   64304.962728  \n",
       "x31    62.000000    1512.000000  \n",
       "x32     0.500000       6.000000  \n",
       "x33    18.000000      76.000000  \n",
       "x34     1.000000       1.000000  \n",
       "x35     0.500000       1.000000  \n",
       "x36     5.000000      75.000000  \n",
       "x37    61.000000    1724.000000  \n",
       "x38     1.000000     364.000000  \n",
       "x39     3.000000      50.000000  \n",
       "x40    43.000000    1110.000000  \n",
       "x41     9.250000      19.670000  \n",
       "x42     0.300000       0.650000  \n",
       "x43     1.677000      29.774000  \n",
       "x44     1.800000      36.400000  \n",
       "x45     0.833000       1.000000  \n",
       "x46     0.833000       1.000000  \n",
       "x47     0.818000       1.000000  \n",
       "x48     0.841000       1.000000  \n",
       "x49     0.822000       1.000000  \n",
       "x50     0.846000       1.000000  \n",
       "x51     0.851000       1.000000  \n",
       "x52     0.879000       1.000000  \n",
       "x53     0.624234       3.473768  \n",
       "x54     0.804853       2.242850  \n",
       "x55     0.018349       1.000000  \n",
       "x56     0.500000       1.000000  \n",
       "x57     0.481013       1.000000  \n",
       "x58     0.304348       1.000000  \n",
       "x59     0.129900       1.000000  \n",
       "x60     0.025157       1.000000  \n",
       "x61   220.000000    5462.000000  \n",
       "y       4.000000       6.000000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anon_train.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it's a good idea to introduce new features. We can understand something about the generation process of data fi we generate the following feature, that we will call Error. If in the row there are NaN the feature will be 1 otherwise 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32761\n",
      "17239\n"
     ]
    }
   ],
   "source": [
    "anon_train['Error'] = anon_train.isna().any(axis=1)\n",
    "\n",
    "# check how the new feature is distributed\n",
    "\n",
    "false_count = (~anon_train.Error).sum()\n",
    "print(false_count)\n",
    "true_count = (anon_train.Error).sum()\n",
    "print(true_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At least we have a feature that is known!\n",
    "\n",
    "Since we do not know what all the rest of data are, we can try do build a quick baseline with a toy model. At the moment, we don't need to precisely know what the model does but if you care give look at:\n",
    "\n",
    "https://en.wikipedia.org/wiki/Random_forest\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
    "\n",
    "The only thing we know about data is that the task they give us is a multiple classification. We can easily split the dataframe in features and target and use proper copies to work with. \n",
    "\n",
    "Never forget to fill the missing values! Here we are adopting an easy solution: we are filling then nans with an outlier value, but one could decide, if you are considering numerical features, tho fill it with the mean value. There is no limit to your immagination and what you can do. For example, if you want to drop your dataset you could fill all the features that you can and only in the last step remove the entries with missing values which, at this point, may be far less that the beginning. Obviously you introduced a bias in you rada and its on you to decide how to hande this kind of situation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create a copy to work with\n",
    "X = anon_train.copy()\n",
    "\n",
    "# Save and drop labels\n",
    "y = anon_train.y\n",
    "X = X.drop('y', axis=1)\n",
    "\n",
    "# fill NANs \n",
    "X = X.fillna(-999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The factorize method is useful for obtaining a numeric representation of an array when all that matters is identifying distinct values. We are encoding the target values in a manner tht is easy to interpretate for us and faster to process for the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jacopo/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label encoder\n",
    "\n",
    "for c in anon_train.columns[anon_train.dtypes == 'object']:\n",
    "    X[c] = X[c].factorize()[0]\n",
    "    \n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the classifier is fitted it contains a data representation that we can investigate. Random forest allow a so friendly representation that is possible to plot. There will be less or more important features on which we will focus: in this case the most informative feature about our data is the X8:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qTaVjkvci2e5uqrzHXH30CWumhZitsH19lStPo7ek1VohlfaXt/Ve5O1xoyd1J1vIjXZT5bljaqb81QJR+efVbNmLuuBU2r6eC0K5ohtJeT/XVnUXKdDXqdX9/Xn71fPegpZPzbSi81b+dvc/TiTv59Xo59pscCoP2OX5yHvcmh1N0mj3ULuelxQ5FT1QoBHtHiSiQF+Hdnw4eStYPQGmr6+YW9K8eWt3/2Mn1TuKY6K600ywbsVokjwXv3Y9LylyylP+dpzr7RwkokBfh1Zf5d3zt5obPbHruSVttJXX7v7HTqrn2UOtPuRqXW21LhDu9Y+QKfoY1KontcpULT1vH30ryt/ro/WyFOjrMFFlKqp7IO/FpNHWX96+wbz9u/WUoZXD1Tqp0VEh9XRxNXrM81wkWqFS8G+0fjX6zKrVI2fapYgLTtOBHpgLrAPWA9dWWH4IcHtc/mNgMLPsupi+Dri41nsVGehrVcR6HijmHV5W9LfjGmlxt3qUSL1lyPPZ9NqJmVfeu7Jmjnm3funLvfjPuchhmN2iqC6kpgI90AdsAI4HpgKPAieVrXM18KX4egFwe3x9Ulz/EGBO3E/fRO9XVKDP05Jq9paxmSF8zVTOvC3JPBewauUqKh/Vtuv1bzLnlae7QRfIg1tR3cXNBvqzgTsz89cB15Wtcydwdnw9GXgesPJ1s+tVm4oK9M2MFChyeFk9gbOVOtm/W64dz0G6RbWyZh+eK1gf3Ioa5jlRoM/z3yuPA7Zm5kdiWsV13H038Atges5tW6KZ/1y5ffv+vzA0MNBcXrrhvyFWy4PZ/vPt+H3UVP7baB7VfoP2llv0C1YStOO/qOYJ9FYhzXOuk2dbzGyxmQ2b2fDo6GiOLNXWzEEq37bSyVoeIKuld8sPS1cLOFde2f4fQu7mfw9ctFR/bFqK05YfpK/W1B+f6NGum0b76Jt9KHowPRhrJh8HSx+9SB4dH3UTA/dGwsPU8YexJ5et8/vs/zD2G/H1yez/MHYjbXoY697YqJuiH4pKZTp2IsWaKNBbWD4xM3s78DnCCJwV7r7MzJbGHQ+Z2aHA14DTgO3AAnffGLddArwf2A182N3vmOi9GvnNWBGRg91EvxmbK9C3kwK9iEj9kv9xcBERqU6BXkQkcQr0IiKJU6AXEUlc1z2MNbNRYHMTu5hBGMc/UVqedXoxrVvyUXRat+Sj6LRuyUfRad2Sj6LTOpWPvAbcvb/ikmrjLnt1osJY0vK0POv0Ylq35EPlUrm6IR+plKuISV03IiKJU6AXEUlcioF+eY60POv0Ylq35KPotG7JR9Fp3ZKPotO6JR9Fp3UqH03ruoexIiJSrBRb9CIikqFALyKSOAV6EZHEKdCLiHSImfWZ2adb/T6TW/0GrWRmRwJzCb9D68A2wq9h/bxsvSOAS9z9rzNpxwInuvt9ZtYPvAlY5+5rMut80t2vz8y/EZhJ+DGVdcDlwDsIv5z1RQ+/lzu+7rnAs+6+zszOAf4bsInwy1uzCP+ffwMwG7gE+PVMGf4e+Iq774r76gN+F/gA8EF3/6GZTQOuAS6I2y8A/hfwU2Cpu/8qbvuMuw/G11OAjwO/DdwDLInv+yBwGLAWeL+7P25mxwN/FPPzKeCzhF8bewl4kfCbwLuB9YTfAj65w2VYEY/xFuB33P0ndZThaWA1cDz716W/d/fvkmFmFwNXEX5b4RkzM+BS4M2EH+C5AJgfy/Ald98bt/uBu78ps5/LYnnvBj5P+EbkZ4C3AY8Bi9x9JK57TDxO24CvANcD/4Pwgz7/FsvzNKFOntlDZTgb+FX8LF4F7AGmxekouqguxfULPSeiLwKvNQs/BU6L9OyoGzN7L3AD8M/Az2LyTOBC4P+MB3Uz+23Cj6bMIJwMlwOnA9cSPoQPx7TD4/xqQsAz4ErCBwGhInwTuA04i/BhvgTMA/YSPtgPEYL+Zwgn3OQ4/xbCj6TPJfx7h1cADxAuLq+M230/vs9JhEp6NHBFTPs8IRC/lXBx+H4s61bgvYST6knCD7z0xby/HLc9nHAyOfBXhMp4HvB38fV0QqW+D7gI2An8HvAR4OvAkcBlwFdjmQ6N7/0D4JfAOcDrgNsJFb9TZfgy8Bfxc/lN4NuEE+2LcfuJyvB64FzgHwhBiLj8vcDT7v4HEC78sbynAqOEenUiIUi9FfgOIfieBRxBOOm3xbKcDDwR9/0Nwmf/ekI9GGHfBfePYp6OBb4A/A2wDHg87vPEWJ7n4vE5C1hJ+NGfNxCC1P/rgTI8TqgfrycE+E1x/88Crwb+DPhHuqcu/Q2NnxNHxWNTXg4jNBpvB04gxJd/j8tw97+lKK34um07JkLr5aiytCHgjvhBDsXpF4SLwb/HD+WnhGA7jVAhfkWokCOEyrsZWBSnXZnXDxBOGAgXjTFC19fD8cPbSrgYPEv4la3z4nvsyLzXEYQTZQbhArAOOAV4IFOGPYSfXNxFqPybCJV0EyEITyaMtf054YR8mNCqM8JJ9dfA2sz+Xs68fgSYEt/XYp5WAQ/H5a8ktGL+Kb7/VwnBf0tc/lj8O77+g3FfhwBPdrIMZfl6AnhP/OxHYzmem6AMT1Uowy/jtKfs9YuE4HdUPE7Pj+8PeIHwc5tDhOD7FDAADMZyD8TpIcLFfl0sy+PA6rJ8rQX+N7AG+A9Co+Z1hEbN42XH44fxvY4GnuiFMsTlj2fKMBl4Kc7/ZznonrrU8DmRKcOmTDnG53fGfZZPKwqNl50O2E0E+qeAI8vSdhBuQUcIgfa8eEDfTOhGgdBaGCO0oh8CHs0Euc8RgvRxFSrFTwjB+YI4//NMhZ+e2c+xhBbQjwjBfweh9fE4oXWxNs4/HCvFpex/cj4NXA38OJP20/h3aybtXwkn+NPZSgGcQTiRP0S4EO0CfovQxTRe8ZYB/5fQ6rk+5nM28D7gO5mT4BOEC8HzQIlwt3NBPCFOB+7PlGFth8vw4XiMs2VYTWjl/gnhhKpWhscIjYBsGbYQ7sCyQfXJbBni5/kCoSW2BvhuZt3fIjQi5sX5nYRW9xnsqys3A0sJrbrPELocHgLOB75fVtc/CzxDuHitBY4h1Lun4mcwXoY1PVKGwbjNa2J5ZsfP/NL4ma/pprrUzDkR8zQAvCtbjvIytDRedjpgN5zx0MreQLg1vz5OWwgtnssz6z0QK9P9mbSHgHsJLYOZmfRDY4W8D/go4fbul7Gi7Iwf5H3su0XbEZc9DLwls58b4zqPAp8mdAncHyvHj+Oy62Nl/zahdfVUnF4EvgvMyezvVsIJ+8FM2pcJ/am7yo7La4B/iRX7B4SLWral8GtxvT8gnHDPx/dfC3ySePEkdDeti5X/HEJ3wM8IrZOfEVojZ8Uy/F18n06W4UXCXVsjZTidcMI+T7j7++f4+gngjEy+vkNoPNyYSbsjft57y8pwLDBM6IIYIrRo78tMrya0Jm8k1MMthC7APYTugdmZfb2bcKf4LCFAPU7oNvwPQpD+75kyjN/BdnsZ7o7l2E24wGwhBNbbY75/SQiQXVGXmjwnniM0IG/PliPu84OEbp9vx/WejfudmV3voA308SAdQ+gP/ENCYB7vA1uUWec3CP1f2bTZhFb1x8v29+uE/jOLlebWmH552Xq/CdwSK+xVhJbCorJ1zgauyVS2jxJall8GLsysZ7HSTgdmFHRcxp+9vBp4e4HHewahBVgxn71eBkJgO4PQUju2wvLDgMOq7Pe4svlXAK/K1MErq2zXR+jaOzIev8MnWG9yfD2Z0Iq8mAO7L3upDCVCf32pQjm6vi7Vqk95ywHcRbjITY7T5cBdheazyJ11YiK0WD4eA+Y0Qr/cWCbtsAbSPlZhfx/LrPcCoS+/0X2Vr/ejCuW6sBvScq5zBPDeCmlv73RanH8NcEqN7Y4Fzo+v+wkjNs4lBssuTHs/cG7ZOieXlemTFT67nkkD5sRy/dcuT/svZWmLx+sb4Tz/Q+AvCY3CyYSg/oXM/CMVjsMBac1MLQvA7ZoIrY6/IPSJPwFcR3iy3sq0TxS8/0kVyrWlG9JqrUMYmraN0LW1htAnPp421uG06+L8I4SujmrbfYB9D/muInSv/TCW6fkuTNtE6F7YBXwtrrOG0JV4N/DncXo5lv2ROP+FJtKK3l+ltA2EZ19/Tuju3ERoUD1FaOXOz6Sta0Na3vctX29HJu1GQpfQ+wkjoh4DvkUYtbMiTnfH+b44XQbcU2Sc7Olx9NEuwkl8GKGPfRPhpGhl2gbCw9tm9zWH0P1zdhjKDIQHagAzzGyoTWnj85XSau3rPEKgeSOhpfI1woiDMwgjFT7QwbT7gavdfYWZrZtgu8MIXQgPEp6pvJZwOz1IeADfbWlbgV8j3IU8TRg3PxzXOyuWCUJrcYzQoFgd065uIq3o/ZWnzSecz6sJY/EvAP6WMGT6HkK/eS+k/SPhDuwewjOIn7n7CmCFmY0Bp3r4bsKtZvYo8D8Jjb/PEp4LPkC4MBQmhW/GrmJfa+0cwkOf51qc9peEB2DN7msXoSIcQRix8BnCHcrnCA+j2pX2CkIl+5OytDz7eoHw0OpXHr5Ycj4hAF0KeIfTNgG/b2YfIpyE1bbrJ3wBZzewwd3/jfBQb1s3phGGIY65+0ZgZ1znRMKdyiuBu939FsIDzp8Q7iK7Pi3WpRfjsh3uvil+Ts8THvJO7pG0rYRh03sJo4xGzewCM5tOaOjNAojzAO9w93nu3u/ur3L3S9y9mZ9TPVCRtwedmIBShbRPtDKN8PDoPc3uizDa4fzsvjJp97crbXw+vr6/zn1VGtX0YEx/uZNp8fUphJbVyxNsd8AoLEIL+XDCyKmuSovzUwh3idkhoeWjxp6J6af3SNreOI2PcjuWfd8B2NNDabOA7xGGpv4DoTtqR5z/UXx9L3G0HvC9lsfJVr+BphwfApxUIW1Ru9MI30J8cz3bUXlU028QWpgf72RaJm+nEC+mVbY7YBRWTJsNXNFtafH1FEK3WXYEV7VRY4t6KY3wDdk3Z8p1FPBnZXWwF9I+S+iOegehS+0Uwr9ZGJ+fRPiOzzJC182bCBe904HTa8WNeqaOBzlNDvuPHGpklFBRafWMJuqltIOtXBONGuuFtFrl6qW0Wp/Xj9j/uwnj071FxpgU+uhTcBbhdu8BwjOHbYT/PdLutM8TbjU7nQ+Vq7lyzY5pP+nRtFrl6qW0Wp/XOYR/iHh+2XQBBVKg7w6dGDlUKW1Dl+RD5VK5uiEfLS+Xu+8h/NfNllKg7w6dGDnUytFE3ZamcvVW2kFTLjP7FnCXmX3UzGaZ2THjE0XqdP+0JocOjByqlEZBo4m6LU3l6q20g6xc72Hff+XMThvL99fM1LP/j15ERPJR142ISAeY2ccyry8tW/bJIt9LgV5EpDMWZF5fV7ZsbpFvpEAvItIZVuV1pfmmKNCLiHSGV3ldab4pehgrItIBZraH8M/2xr8pOza+CDjU3acU9l4K9CIiaVPXjYhI4hToRUQSp0AvIpI4BXoRkcQp0IuIJO7/A2g8z0lQeTYjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rf.feature_importances_,'bo')\n",
    "plt.xticks(np.arange(X.shape[1]), X.columns.tolist(), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's focus on the \"most important feature\", according to our baseline. We can easily check that is standard scaled. This scaling is only almost exact since the mean and the std are not exaclty 0 and 1; this could mean that the whole dataset (train adn test) was generated and then split. If true this could mean that we are allowed to overfit a little more than if train and test set were generated with different procedures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: -0.00025235202862197324\n",
      "Std: 1.023281636012582\n"
     ]
    }
   ],
   "source": [
    "print( 'Mean:', anon_train.x8.mean())\n",
    "print( 'Std:', anon_train.x8.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there repeated values? Yes, a lot! The first 15 appear thousand of times each. This could mean that we are in front of an integer feature, shifted by a non integer constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.984750    2770\n",
       " 0.480977    2569\n",
       " 0.610941    1828\n",
       " 0.654263    1759\n",
       " 0.567620    1746\n",
       " 0.697585    1691\n",
       " 0.524298    1639\n",
       " 0.740906    1628\n",
       " 0.394333    1610\n",
       " 0.437655    1513\n",
       " 0.351012    1450\n",
       " 0.264369    1429\n",
       " 0.307690    1401\n",
       " 0.221047    1372\n",
       " 0.784228    1293\n",
       "Name: x8, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anon_train.x8.value_counts().head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original feature was multiplied by a number and then shifted. We already understood that this shift is not an integer and that the variable is likely to be integer. \n",
    "\n",
    "We can formalize our intuition as follows:\n",
    "Let be $X$ the (standard scaled) feature we want to scale back. Our hypothesis is that the original feature $Z$ can be obtained from the current with the transformation:\n",
    "\n",
    "$ Z = \\mu + \\sigma X = <Z> + \\sigma_Z X$\n",
    "\n",
    "Now our aim is to obtain $<Z>$ and $ \\sigma_Z $, we will do it by an ansatz solution.\n",
    "So far, the two quantities follow two normal distribution, with different parameters:\n",
    "\n",
    "$$ X \\sim N(0,1) \\quad Z \\sim N(\\mu,\\sigma)$$\n",
    "\n",
    "First we sort the array of unique values of $X$.\n",
    "If we print the difference between two consecutive entries we can imediately notice that the difference is a constant, that was the same in the original data too.\n",
    "It was probably not 0.043 something, it could be 9 or 11 or 11.7, but it was the same between all the pairs, and our ansatz is that it was 1.\n",
    "If we divide our feature by this number, we see that the differences become close to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43.27826527, 38.98942817,  0.21660793,  0.04332159,  0.17328635,\n",
       "        0.21660793,  0.08664317,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.12996476,  0.04332159,  0.04332159,  0.04332159,\n",
       "        0.04332159,  0.04332159,  0.04332159,  0.04332159,  0.21660793,\n",
       "        1.16968285,  0.04332159,  0.38989428,         nan])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x8_unique = anon_train.x8.unique()\n",
    "x8_unique_sorted = np.sort(x8_unique)\n",
    "                           \n",
    "np.diff(x8_unique_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([998.99992752, 899.9999347 ,   4.99999964,   0.99999993,\n",
       "         3.99999971,   4.99999964,   1.99999985,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   2.99999978,   0.99999993,\n",
       "         0.99999993,   0.99999993,   0.99999993,   0.99999993,\n",
       "         0.99999993,   0.99999993,   4.99999964,  26.99999804,\n",
       "         0.99999993,   8.99999935,          nan])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(x8_unique_sorted/0.04332159)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " if we divide our features for 0.43.. this is what we get\n",
    " we can see that all the number ends with the same digits, x.102469 for positive values \n",
    " and about -x.897526 for negative values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -15.897530\n",
       "1    20.102468\n",
       "2    10.102468\n",
       "3     0.102469\n",
       "4    11.102468\n",
       "5   -68.897526\n",
       "6    10.102468\n",
       "7    15.102468\n",
       "Name: x8, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(anon_train.x8/0.04332159).head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "subtracting the fractional part our features will be almost integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -15.999998\n",
       "1    20.000000\n",
       "2    10.000000\n",
       "3     0.000001\n",
       "4    11.000000\n",
       "5   -68.999994\n",
       "6    10.000000\n",
       "7    15.000000\n",
       "8     9.000000\n",
       "9   -68.999994\n",
       "Name: x8, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(anon_train.x8/0.04332159 - .102468).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -16.0\n",
       "1    20.0\n",
       "2    10.0\n",
       "3     0.0\n",
       "4    11.0\n",
       "5   -69.0\n",
       "6    10.0\n",
       "7    15.0\n",
       "8     9.0\n",
       "9   -69.0\n",
       "Name: x8, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# round them\n",
    "\n",
    "x8_int = (anon_train.x8/0.04332159 - .102468).round()\n",
    "x8_int.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage we found only the fractional part of the variable $Z$\n",
    "Can we find the integer part? It is not obvious how to proceed but we can improve our intuition about the ansatz (hypothesis to buil) with further exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-69.0    2770\n",
       " 11.0    2569\n",
       " 14.0    1828\n",
       " 15.0    1759\n",
       " 13.0    1746\n",
       "         ... \n",
       " 42.0       1\n",
       "-63.0       1\n",
       "-48.0       1\n",
       "-64.0       1\n",
       " 35.0       1\n",
       "Name: x8, Length: 99, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x8_int.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we scroll down we will see this, -1968 and it definitely looks like year a of birth. \n",
    "Immediately I have a hypothesis, that this could be a text box where a person should enter his year of birth. We have been guided by mathematics but giving a look the the data values and with a spark of fantasy one can get a reasonable hypothesis.\n",
    "In this entry something wrong happened and the data recording system set a default/error value. So be carefoul with dropping outliers, because they can be the keays to leak your data!\n",
    "\n",
    "And wow, that isn't the key.\n",
    "If we assume the value was originally 0, then the shifting parameter is exactly 1968, let's try it. The outlier remain an outlier but now all the pther values are properly shifted and it's clear that X8 it's a year birth!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0          1\n",
       "999.0        4\n",
       "1899.0    2770\n",
       "1904.0       1\n",
       "1905.0       1\n",
       "          ... \n",
       "2005.0       2\n",
       "2010.0       1\n",
       "2037.0       1\n",
       "2038.0       1\n",
       "2047.0       1\n",
       "Name: x8, Length: 99, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x8_int + 1968.0).value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Something more about decoding\n",
    "\n",
    "The best we can do is to recognize the type of the features.\n",
    "Is it categorical, numeric, text, or something else?\n",
    "Each data type should be treated differently,and more treatment depends on the model we want to use. That is why to make a stronger model, we should know what data we are working with. Even though we cannot understand what the features are about, we should at least detect the types of variables in the data.\n",
    "\n",
    "When the number of columns in data set is small, we can just consider the table, and manually sort the types out. But, what if there are thousand of features in the data set? Pandas offers very useful functions to facilitate our exploration, such as function dtypes that guesses the types for each column in the data frame and sorts the columns according to its guess. The outcome may be four-fold: \n",
    "\n",
    "* float64 for numeric data\n",
    "* int64 for categorical or event counting\n",
    "* datatime64 for time features\n",
    "* object for strings, text ecc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x0        object\n",
       "x1        object\n",
       "x2        object\n",
       "x3        object\n",
       "x4        object\n",
       "          ...   \n",
       "x59      float64\n",
       "x60      float64\n",
       "x61      float64\n",
       "y          int64\n",
       "Error       bool\n",
       "Length: 64, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anon_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The truth is that object is quite problematic. We recommend to look directly into your column to improve yous ansatzs.\n",
    "Be careful! The dtype returned by this function can be in contrast with the dtype guessed by dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aa2f3cd34a    13090\n",
       "cc73658409     3006\n",
       "467f9617a3     1588\n",
       "65d757ad7d     1523\n",
       "bfff7d2d9e     1523\n",
       "              ...  \n",
       "e6b51a9c15       18\n",
       "d4d4b9c606       11\n",
       "6ea3724cfa       10\n",
       "e2f6f3422c        5\n",
       "6e38793f2b        2\n",
       "Name: x0, Length: 85, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anon_train.x0.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "anon_num = anon_train.select_dtypes(include=['float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 41)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anon_num.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it may be important to detect the feature with the highest mean value or investigate, who knows, the features with the first three or four highest mean value. Let's try with the one with the highest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff63c597050>]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_feature_mean = np.array( anon_num.mean() )\n",
    "num_feature_id   = np.array( range( 41 ) )\n",
    "\n",
    "plt.plot(feature_id,feature_mean,'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 4428.915253120056, 9: 579.8522041815261, 40: 161.14391015912935, 16: 49.56753667761107, 10: 43.258414243519944, 8: 42.403973234695506, 2: 35.96129898816198, 19: 34.22134618850243, 12: 12.779809650471718, 20: 6.597967877094929, 5: 3.652688306110872, 15: 3.571173267943976, 18: 2.4630730227338846, 23: 1.3200735724437265, 22: 1.2202181882786383, 17: 0.7708324674784922, 24: 0.5405414263021449, 25: 0.5213348800267512, 26: 0.5046190117883088, 27: 0.47032729286848296, 29: 0.4643052629378774, 31: 0.4642925131677977, 30: 0.4618370955605715, 28: 0.4604432321712223, 13: 0.4374298657578654, 11: 0.38960420043500094, 35: 0.298044466094258, 36: 0.2860404443012271, 14: 0.27723702256763955, 21: 0.2168922328347537, 37: 0.20155726643545605, 38: 0.1165783303124601, 39: 0.05123354838521274, 34: 0.04632493988543323, 6: 0.006830594924892294, 7: 0.0020509575188448837, 0: -0.00025235202862197324, 32: -0.0006858238223447618, 33: -0.003265624204077573, 4: -0.005181671639822081, 3: -0.006380185710827436}\n"
     ]
    }
   ],
   "source": [
    "average = dict(zip(num_feature_mean,num_feature_id))\n",
    "\n",
    "avg = np.sort(num_feature_mean)\n",
    "avg = avg[::-1]\n",
    "\n",
    "mu = {}\n",
    "\n",
    "for i in range(avg.shape[0]):\n",
    "    \n",
    "    mu[average[avg[i]]] = avg[i]\n",
    "\n",
    "print(mu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sorting show us that there are few (3) features with high mean value and most of the oters are not higher that 10. We may investigate the first two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0.0010622264382784572\n"
     ]
    }
   ],
   "source": [
    "magic1 = 1\n",
    "print(magic1)\n",
    "\n",
    "imp1 = rf.feature_importances_\n",
    "\n",
    "print(imp[magic1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x8</th>\n",
       "      <th>x13</th>\n",
       "      <th>x23</th>\n",
       "      <th>x24</th>\n",
       "      <th>x25</th>\n",
       "      <th>x26</th>\n",
       "      <th>x27</th>\n",
       "      <th>x28</th>\n",
       "      <th>x29</th>\n",
       "      <th>x30</th>\n",
       "      <th>x31</th>\n",
       "      <th>x32</th>\n",
       "      <th>x33</th>\n",
       "      <th>x34</th>\n",
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       "      <th>x39</th>\n",
       "      <th>x40</th>\n",
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       "      <th>x48</th>\n",
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       "      <td>-0.112693</td>\n",
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       "      <td>1.674538</td>\n",
       "      <td>0.630889</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.294922</td>\n",
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       "      <td>0.074</td>\n",
       "      <td>0.634</td>\n",
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       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.870871</td>\n",
       "      <td>18156.0</td>\n",
       "      <td>28.765503</td>\n",
       "      <td>2.612285</td>\n",
       "      <td>2.159091</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.710714</td>\n",
       "      <td>1.713538</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.027669</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>244.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>17.25</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3.452</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.619</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.782346</td>\n",
       "      <td>1.322409</td>\n",
       "      <td>0.011647</td>\n",
       "      <td>0.397671</td>\n",
       "      <td>0.239601</td>\n",
       "      <td>0.249584</td>\n",
       "      <td>0.068220</td>\n",
       "      <td>0.033278</td>\n",
       "      <td>601.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.437655</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>24.943933</td>\n",
       "      <td>-0.814660</td>\n",
       "      <td>-0.708308</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>-0.512422</td>\n",
       "      <td>-0.733967</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>14.837728</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.835</td>\n",
       "      <td>-0.586540</td>\n",
       "      <td>0.672436</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606061</td>\n",
       "      <td>0.121212</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.004439</td>\n",
       "      <td>14559.0</td>\n",
       "      <td>41.576860</td>\n",
       "      <td>-0.907833</td>\n",
       "      <td>-0.761736</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.627525</td>\n",
       "      <td>-0.805801</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.92</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.600326</td>\n",
       "      <td>-1.838680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.480977</td>\n",
       "      <td>5777.0</td>\n",
       "      <td>31.080282</td>\n",
       "      <td>-0.371787</td>\n",
       "      <td>-0.367616</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.271307</td>\n",
       "      <td>0.013112</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>1713.439128</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>8.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.032</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.517</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.960991</td>\n",
       "      <td>0.790990</td>\n",
       "      <td>0.020161</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.036290</td>\n",
       "      <td>0.040323</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x8      x13        x23       x24       x25       x26       x27  \\\n",
       "0 -0.688706   3694.0  40.579612 -0.112693 -0.172191  1.166667  1.674538   \n",
       "1  0.870871  18156.0  28.765503  2.612285  2.159091  4.000000  1.710714   \n",
       "2  0.437655   1178.0  24.943933 -0.814660 -0.708308  1.500000 -0.512422   \n",
       "3  0.004439  14559.0  41.576860 -0.907833 -0.761736  0.500000 -0.627525   \n",
       "4  0.480977   5777.0  31.080282 -0.371787 -0.367616  1.666667  0.271307   \n",
       "\n",
       "        x28        x29          x30    x31       x32   x33  x34       x35  \\\n",
       "0  0.630889  37.000000     1.294922   55.0  0.166667  10.0  0.0  0.000000   \n",
       "1  1.713538   0.166667     0.027669  109.0  0.000000  31.0  0.0  0.000000   \n",
       "2 -0.733967   0.333333    14.837728   11.0  0.000000  24.0  0.0  0.000000   \n",
       "3 -0.805801   1.166667     0.004395    0.0  0.500000   0.0  0.0  0.000000   \n",
       "4  0.013112  17.333333  1713.439128   33.0  0.000000   6.0  1.0  0.666667   \n",
       "\n",
       "   x36    x37  x38  x39   x40    x41   x42    x43  x44    x45    x46    x47  \\\n",
       "0  1.0    9.0  0.0  1.0  23.0   3.67  0.12  1.935  2.2  0.625  0.250  0.125   \n",
       "1  1.0  244.0  1.0  1.0  68.0  17.25  0.57  3.452  4.0  0.409  0.619  0.579   \n",
       "2  1.0   29.0  0.0  3.0  11.0   4.42  0.15  0.161  0.2  1.000  1.000  1.000   \n",
       "3  7.0    7.0  0.0  3.0  15.0   8.92  0.29  0.226  0.8  0.000  0.000  0.000   \n",
       "4  8.0  108.0  1.0  4.0  86.0   1.58  0.05  2.032  2.4  0.348  0.762  0.550   \n",
       "\n",
       "     x48    x49    x50    x51    x52       x53       x54       x55       x56  \\\n",
       "0  0.000  0.813  0.074  0.634  0.548  0.235333  0.264952  0.000000  0.333333   \n",
       "1  0.248  0.346  0.541  0.522  0.000  1.782346  1.322409  0.011647  0.397671   \n",
       "2  1.000  1.000  0.520  0.533  0.835 -0.586540  0.672436  0.000000  0.606061   \n",
       "3  0.000  0.000  1.000  0.000  0.000 -1.600326 -1.838680  0.000000  1.000000   \n",
       "4  0.392  0.489  0.517  1.000  0.642  0.960991  0.790990  0.020161  0.645161   \n",
       "\n",
       "        x57       x58       x59       x60    x61  \n",
       "0  0.333333  0.333333  0.000000  0.000000    9.0  \n",
       "1  0.239601  0.249584  0.068220  0.033278  601.0  \n",
       "2  0.121212  0.212121  0.060606  0.000000   33.0  \n",
       "3  0.000000  0.000000  0.000000  0.000000    1.0  \n",
       "4  0.258065  0.036290  0.040323  0.000000  248.0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anon_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-99.0, 30000)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "magic_feature = anon_num['x13']\n",
    "\n",
    "magic_hist = magic_feature.hist(bins=300)\n",
    "magic_hist.set_xlim((anon_train.x13.min(),30000))\n",
    "\n",
    "\n",
    "#magic_hist = anon_train.x13.hist(bins=300)\n",
    "#magic_hist.set_xlim((anon_train.x13.min(),30000))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some good practices: CASE STUDY 3 (very brief)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can still get higher confidence with pandas, using it as if were a data base. \n",
    "Here we are going to see an example of how to build a query in pandas.\n",
    "First of all, download the dataset:\n",
    "\n",
    "https://www.kaggle.com/c/competitive-data-science-final-project/data\n",
    "\n",
    "And encode it with pandas dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FOLDER = '/home/jacopo/Desktop/MLJC/PROX/FILES/Notebooks/jovyan/readonly/final_project_data'\n",
    "\n",
    "transactions    = pd.read_csv(os.path.join(DATA_FOLDER, 'sales_train.csv.gz'))\n",
    "items           = pd.read_csv(os.path.join(DATA_FOLDER, 'items.csv'))\n",
    "item_categories = pd.read_csv(os.path.join(DATA_FOLDER, 'item_categories.csv'))\n",
    "shops           = pd.read_csv(os.path.join(DATA_FOLDER, 'shops.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use your `pandas` skills to get answers for the following questions. \n",
    "The first question is:\n",
    "\n",
    "### What was the maximum total revenue among all the shops in September, 2014? Hereinafter *revenue* refers to total sales minus value of goods returned.\n",
    "\n",
    "Track of the solution:\n",
    "\n",
    "Sometimes items are returned, find such examples in the dataset. \n",
    "\n",
    "It is handy to split `date` field into [`day`, `month`, `year`] components and use `df.year == 14` and `df.month == 9` in order to select target subset of dates.\n",
    "\n",
    "You may work with `date` feature as with strings, or you may first convert it to `pd.datetime` type with `pd.to_datetime` function, but do not forget to set correct `format` argument.\n",
    "\n",
    "Given the hints we can translate our reasoning into code!\n",
    "\n",
    "First we change date format to datetime. Doing this we will be able to better handle the data.\n",
    "The next step is the splitting procedure: from one original features we create three new ones and then drop the old.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions[\"date\"] = pd.to_datetime(transactions[\"date\"],format = \"%d.%m.%Y\")\n",
    "\n",
    "transactions[\"year\"] = transactions.date.dt.year\n",
    "transactions[\"month\"] = transactions.date.dt.month\n",
    "transactions[\"day\"] = transactions.date.dt.day\n",
    "\n",
    "transactions.drop('date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second step is to create an object where we save only what we want to investigate further.\n",
    "Again we are redefining our feature. We are defining its entries trough a lambda function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "shortlist = transactions[(transactions.year == 2014) & (transactions.month == 9)]\n",
    "shortlist = shortlist.assign(dailygain = lambda x: x.item_cnt_day * x.item_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we store our target feature in a numpy array, and will find the maximum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7982852.199999956\n"
     ]
    }
   ],
   "source": [
    "revenuelist = np.array([])\n",
    "\n",
    "for i in shortlist.shop_id:\n",
    "    revenuelist = np.append(revenuelist,shortlist[(shortlist.shop_id == i)].dailygain.sum())\n",
    "    \n",
    "\n",
    "max_revenue = revenuelist.max()\n",
    "\n",
    "print(max_revenue)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset cleaning: CASE STUDY 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic idea here is to think to our dataset as a big table. In this table it may happen that some information is just a plus, and this can happen in several ways. \n",
    "We have already checked one. In our first real case we spotted some practically constant features and decided to remove them because equally distributed features means noise something that, in principle we would like to avoid.\n",
    "The second thing that may help us to decide how to clean the dataset are the identical features. This may happen if the data provider made some errors in the dataset design and/or sampling. If two features are identical we can drop one of them without loss of information.\n",
    "Another important thing that may happen is that the dataset needs to be filled. There may be a lot of missing entries (considered as single values). We can choose serveral quality criteria.\n",
    "\n",
    "for example:\n",
    "$MV \\rightarrow \\textit{fraction of missing values}$\n",
    "* $ 0 < MV < 0.3 $ Fill missing values with something meaningful, eg the mean value\n",
    "* $ 0.3 < MV < 0.5 $ Convert those features in categorical ones\n",
    "* $ MV > 0.5 $ Directly drop such features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another important thing to do good EDA is the dataset cleaning and check if data are properly shuffled. This is important if we want to perform solid statistical test on our data.\n",
    "\n",
    "It may happens that, to prove our capability to handle data, someone give a us feature that takes allways (or almost) the same value both in train and test set. For example the feature may be a day of the year and, if the dataset were sampled only in one day the feature appears to be just a constant. It can be a good choice to drop this feature to gain some computational memory and do not dirt the representation of data that our model will build. Before doing that we shold consider the case where the fature is constant in the train set but it is not in the test set. To remove the constant feature is still a good choice at all, since, if our model bases a portion of its prediction on this features those portion will be completely unreliable or, in the ML language the model will not generalize.\n",
    "\n",
    "Let's consider the reverse situation, where some values appear only in the train set and not in the test. We have to decide those particular values are important to build our hypothesis. A standard procedure in this case, if data are large enough, is to split from the train a subset for the validation where those values are exluded and see the performance of our model. One this is done we can consider to remove the feature or to let our model process those feature in a different way.\n",
    "\n",
    "It may happen that two features are completeley identical. We can remove one of those two columns, since the second does not contribute to information gain and slows down model training. This is easy to do for numeric features where we can do an element-wise comparison. We may have duplicated categorical features. How do we find them?\n",
    "\n",
    "We can start with the dtypes and choose only the categorical (often the int64, but be careful) and store their name in an iterable object, categrocial_features here.\n",
    "\n",
    "The most important part here is label encoding, that need do be done properly. Suppose we have two categorical features, f1 and f2. If we choose to encode the first unique value with 1, the second with 2 and so on we can detect if they are equal. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/c/springleaf-marketing-response/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jacopo/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (8,9,10,11,12,43,157,196,214,225,228,229,231,235,238) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/home/jacopo/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (8,9,10,11,12,43,157,167,177,196,214,225,228,229,231,235,238) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "DATA_FOLDER = # your path here\n",
    "\n",
    "train = pd.read_csv(os.path.join(DATA_FOLDER,'leaf_train.csv'))\n",
    "test  = pd.read_csv(os.path.join(DATA_FOLDER,'leaf_test.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape (5000, 1934)\n",
      "Test shape (5000, 1933)\n"
     ]
    }
   ],
   "source": [
    "print('Train shape', train.shape)\n",
    "print('Test shape',  test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 1934)\n",
      "(5000, 1933)\n"
     ]
    }
   ],
   "source": [
    "set_size = int(5e3)\n",
    "\n",
    "train = train[:set_size]\n",
    "test = test[:set_size]\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = train.target\n",
    "test_ID = test.ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>VAR_0001</th>\n",
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       "      <th>VAR_0003</th>\n",
       "      <th>VAR_0004</th>\n",
       "      <th>VAR_0005</th>\n",
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       "      <th>VAR_0008</th>\n",
       "      <th>VAR_0009</th>\n",
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       "      <th>VAR_1927</th>\n",
       "      <th>VAR_1928</th>\n",
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       "      <td>998</td>\n",
       "      <td>998</td>\n",
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       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>False</td>\n",
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       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
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       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
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       "    </tr>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>3464</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>H</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>3200</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
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       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>RCC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>R</td>\n",
       "      <td>72</td>\n",
       "      <td>261</td>\n",
       "      <td>2000</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>BRANCH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1934 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID VAR_0001  VAR_0002  VAR_0003  VAR_0004 VAR_0005  VAR_0006  VAR_0007  \\\n",
       "0   2        H       224         0      4300        C       0.0       0.0   \n",
       "1   4        H         7        53      4448        B       1.0       0.0   \n",
       "2   5        H       116         3      3464        C       0.0       0.0   \n",
       "3   7        H       240       300      3200        C       0.0       0.0   \n",
       "4   8        R        72       261      2000        N       0.0       0.0   \n",
       "\n",
       "  VAR_0008 VAR_0009  ... VAR_1926 VAR_1927 VAR_1928   VAR_1929  VAR_1930  \\\n",
       "0    False    False  ...       98       98      998  999999998       998   \n",
       "1    False    False  ...       98       98      998  999999998       998   \n",
       "2    False    False  ...       98       98      998  999999998       998   \n",
       "3    False    False  ...       98       98      998  999999998       998   \n",
       "4    False    False  ...       98       98      998  999999998       998   \n",
       "\n",
       "   VAR_1931  VAR_1932  VAR_1933  VAR_1934  target  \n",
       "0       998      9998      9998      IAPS       0  \n",
       "1       998      9998      9998      IAPS       0  \n",
       "2       998      9998      9998      IAPS       0  \n",
       "3       998      9998      9998       RCC       0  \n",
       "4       998      9998      9998    BRANCH       1  \n",
       "\n",
       "[5 rows x 1934 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>VAR_1929</th>\n",
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       "      <th>VAR_1934</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>192</td>\n",
       "      <td>3274</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>998</td>\n",
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       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
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       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1500</td>\n",
       "      <td>B</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>H</td>\n",
       "      <td>91</td>\n",
       "      <td>39</td>\n",
       "      <td>84500</td>\n",
       "      <td>C</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID VAR_0001  VAR_0002  VAR_0003  VAR_0004 VAR_0005  VAR_0006  VAR_0007  \\\n",
       "0   1        R       360        25      2251        B       2.0       2.0   \n",
       "1   3        R        74       192      3274        C       2.0       3.0   \n",
       "2   6        R        21        36      3500        C       1.0       1.0   \n",
       "3   9        R         8         2      1500        B       0.0       0.0   \n",
       "4  10        H        91        39     84500        C       8.0       3.0   \n",
       "\n",
       "  VAR_0008 VAR_0009  ... VAR_1925 VAR_1926 VAR_1927  VAR_1928   VAR_1929  \\\n",
       "0    False    False  ...        0       98       98       998  999999998   \n",
       "1    False    False  ...        0       98       98       998  999999998   \n",
       "2    False    False  ...        0       98       98       998  999999998   \n",
       "3    False    False  ...        0       98       98       998  999999998   \n",
       "4    False    False  ...        0       98       98       998  999999998   \n",
       "\n",
       "   VAR_1930  VAR_1931  VAR_1932  VAR_1933  VAR_1934  \n",
       "0       998       998      9998      9998      IAPS  \n",
       "1       998       998      9998      9998      IAPS  \n",
       "2       998       998      9998      9998      IAPS  \n",
       "3       998       998      9998      9998      IAPS  \n",
       "4       998       998      9998      9998      IAPS  \n",
       "\n",
       "[5 rows x 1933 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are almost 2000 anonymized variables! It's clear, some of them are categorical, some look like numeric. Some numeric feateures are integer typed, so probably they are event conters or dates. And others are of float type, but from the first few rows they look like integer-typed too, since fractional part is zero, but pandas treats them as `float` since there are NaN values in that features.   \n",
    "\n",
    "From the first glance we see train has one more column `target` which we should not forget to drop before fitting a classifier. We also see `ID` column is shared between train and test, which sometimes can be succesfully used to improve the score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also useful to know if there are any NaNs in the data. You should pay attention to columns with NaNs and the number of NaNs for each row can serve as a nice feature later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values are  0.00591266149870801 % of the whole\n",
      "[11  6 10 ... 10 10 11]\n",
      "289 1528\n"
     ]
    }
   ],
   "source": [
    "# Number of NaNs for each object\n",
    "nan_list = np.array(train.isnull().sum(axis=1))\n",
    "\n",
    "# we can easily obtain the missing value percentage of our dataset\n",
    "nan_perc = np.sum(nan_list)/(train.shape[0]*train.shape[1])\n",
    "print('Missing values are ',nan_perc,'% of the whole')\n",
    "\n",
    "print(nan_list)\n",
    "print(np.max(nan_list),np.argmax(nan_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 883., 4079.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,   38.]),\n",
       " array([  3.  ,   8.72,  14.44,  20.16,  25.88,  31.6 ,  37.32,  43.04,\n",
       "         48.76,  54.48,  60.2 ,  65.92,  71.64,  77.36,  83.08,  88.8 ,\n",
       "         94.52, 100.24, 105.96, 111.68, 117.4 , 123.12, 128.84, 134.56,\n",
       "        140.28, 146.  , 151.72, 157.44, 163.16, 168.88, 174.6 , 180.32,\n",
       "        186.04, 191.76, 197.48, 203.2 , 208.92, 214.64, 220.36, 226.08,\n",
       "        231.8 , 237.52, 243.24, 248.96, 254.68, 260.4 , 266.12, 271.84,\n",
       "        277.56, 283.28, 289.  ]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nan_list,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can see that most of the dataset features are completely filled. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID          0\n",
       "VAR_0001    0\n",
       "VAR_0002    0\n",
       "VAR_0003    0\n",
       "VAR_0004    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of NaNs for each column\n",
    "train.isnull().sum(axis=0).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All 1932 columns are anonimized which makes us to deduce the meaning of the features ourselves. We will now try to clean the dataset. \n",
    "\n",
    "It is usually convenient to concatenate train and test into one dataframe and do all feature engineering using it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jacopo/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1934)\n"
     ]
    }
   ],
   "source": [
    "traintest = pd.concat([train, test], axis = 0)\n",
    "print(traintest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we schould look for a constant features, such features do not provide any information and only make our dataset larger. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# `dropna = False` makes nunique treat NaNs as a distinct value\n",
    "feats_counts = train.nunique(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats_counts.sort_values()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found 5 constant features. The idea here is to translate what we found in a list and then use the drop method `loc()` method is used to access a group of rows and columns by label(s) or a boolean array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['VAR_0008', 'VAR_0009', 'VAR_0010', 'VAR_0011', 'VAR_0012', 'VAR_0018', 'VAR_0019', 'VAR_0020', 'VAR_0021', 'VAR_0022', 'VAR_0023', 'VAR_0024', 'VAR_0025', 'VAR_0026', 'VAR_0027', 'VAR_0028', 'VAR_0029', 'VAR_0030', 'VAR_0031', 'VAR_0032', 'VAR_0038', 'VAR_0039', 'VAR_0040', 'VAR_0041', 'VAR_0042', 'VAR_0043', 'VAR_0044', 'VAR_0106', 'VAR_0188', 'VAR_0189', 'VAR_0190', 'VAR_0191', 'VAR_0192', 'VAR_0193', 'VAR_0196', 'VAR_0197', 'VAR_0199', 'VAR_0202', 'VAR_0203', 'VAR_0207', 'VAR_0213', 'VAR_0214', 'VAR_0215', 'VAR_0216', 'VAR_0221', 'VAR_0222', 'VAR_0223', 'VAR_0229', 'VAR_0239', 'VAR_0840', 'VAR_0847', 'VAR_1428']\n"
     ]
    }
   ],
   "source": [
    "constant_features = feats_counts.loc[feats_counts==1].index.tolist()\n",
    "print (constant_features)\n",
    "\n",
    "\n",
    "traintest.drop(constant_features,axis = 1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove duplicated features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill the NAN values. Since our datasets are very large we need to do a piece-wise cleaning. Otherwise the kernel may die."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = 100\n",
    "\n",
    "for i in range(int(set_size/w)):\n",
    "    train[i*w:(i+1)*w].fillna('NaN', inplace=True)\n",
    "    test[i*w:(i+1)*w].fillna('NaN', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's encode each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3caec9b386c84a5e903226345e2a42f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1934), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_enc =  pd.DataFrame(index = train.index)\n",
    "\n",
    "for col in tqdm_notebook(traintest.columns):\n",
    "    train_enc[col] = train[col].factorize()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 1934)\n",
      "(5000, 1933)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting data frame is very very large, so we cannot just transpose it and use `.duplicated`. That is why we will use a simple loop. This step may take some time, have a coffe!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f713cab322d4f64ba5306cfb2c40d2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1934), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dup_cols = {}\n",
    "\n",
    "for i, c1 in enumerate(tqdm_notebook(train_enc.columns)):\n",
    "    for c2 in train_enc.columns[i + 1:]:\n",
    "        if c2 not in dup_cols and np.all(train_enc[c1] == train_enc[c2]):\n",
    "            dup_cols[c2] = c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'VAR_0227': 'ID',\n",
       " 'VAR_0228': 'ID',\n",
       " 'VAR_0013': 'VAR_0006',\n",
       " 'VAR_0009': 'VAR_0008',\n",
       " 'VAR_0010': 'VAR_0008',\n",
       " 'VAR_0011': 'VAR_0008',\n",
       " 'VAR_0012': 'VAR_0008',\n",
       " 'VAR_0018': 'VAR_0008',\n",
       " 'VAR_0019': 'VAR_0008',\n",
       " 'VAR_0020': 'VAR_0008',\n",
       " 'VAR_0021': 'VAR_0008',\n",
       " 'VAR_0022': 'VAR_0008',\n",
       " 'VAR_0023': 'VAR_0008',\n",
       " 'VAR_0024': 'VAR_0008',\n",
       " 'VAR_0025': 'VAR_0008',\n",
       " 'VAR_0026': 'VAR_0008',\n",
       " 'VAR_0027': 'VAR_0008',\n",
       " 'VAR_0028': 'VAR_0008',\n",
       " 'VAR_0029': 'VAR_0008',\n",
       " 'VAR_0030': 'VAR_0008',\n",
       " 'VAR_0031': 'VAR_0008',\n",
       " 'VAR_0032': 'VAR_0008',\n",
       " 'VAR_0038': 'VAR_0008',\n",
       " 'VAR_0039': 'VAR_0008',\n",
       " 'VAR_0040': 'VAR_0008',\n",
       " 'VAR_0041': 'VAR_0008',\n",
       " 'VAR_0042': 'VAR_0008',\n",
       " 'VAR_0043': 'VAR_0008',\n",
       " 'VAR_0044': 'VAR_0008',\n",
       " 'VAR_0106': 'VAR_0008',\n",
       " 'VAR_0188': 'VAR_0008',\n",
       " 'VAR_0189': 'VAR_0008',\n",
       " 'VAR_0190': 'VAR_0008',\n",
       " 'VAR_0191': 'VAR_0008',\n",
       " 'VAR_0192': 'VAR_0008',\n",
       " 'VAR_0193': 'VAR_0008',\n",
       " 'VAR_0196': 'VAR_0008',\n",
       " 'VAR_0197': 'VAR_0008',\n",
       " 'VAR_0199': 'VAR_0008',\n",
       " 'VAR_0202': 'VAR_0008',\n",
       " 'VAR_0203': 'VAR_0008',\n",
       " 'VAR_0214': 'VAR_0008',\n",
       " 'VAR_0215': 'VAR_0008',\n",
       " 'VAR_0216': 'VAR_0008',\n",
       " 'VAR_0221': 'VAR_0008',\n",
       " 'VAR_0222': 'VAR_0008',\n",
       " 'VAR_0223': 'VAR_0008',\n",
       " 'VAR_0229': 'VAR_0008',\n",
       " 'VAR_0239': 'VAR_0008',\n",
       " 'VAR_0847': 'VAR_0008',\n",
       " 'VAR_1428': 'VAR_0008',\n",
       " 'VAR_0201': 'VAR_0051',\n",
       " 'VAR_0238': 'VAR_0089',\n",
       " 'VAR_0114': 'VAR_0098',\n",
       " 'VAR_0130': 'VAR_0098',\n",
       " 'VAR_0115': 'VAR_0099',\n",
       " 'VAR_0181': 'VAR_0180',\n",
       " 'VAR_0182': 'VAR_0180',\n",
       " 'VAR_0195': 'VAR_0194',\n",
       " 'VAR_0213': 'VAR_0207',\n",
       " 'VAR_0840': 'VAR_0207',\n",
       " 'VAR_0210': 'VAR_0208',\n",
       " 'VAR_0211': 'VAR_0208',\n",
       " 'VAR_0394': 'VAR_0246',\n",
       " 'VAR_0395': 'VAR_0246',\n",
       " 'VAR_0396': 'VAR_0246',\n",
       " 'VAR_0397': 'VAR_0246',\n",
       " 'VAR_0398': 'VAR_0246',\n",
       " 'VAR_0399': 'VAR_0246',\n",
       " 'VAR_0411': 'VAR_0246',\n",
       " 'VAR_0412': 'VAR_0246',\n",
       " 'VAR_0438': 'VAR_0246',\n",
       " 'VAR_0445': 'VAR_0246',\n",
       " 'VAR_0446': 'VAR_0246',\n",
       " 'VAR_0449': 'VAR_0246',\n",
       " 'VAR_0459': 'VAR_0246',\n",
       " 'VAR_0463': 'VAR_0246',\n",
       " 'VAR_0526': 'VAR_0246',\n",
       " 'VAR_0527': 'VAR_0246',\n",
       " 'VAR_0528': 'VAR_0246',\n",
       " 'VAR_0529': 'VAR_0246',\n",
       " 'VAR_0530': 'VAR_0246',\n",
       " 'VAR_0357': 'VAR_0260',\n",
       " 'VAR_0512': 'VAR_0506',\n",
       " 'VAR_0671': 'VAR_0669',\n",
       " 'VAR_0672': 'VAR_0670',\n",
       " 'VAR_1036': 'VAR_0916',\n",
       " 'VAR_1628': 'VAR_1620',\n",
       " 'VAR_1849': 'VAR_1844',\n",
       " 'VAR_1846': 'VAR_1845',\n",
       " 'VAR_1847': 'VAR_1845',\n",
       " 'VAR_1850': 'VAR_1845'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to save them, as it takes long time to find these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "traintest.drop(dup_cols.keys(), axis = 1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID          5000\n",
       "VAR_0001       3\n",
       "VAR_0002     456\n",
       "VAR_0003     385\n",
       "VAR_0004    1929\n",
       "            ... \n",
       "VAR_1931       9\n",
       "VAR_1932      17\n",
       "VAR_1933     154\n",
       "VAR_1934       5\n",
       "target         2\n",
       "Length: 1934, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nunique = train.nunique(dropna=False)\n",
    "nunique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And build an histrogam of those values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a looks at the features with a huge number of unique values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>ID</th>\n",
       "      <th>VAR_0212</th>\n",
       "      <th>VAR_0227</th>\n",
       "      <th>VAR_0228</th>\n",
       "      <th>VAR_0541</th>\n",
       "      <th>VAR_0543</th>\n",
       "      <th>VAR_0704</th>\n",
       "      <th>VAR_0887</th>\n",
       "      <th>VAR_0899</th>\n",
       "      <th>VAR_1081</th>\n",
       "      <th>VAR_1082</th>\n",
       "      <th>VAR_1087</th>\n",
       "      <th>VAR_1179</th>\n",
       "      <th>VAR_1180</th>\n",
       "      <th>VAR_1181</th>\n",
       "      <th>VAR_1495</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>311951.0</td>\n",
       "      <td>311951.0</td>\n",
       "      <td>49463</td>\n",
       "      <td>116783</td>\n",
       "      <td>25619</td>\n",
       "      <td>19214</td>\n",
       "      <td>112871</td>\n",
       "      <td>76857</td>\n",
       "      <td>76857</td>\n",
       "      <td>116783</td>\n",
       "      <td>76857</td>\n",
       "      <td>76857</td>\n",
       "      <td>76857</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.207125e+10</td>\n",
       "      <td>2769488.0</td>\n",
       "      <td>2769488.0</td>\n",
       "      <td>303472</td>\n",
       "      <td>346196</td>\n",
       "      <td>28336</td>\n",
       "      <td>28336</td>\n",
       "      <td>346375</td>\n",
       "      <td>341365</td>\n",
       "      <td>341365</td>\n",
       "      <td>346196</td>\n",
       "      <td>341365</td>\n",
       "      <td>341365</td>\n",
       "      <td>176604</td>\n",
       "      <td>27542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2.654775e+10</td>\n",
       "      <td>654127.0</td>\n",
       "      <td>654127.0</td>\n",
       "      <td>94990</td>\n",
       "      <td>122601</td>\n",
       "      <td>35589</td>\n",
       "      <td>35589</td>\n",
       "      <td>121501</td>\n",
       "      <td>107267</td>\n",
       "      <td>107267</td>\n",
       "      <td>121501</td>\n",
       "      <td>107267</td>\n",
       "      <td>107267</td>\n",
       "      <td>58714</td>\n",
       "      <td>19238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7.757535e+10</td>\n",
       "      <td>3015088.0</td>\n",
       "      <td>3015088.0</td>\n",
       "      <td>20593</td>\n",
       "      <td>59490</td>\n",
       "      <td>5854</td>\n",
       "      <td>5204</td>\n",
       "      <td>61890</td>\n",
       "      <td>45794</td>\n",
       "      <td>47568</td>\n",
       "      <td>59490</td>\n",
       "      <td>45794</td>\n",
       "      <td>47568</td>\n",
       "      <td>47568</td>\n",
       "      <td>29182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6.042377e+10</td>\n",
       "      <td>118678.0</td>\n",
       "      <td>118678.0</td>\n",
       "      <td>10071</td>\n",
       "      <td>35708</td>\n",
       "      <td>2550</td>\n",
       "      <td>2266</td>\n",
       "      <td>34787</td>\n",
       "      <td>20475</td>\n",
       "      <td>23647</td>\n",
       "      <td>34708</td>\n",
       "      <td>20475</td>\n",
       "      <td>23647</td>\n",
       "      <td>23647</td>\n",
       "      <td>23932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4995</td>\n",
       "      <td>9725</td>\n",
       "      <td>3.202406e+10</td>\n",
       "      <td>2913739.0</td>\n",
       "      <td>2913739.0</td>\n",
       "      <td>50054</td>\n",
       "      <td>89996</td>\n",
       "      <td>16555</td>\n",
       "      <td>16555</td>\n",
       "      <td>92321</td>\n",
       "      <td>81099</td>\n",
       "      <td>81099</td>\n",
       "      <td>89996</td>\n",
       "      <td>81099</td>\n",
       "      <td>81099</td>\n",
       "      <td>42321</td>\n",
       "      <td>21348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4996</td>\n",
       "      <td>9730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1713017.0</td>\n",
       "      <td>1713017.0</td>\n",
       "      <td>227</td>\n",
       "      <td>1000</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>500</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>1000</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>2647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4997</td>\n",
       "      <td>9736</td>\n",
       "      <td>2.454042e+10</td>\n",
       "      <td>1564676.0</td>\n",
       "      <td>1564676.0</td>\n",
       "      <td>13943</td>\n",
       "      <td>15343</td>\n",
       "      <td>3844</td>\n",
       "      <td>3844</td>\n",
       "      <td>19193</td>\n",
       "      <td>14444</td>\n",
       "      <td>14959</td>\n",
       "      <td>15193</td>\n",
       "      <td>14444</td>\n",
       "      <td>14959</td>\n",
       "      <td>14959</td>\n",
       "      <td>13943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4998</td>\n",
       "      <td>9738</td>\n",
       "      <td>2.973045e+10</td>\n",
       "      <td>2400564.0</td>\n",
       "      <td>2400564.0</td>\n",
       "      <td>440</td>\n",
       "      <td>726</td>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>4290</td>\n",
       "      <td>505</td>\n",
       "      <td>505</td>\n",
       "      <td>726</td>\n",
       "      <td>505</td>\n",
       "      <td>505</td>\n",
       "      <td>505</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4999</td>\n",
       "      <td>9740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2696291.0</td>\n",
       "      <td>2696291.0</td>\n",
       "      <td>8159</td>\n",
       "      <td>19077</td>\n",
       "      <td>1967</td>\n",
       "      <td>1788</td>\n",
       "      <td>29792</td>\n",
       "      <td>12106</td>\n",
       "      <td>12106</td>\n",
       "      <td>18777</td>\n",
       "      <td>12106</td>\n",
       "      <td>12106</td>\n",
       "      <td>12106</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID      VAR_0212   VAR_0227   VAR_0228  VAR_0541  VAR_0543  VAR_0704  \\\n",
       "0        2           NaN   311951.0   311951.0     49463    116783     25619   \n",
       "1        4  9.207125e+10  2769488.0  2769488.0    303472    346196     28336   \n",
       "2        5  2.654775e+10   654127.0   654127.0     94990    122601     35589   \n",
       "3        7  7.757535e+10  3015088.0  3015088.0     20593     59490      5854   \n",
       "4        8  6.042377e+10   118678.0   118678.0     10071     35708      2550   \n",
       "...    ...           ...        ...        ...       ...       ...       ...   \n",
       "4995  9725  3.202406e+10  2913739.0  2913739.0     50054     89996     16555   \n",
       "4996  9730           NaN  1713017.0  1713017.0       227      1000       342   \n",
       "4997  9736  2.454042e+10  1564676.0  1564676.0     13943     15343      3844   \n",
       "4998  9738  2.973045e+10  2400564.0  2400564.0       440       726       577   \n",
       "4999  9740           NaN  2696291.0  2696291.0      8159     19077      1967   \n",
       "\n",
       "      VAR_0887  VAR_0899  VAR_1081  VAR_1082  VAR_1087  VAR_1179  VAR_1180  \\\n",
       "0        19214    112871     76857     76857    116783     76857     76857   \n",
       "1        28336    346375    341365    341365    346196    341365    341365   \n",
       "2        35589    121501    107267    107267    121501    107267    107267   \n",
       "3         5204     61890     45794     47568     59490     45794     47568   \n",
       "4         2266     34787     20475     23647     34708     20475     23647   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "4995     16555     92321     81099     81099     89996     81099     81099   \n",
       "4996       342       500       227       227      1000       227       227   \n",
       "4997      3844     19193     14444     14959     15193     14444     14959   \n",
       "4998       577      4290       505       505       726       505       505   \n",
       "4999      1788     29792     12106     12106     18777     12106     12106   \n",
       "\n",
       "      VAR_1181  VAR_1495  \n",
       "0        76857     50000  \n",
       "1       176604     27542  \n",
       "2        58714     19238  \n",
       "3        47568     29182  \n",
       "4        23647     23932  \n",
       "...        ...       ...  \n",
       "4995     42321     21348  \n",
       "4996       227      2647  \n",
       "4997     14959     13943  \n",
       "4998       505       440  \n",
       "4999     12106     16000  \n",
       "\n",
       "[5000 rows x 16 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (nunique.astype(float)/train.shape[0] > 0.8)\n",
    "train.loc[:, mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values are not float, they are integer, so these features are likely to be even counts. Let's look at another pack of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>VAR_0200</th>\n",
       "      <th>VAR_0241</th>\n",
       "      <th>VAR_0293</th>\n",
       "      <th>VAR_0313</th>\n",
       "      <th>VAR_0585</th>\n",
       "      <th>VAR_0609</th>\n",
       "      <th>VAR_0648</th>\n",
       "      <th>VAR_0649</th>\n",
       "      <th>VAR_0652</th>\n",
       "      <th>VAR_0705</th>\n",
       "      <th>...</th>\n",
       "      <th>VAR_1374</th>\n",
       "      <th>VAR_1489</th>\n",
       "      <th>VAR_1494</th>\n",
       "      <th>VAR_1496</th>\n",
       "      <th>VAR_1497</th>\n",
       "      <th>VAR_1801</th>\n",
       "      <th>VAR_1802</th>\n",
       "      <th>VAR_1809</th>\n",
       "      <th>VAR_1810</th>\n",
       "      <th>VAR_1859</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>FT LAUDERDALE</td>\n",
       "      <td>33324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48233</td>\n",
       "      <td>73627</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>49463</td>\n",
       "      <td>...</td>\n",
       "      <td>39926</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>49463</td>\n",
       "      <td>49463</td>\n",
       "      <td>30537</td>\n",
       "      <td>30537</td>\n",
       "      <td>999999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SANTEE</td>\n",
       "      <td>92071.0</td>\n",
       "      <td>163400.0</td>\n",
       "      <td>163400.0</td>\n",
       "      <td>2407</td>\n",
       "      <td>3502</td>\n",
       "      <td>26051</td>\n",
       "      <td>2805</td>\n",
       "      <td>2625</td>\n",
       "      <td>798</td>\n",
       "      <td>...</td>\n",
       "      <td>2847</td>\n",
       "      <td>32597</td>\n",
       "      <td>33667</td>\n",
       "      <td>27542</td>\n",
       "      <td>11222</td>\n",
       "      <td>5296</td>\n",
       "      <td>5296</td>\n",
       "      <td>2847</td>\n",
       "      <td>2847</td>\n",
       "      <td>2684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>REEDSVILLE</td>\n",
       "      <td>26547.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>10161</td>\n",
       "      <td>2116</td>\n",
       "      <td>7071</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>999999997</td>\n",
       "      <td>12277</td>\n",
       "      <td>22523</td>\n",
       "      <td>15452</td>\n",
       "      <td>11262</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LIBERTY</td>\n",
       "      <td>77575.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>236</td>\n",
       "      <td>1903</td>\n",
       "      <td>20593</td>\n",
       "      <td>18802</td>\n",
       "      <td>29182</td>\n",
       "      <td>183</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>45508</td>\n",
       "      <td>59190</td>\n",
       "      <td>29182</td>\n",
       "      <td>9865</td>\n",
       "      <td>286</td>\n",
       "      <td>2060</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>FRANKFORT</td>\n",
       "      <td>60423.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6183</td>\n",
       "      <td>6693</td>\n",
       "      <td>10071</td>\n",
       "      <td>10071</td>\n",
       "      <td>20947</td>\n",
       "      <td>1918</td>\n",
       "      <td>...</td>\n",
       "      <td>3357</td>\n",
       "      <td>10071</td>\n",
       "      <td>20947</td>\n",
       "      <td>20947</td>\n",
       "      <td>20947</td>\n",
       "      <td>10404</td>\n",
       "      <td>13576</td>\n",
       "      <td>3357</td>\n",
       "      <td>3357</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>SPRING</td>\n",
       "      <td>77379.0</td>\n",
       "      <td>127200.0</td>\n",
       "      <td>127200.0</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>18877</td>\n",
       "      <td>1025</td>\n",
       "      <td>4000</td>\n",
       "      <td>692</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>19902</td>\n",
       "      <td>26755</td>\n",
       "      <td>22755</td>\n",
       "      <td>13378</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>999999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>GRESHAM</td>\n",
       "      <td>97030.0</td>\n",
       "      <td>252970.0</td>\n",
       "      <td>252970.0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>5818</td>\n",
       "      <td>5818</td>\n",
       "      <td>23665</td>\n",
       "      <td>116</td>\n",
       "      <td>...</td>\n",
       "      <td>157</td>\n",
       "      <td>5818</td>\n",
       "      <td>23665</td>\n",
       "      <td>23665</td>\n",
       "      <td>23665</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>999999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>WARNER ROBINS</td>\n",
       "      <td>31098.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>2961</td>\n",
       "      <td>1015</td>\n",
       "      <td>1544</td>\n",
       "      <td>219</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>3976</td>\n",
       "      <td>4681</td>\n",
       "      <td>3137</td>\n",
       "      <td>2341</td>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>1365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SAN ANTONIO</td>\n",
       "      <td>78212.0</td>\n",
       "      <td>68120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>20359</td>\n",
       "      <td>820</td>\n",
       "      <td>3048</td>\n",
       "      <td>412</td>\n",
       "      <td>...</td>\n",
       "      <td>308</td>\n",
       "      <td>23389</td>\n",
       "      <td>28314</td>\n",
       "      <td>21942</td>\n",
       "      <td>5663</td>\n",
       "      <td>1580</td>\n",
       "      <td>3739</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>5209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>362</td>\n",
       "      <td>1127</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>118</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>1197</td>\n",
       "      <td>1197</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>MURFREESBORO</td>\n",
       "      <td>37129.0</td>\n",
       "      <td>33850.0</td>\n",
       "      <td>33850.0</td>\n",
       "      <td>921</td>\n",
       "      <td>921</td>\n",
       "      <td>6088</td>\n",
       "      <td>6088</td>\n",
       "      <td>14233</td>\n",
       "      <td>504</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>6088</td>\n",
       "      <td>14233</td>\n",
       "      <td>14233</td>\n",
       "      <td>14233</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>999999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>CARTERSVILLE</td>\n",
       "      <td>30120.0</td>\n",
       "      <td>33080.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>139</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>136</td>\n",
       "      <td>111</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>BETHLEHEM</td>\n",
       "      <td>18018.0</td>\n",
       "      <td>30800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>383</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>752</td>\n",
       "      <td>1158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>GIG HARBOR</td>\n",
       "      <td>98335.0</td>\n",
       "      <td>813900.0</td>\n",
       "      <td>813900.0</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>14359</td>\n",
       "      <td>14359</td>\n",
       "      <td>44062</td>\n",
       "      <td>1663</td>\n",
       "      <td>...</td>\n",
       "      <td>153</td>\n",
       "      <td>14359</td>\n",
       "      <td>44062</td>\n",
       "      <td>44062</td>\n",
       "      <td>44062</td>\n",
       "      <td>3347</td>\n",
       "      <td>3347</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>BAKERSFIELD</td>\n",
       "      <td>93308.0</td>\n",
       "      <td>137497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3055</td>\n",
       "      <td>5752</td>\n",
       "      <td>14112</td>\n",
       "      <td>9006</td>\n",
       "      <td>36563</td>\n",
       "      <td>434</td>\n",
       "      <td>...</td>\n",
       "      <td>2462</td>\n",
       "      <td>23372</td>\n",
       "      <td>53915</td>\n",
       "      <td>36563</td>\n",
       "      <td>17972</td>\n",
       "      <td>7864</td>\n",
       "      <td>7864</td>\n",
       "      <td>2462</td>\n",
       "      <td>2462</td>\n",
       "      <td>3909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>HERNDON</td>\n",
       "      <td>20171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>10040</td>\n",
       "      <td>10040</td>\n",
       "      <td>11119</td>\n",
       "      <td>407</td>\n",
       "      <td>...</td>\n",
       "      <td>641</td>\n",
       "      <td>10040</td>\n",
       "      <td>11119</td>\n",
       "      <td>11119</td>\n",
       "      <td>11119</td>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "      <td>641</td>\n",
       "      <td>641</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>SHELBY GAP</td>\n",
       "      <td>41563.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>4880</td>\n",
       "      <td>4880</td>\n",
       "      <td>5080</td>\n",
       "      <td>545</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>8620</td>\n",
       "      <td>9007</td>\n",
       "      <td>5080</td>\n",
       "      <td>4504</td>\n",
       "      <td>545</td>\n",
       "      <td>545</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>999999996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>ONTARIO</td>\n",
       "      <td>91761.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1061</td>\n",
       "      <td>3597</td>\n",
       "      <td>12900</td>\n",
       "      <td>6691</td>\n",
       "      <td>11615</td>\n",
       "      <td>1487</td>\n",
       "      <td>...</td>\n",
       "      <td>3189</td>\n",
       "      <td>21735</td>\n",
       "      <td>28040</td>\n",
       "      <td>14125</td>\n",
       "      <td>9347</td>\n",
       "      <td>4361</td>\n",
       "      <td>4361</td>\n",
       "      <td>3189</td>\n",
       "      <td>3189</td>\n",
       "      <td>2645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>MEMPHIS</td>\n",
       "      <td>38134.0</td>\n",
       "      <td>36350.0</td>\n",
       "      <td>36350.0</td>\n",
       "      <td>788</td>\n",
       "      <td>6317</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27888</td>\n",
       "      <td>278</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>31089</td>\n",
       "      <td>31680</td>\n",
       "      <td>27888</td>\n",
       "      <td>15840</td>\n",
       "      <td>888</td>\n",
       "      <td>6687</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>5799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>BAKERSFIED</td>\n",
       "      <td>93304.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>3730</td>\n",
       "      <td>4173</td>\n",
       "      <td>13898</td>\n",
       "      <td>13898</td>\n",
       "      <td>18866</td>\n",
       "      <td>2971</td>\n",
       "      <td>...</td>\n",
       "      <td>513</td>\n",
       "      <td>13898</td>\n",
       "      <td>18866</td>\n",
       "      <td>18866</td>\n",
       "      <td>18866</td>\n",
       "      <td>6224</td>\n",
       "      <td>6224</td>\n",
       "      <td>514</td>\n",
       "      <td>514</td>\n",
       "      <td>2244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>ALTAMONTE SPRINGS</td>\n",
       "      <td>32714.0</td>\n",
       "      <td>21536.0</td>\n",
       "      <td>21536.0</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>3524</td>\n",
       "      <td>3524</td>\n",
       "      <td>7233</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>521</td>\n",
       "      <td>3524</td>\n",
       "      <td>7233</td>\n",
       "      <td>7233</td>\n",
       "      <td>7233</td>\n",
       "      <td>2314</td>\n",
       "      <td>2314</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>BARNESVILLE</td>\n",
       "      <td>18214.0</td>\n",
       "      <td>57870.0</td>\n",
       "      <td>57870.0</td>\n",
       "      <td>8362</td>\n",
       "      <td>11494</td>\n",
       "      <td>129873</td>\n",
       "      <td>129873</td>\n",
       "      <td>143000</td>\n",
       "      <td>4276</td>\n",
       "      <td>...</td>\n",
       "      <td>6503</td>\n",
       "      <td>170922</td>\n",
       "      <td>185442</td>\n",
       "      <td>143000</td>\n",
       "      <td>92721</td>\n",
       "      <td>12127</td>\n",
       "      <td>12127</td>\n",
       "      <td>6503</td>\n",
       "      <td>6503</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>HOUSTON</td>\n",
       "      <td>77015.0</td>\n",
       "      <td>85000.0</td>\n",
       "      <td>85000.0</td>\n",
       "      <td>481</td>\n",
       "      <td>1699</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>1392</td>\n",
       "      <td>...</td>\n",
       "      <td>5839</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>5565</td>\n",
       "      <td>5565</td>\n",
       "      <td>5839</td>\n",
       "      <td>5839</td>\n",
       "      <td>1368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>PERU</td>\n",
       "      <td>46970.0</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>101900.0</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>TACOMA</td>\n",
       "      <td>98444.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999996</td>\n",
       "      <td>1270</td>\n",
       "      <td>204</td>\n",
       "      <td>2248</td>\n",
       "      <td>949</td>\n",
       "      <td>...</td>\n",
       "      <td>334</td>\n",
       "      <td>1474</td>\n",
       "      <td>3605</td>\n",
       "      <td>2248</td>\n",
       "      <td>1803</td>\n",
       "      <td>1016</td>\n",
       "      <td>1016</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>OZARK</td>\n",
       "      <td>65721.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2458</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>999999998</td>\n",
       "      <td>2015</td>\n",
       "      <td>2458</td>\n",
       "      <td>2458</td>\n",
       "      <td>2458</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             VAR_0200  VAR_0241  VAR_0293  VAR_0313   VAR_0585   VAR_0609  \\\n",
       "0       FT LAUDERDALE   33324.0       0.0       0.0      48233      73627   \n",
       "1              SANTEE   92071.0  163400.0  163400.0       2407       3502   \n",
       "2          REEDSVILLE   26547.0       0.0       0.0  999999997  999999997   \n",
       "3             LIBERTY   77575.0       0.0       0.0        236       1903   \n",
       "4           FRANKFORT   60423.0       0.0       0.0       6183       6693   \n",
       "5              SPRING   77379.0  127200.0  127200.0        412        412   \n",
       "6             GRESHAM   97030.0  252970.0  252970.0  999999996  999999996   \n",
       "7       WARNER ROBINS   31098.0       0.0       0.0  999999996  999999996   \n",
       "8         SAN ANTONIO   78212.0   68120.0       0.0  999999996  999999996   \n",
       "9          NORRISTOWN   19401.0       0.0       0.0        362       1127   \n",
       "10       MURFREESBORO   37129.0   33850.0   33850.0        921        921   \n",
       "11       CARTERSVILLE   30120.0   33080.0       0.0  999999996        139   \n",
       "12          BETHLEHEM   18018.0   30800.0       0.0  999999996  999999996   \n",
       "13         GIG HARBOR   98335.0  813900.0  813900.0       3231       3231   \n",
       "14        BAKERSFIELD   93308.0  137497.0       0.0       3055       5752   \n",
       "15            HERNDON   20171.0       0.0       0.0  999999996  999999996   \n",
       "16         SHELBY GAP   41563.0   35000.0       0.0  999999996  999999996   \n",
       "17            ONTARIO   91761.0       0.0       0.0       1061       3597   \n",
       "18            MEMPHIS   38134.0   36350.0   36350.0        788       6317   \n",
       "19         BAKERSFIED   93304.0   46000.0   46000.0       3730       4173   \n",
       "20  ALTAMONTE SPRINGS   32714.0   21536.0   21536.0        179        179   \n",
       "21        BARNESVILLE   18214.0   57870.0   57870.0       8362      11494   \n",
       "22            HOUSTON   77015.0   85000.0   85000.0        481       1699   \n",
       "23               PERU   46970.0   43500.0  101900.0  999999999  999999999   \n",
       "24             TACOMA   98444.0       0.0       0.0  999999998  999999996   \n",
       "25              OZARK   65721.0       0.0       0.0  999999998  999999998   \n",
       "\n",
       "     VAR_0648   VAR_0649   VAR_0652  VAR_0705  ...   VAR_1374   VAR_1489  \\\n",
       "0   999999997  999999996  999999996     49463  ...      39926  999999997   \n",
       "1       26051       2805       2625       798  ...       2847      32597   \n",
       "2       10161       2116       7071    -99999  ...  999999997      12277   \n",
       "3       20593      18802      29182       183  ...         14      45508   \n",
       "4       10071      10071      20947      1918  ...       3357      10071   \n",
       "5       18877       1025       4000       692  ...         88      19902   \n",
       "6        5818       5818      23665       116  ...        157       5818   \n",
       "7        2961       1015       1544       219  ...        150       3976   \n",
       "8       20359        820       3048       412  ...        308      23389   \n",
       "9   999999996  999999996  999999996    -99999  ...        118  999999996   \n",
       "10       6088       6088      14233       504  ...         11       6088   \n",
       "11  999999997  999999996  999999996    -99999  ...        136  999999997   \n",
       "12  999999997  999999996  999999996       383  ...          0  999999997   \n",
       "13      14359      14359      44062      1663  ...        153      14359   \n",
       "14      14112       9006      36563       434  ...       2462      23372   \n",
       "15      10040      10040      11119       407  ...        641      10040   \n",
       "16       4880       4880       5080       545  ...         55       8620   \n",
       "17      12900       6691      11615      1487  ...       3189      21735   \n",
       "18      27457      27457      27888       278  ...         29      31089   \n",
       "19      13898      13898      18866      2971  ...        513      13898   \n",
       "20       3524       3524       7233       244  ...        521       3524   \n",
       "21     129873     129873     143000      4276  ...       6503     170922   \n",
       "22  999999997  999999996  999999996      1392  ...       5839  999999997   \n",
       "23  999999999  999999999  999999999    -99999  ...  999999999  999999999   \n",
       "24       1270        204       2248       949  ...        334       1474   \n",
       "25       2015       2015       2458    -99999  ...  999999998       2015   \n",
       "\n",
       "     VAR_1494   VAR_1496   VAR_1497   VAR_1801   VAR_1802   VAR_1809  \\\n",
       "0   999999997  999999997  999999997      49463      49463      30537   \n",
       "1       33667      27542      11222       5296       5296       2847   \n",
       "2       22523      15452      11262  999999997  999999997  999999997   \n",
       "3       59190      29182       9865        286       2060         14   \n",
       "4       20947      20947      20947      10404      13576       3357   \n",
       "5       26755      22755      13378       1237       1237         88   \n",
       "6       23665      23665      23665        143        143        157   \n",
       "7        4681       3137       2341        350        350        150   \n",
       "8       28314      21942       5663       1580       3739        308   \n",
       "9   999999996  999999996  999999996       1197       1197        118   \n",
       "10      14233      14233      14233        989        989         11   \n",
       "11  999999997  999999997  999999997        621        621        136   \n",
       "12  999999997  999999997  999999997        752       1158          0   \n",
       "13      44062      44062      44062       3347       3347        153   \n",
       "14      53915      36563      17972       7864       7864       2462   \n",
       "15      11119      11119      11119        359        359        641   \n",
       "16       9007       5080       4504        545        545         55   \n",
       "17      28040      14125       9347       4361       4361       3189   \n",
       "18      31680      27888      15840        888       6687         29   \n",
       "19      18866      18866      18866       6224       6224        514   \n",
       "20       7233       7233       7233       2314       2314        521   \n",
       "21     185442     143000      92721      12127      12127       6503   \n",
       "22  999999997  999999997  999999997       5565       5565       5839   \n",
       "23  999999999  999999999  999999999  999999999  999999999  999999999   \n",
       "24       3605       2248       1803       1016       1016        334   \n",
       "25       2458       2458       2458  999999998  999999998  999999998   \n",
       "\n",
       "     VAR_1810   VAR_1859  \n",
       "0       30537  999999997  \n",
       "1        2847       2684  \n",
       "2   999999997  999999997  \n",
       "3          14       1774  \n",
       "4        3357        568  \n",
       "5          88  999999998  \n",
       "6         157  999999997  \n",
       "7         150       1365  \n",
       "8         308       5209  \n",
       "9         118        815  \n",
       "10         11  999999998  \n",
       "11        111        621  \n",
       "12          0        369  \n",
       "13        153          0  \n",
       "14       2462       3909  \n",
       "15        641        359  \n",
       "16         55  999999996  \n",
       "17       3189       2645  \n",
       "18         29       5799  \n",
       "19        514       2244  \n",
       "20        521          0  \n",
       "21       6503       3530  \n",
       "22       5839       1368  \n",
       "23  999999999  999999999  \n",
       "24        334       1016  \n",
       "25  999999998  999999998  \n",
       "\n",
       "[26 rows x 63 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (nunique.astype(float)/train.shape[0] < 0.8) & (nunique.astype(float)/train.shape[0] > 0.4)\n",
    "train.loc[:25, mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These look like counts too. First thing to notice is the 23th line: 99999.., -99999 values look like NaNs so we should probably built a related feature. Second: the columns are sometimes placed next to each other, so the columns are probably grouped together and we can disentangle that.  \n",
    "Our conclusion: there are no floating point variables, there are some counts variables, which we will treat as numeric. \n",
    "\n",
    "And finally, let's pick one variable (in this case 'VAR_0015') from the third group of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     3595\n",
       "1.0      936\n",
       "2.0      294\n",
       "3.0       94\n",
       "4.0       38\n",
       "5.0       21\n",
       "6.0       12\n",
       "7.0        4\n",
       "8.0        2\n",
       "10.0       2\n",
       "14.0       1\n",
       "9.0        1\n",
       "Name: VAR_0015, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['VAR_0015'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = list(train.select_dtypes(include=['object']).columns)\n",
    "num_cols = list(train.select_dtypes(exclude=['object']).columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace Nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.replace('NaN', -999, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate how many times one feature is greater than the other and create cross tabel out of it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats = num_cols[:42]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, we see interesting patterns here. There are blocks of features where one is strictly greater than the other. So we can hypothesize, that each column correspondes to cumulative counts, e.g. feature number one is counts in first month, second -- total count number in first two month and so on. So we immediately understand what features we should generate to make tree-based models more efficient: the differences between consecutive values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VAR_0002, VAR_0003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.152e+03, 8.430e+02, 3.940e+02, 5.610e+02, 3.610e+02, 2.280e+02,\n",
       "        2.720e+02, 2.560e+02, 1.300e+02, 1.160e+02, 1.110e+02, 6.000e+01,\n",
       "        7.700e+01, 4.400e+01, 3.400e+01, 5.100e+01, 1.900e+01, 2.100e+01,\n",
       "        3.700e+01, 2.700e+01, 1.800e+01, 2.100e+01, 2.000e+01, 1.500e+01,\n",
       "        2.200e+01, 2.000e+01, 5.000e+00, 4.000e+00, 5.000e+00, 3.000e+00,\n",
       "        6.000e+00, 9.000e+00, 2.000e+00, 0.000e+00, 2.000e+00, 3.000e+00,\n",
       "        1.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 1.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 4.700e+01]),\n",
       " array([  0.  ,  19.98,  39.96,  59.94,  79.92,  99.9 , 119.88, 139.86,\n",
       "        159.84, 179.82, 199.8 , 219.78, 239.76, 259.74, 279.72, 299.7 ,\n",
       "        319.68, 339.66, 359.64, 379.62, 399.6 , 419.58, 439.56, 459.54,\n",
       "        479.52, 499.5 , 519.48, 539.46, 559.44, 579.42, 599.4 , 619.38,\n",
       "        639.36, 659.34, 679.32, 699.3 , 719.28, 739.26, 759.24, 779.22,\n",
       "        799.2 , 819.18, 839.16, 859.14, 879.12, 899.1 , 919.08, 939.06,\n",
       "        959.04, 979.02, 999.  ]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['VAR_0002'],bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.426e+03, 6.620e+02, 4.670e+02, 6.310e+02, 3.480e+02, 2.160e+02,\n",
       "        2.440e+02, 2.370e+02, 1.290e+02, 1.190e+02, 9.700e+01, 6.900e+01,\n",
       "        5.800e+01, 5.500e+01, 2.800e+01, 3.200e+01, 3.100e+01, 1.300e+01,\n",
       "        2.400e+01, 2.100e+01, 1.300e+01, 9.000e+00, 5.000e+00, 5.000e+00,\n",
       "        3.000e+00, 3.000e+00, 2.000e+00, 0.000e+00, 1.000e+00, 1.000e+00,\n",
       "        1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 4.900e+01]),\n",
       " array([  0.  ,  19.98,  39.96,  59.94,  79.92,  99.9 , 119.88, 139.86,\n",
       "        159.84, 179.82, 199.8 , 219.78, 239.76, 259.74, 279.72, 299.7 ,\n",
       "        319.68, 339.66, 359.64, 379.62, 399.6 , 419.58, 439.56, 459.54,\n",
       "        479.52, 499.5 , 519.48, 539.46, 559.44, 579.42, 599.4 , 619.38,\n",
       "        639.36, 659.34, 679.32, 699.3 , 719.28, 739.26, 759.24, 779.22,\n",
       "        799.2 , 819.18, 839.16, 859.14, 879.12, 899.1 , 919.08, 939.06,\n",
       "        959.04, 979.02, 999.  ]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['VAR_0003'],bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12     167\n",
       "24     157\n",
       "36     129\n",
       "13     103\n",
       "60     103\n",
       "      ... \n",
       "828      1\n",
       "760      1\n",
       "704      1\n",
       "684      1\n",
       "235      1\n",
       "Name: VAR_0002, Length: 456, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['VAR_0002'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      568\n",
       "24     118\n",
       "12     112\n",
       "60     112\n",
       "36      82\n",
       "      ... \n",
       "371      1\n",
       "385      1\n",
       "397      1\n",
       "603      1\n",
       "405      1\n",
       "Name: VAR_0003, Length: 385, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['VAR_0003'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see there is something special about 12, 24 and so on, sowe can create another feature x mod 12. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1065.,  230.,    0.,  301.,    0.,  240.,    0.,  870.,    0.,\n",
       "         224.,  304.,    0.,  227.,    0.,  822.,    0.,  198.,    0.,\n",
       "         304.,  215.]),\n",
       " array([ 0.  ,  0.55,  1.1 ,  1.65,  2.2 ,  2.75,  3.3 ,  3.85,  4.4 ,\n",
       "         4.95,  5.5 ,  6.05,  6.6 ,  7.15,  7.7 ,  8.25,  8.8 ,  9.35,\n",
       "         9.9 , 10.45, 11.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z2r/R9SyTL6mPsBPY2JY3Ag9MsJahLfVTNhfdBB71sJSuAG4Afpzkh63t81X10ARr0sv3MeCeNhh5CrhxwvWMRVXtTrIDeIzBFWaPc5o/siDJvcB7gPOTzAK3ALcC9yXZxOCD7rrJVTg8H8MgSR1ZjtM7kqRjMPQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSR/4fi9SoBVrA3wAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['VAR_0004_mod12'] = train['VAR_0004'] % 12\n",
    "\n",
    "plt.hist(train['VAR_0004_mod12'],bins=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VAR_0004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2027.,   52.,   55.,   50.,   86.,   74.,   72.,   42.,   51.,\n",
       "          34.,   98.,   41.,   57.,   40.,   64.,   73.,   48.,   33.,\n",
       "          55.,   53.,  104.,   65.,   90.,   62.,   51.,   87.,   48.,\n",
       "          52.,   47.,   54.,  107.,   54.,   58.,   45.,   43.,   50.,\n",
       "          69.,   68.,   70.,   56.,  107.,   47.,   61.,   47.,   62.,\n",
       "          51.,   66.,   60.,   70.,   44.]),\n",
       " array([ 0.  ,  0.98,  1.96,  2.94,  3.92,  4.9 ,  5.88,  6.86,  7.84,\n",
       "         8.82,  9.8 , 10.78, 11.76, 12.74, 13.72, 14.7 , 15.68, 16.66,\n",
       "        17.64, 18.62, 19.6 , 20.58, 21.56, 22.54, 23.52, 24.5 , 25.48,\n",
       "        26.46, 27.44, 28.42, 29.4 , 30.38, 31.36, 32.34, 33.32, 34.3 ,\n",
       "        35.28, 36.26, 37.24, 38.22, 39.2 , 40.18, 41.16, 42.14, 43.12,\n",
       "        44.1 , 45.08, 46.06, 47.04, 48.02, 49.  ]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aZ1Vtr6rJqpqcmJgYtYuSpM5IoZ/kNfQC/4tV9WWAqjpcVa9W1Y+BL/CTKZxpYF3f4muBg6NsX5K0OKOcvRPgLmBfVX2ur766r9kHgb3d453AdUlel+RCYAPwzWG3L0lavFHO3rkM+DDwdJI9Xe1TwPVJNtKbujkAfBSgqp5J8gDwbXpn/tzkmTuStLyGDv2q+gsGz9M/Ms8ytwG3DbtNSdJo/EauJDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1ZJSfYZBOC+u3/fHA+oHb37fMPVkerY0X2hzzUjH0Vxj/c0iaj6E/AgP2zDSuv5t/f52JDP0lMFcYzGUlhIQBKJ0ZDH0tymI/0E6lxfbVD67hjet9cSr/rRf791/q9kulydBvcU98LqfLG3HWmfShsljjConFrn8+p9t7e1x///nWs9RjPt3fw6kaeJna08bk5GRNTU0Ntezp/o8/a5j/9Kdq72Op9+jOlL+ZtNRG+XBKsruqJge+ZuhL0ulnqUJ/2b+clWRTkmeT7E+ybbm3L0ktW9bQT7IK+F3gauAietfTvWg5+yBJLVvuPf1LgP1V9UJV/T/gfmDzMvdBkpq13GfvrAFe7Hs+Dbz7+EZJtgJbu6d/l+TZIbd3HvA3Qy57JnPcbXHcK1B+e86XFjLufzbXC8sd+hlQO+FIclVtB7aPvLFkaq6DGSuZ426L427LqONe7umdaWBd3/O1wMFl7oMkNWu5Q/8JYEOSC5O8FrgO2LnMfZCkZi3r9E5VHUtyM/AVYBWwo6qeWcJNjjxFdIZy3G1x3G0Zadyn/ZezJEnj45WzJKkhhr4kNWRFhn5LP/WQZEeSI0n29tXOTbIryXPd/Tmnso/jlmRdkq8l2ZfkmSQf7+oretwASf5pkm8m+ctu7P+pq1+Y5Bvd2P+gO1FiRUmyKsm3kvzP7vmKHzNAkgNJnk6yJ8lUVxv6vb7iQr/Bn3q4G9h0XG0b8GhVbQAe7Z6vJMeAT1bVzwOXAjd1f+OVPm6Ao8B7q+oXgY3ApiSXAr8NfL4b+w+AG09hH5fKx4F9fc9bGPOsX66qjX3n5w/9Xl9xoU9jP/VQVY8BLx1X3gzc0z2+B7h2WTu1xKrqUFU92T1+mV4QrGGFjxugev6ue/qa7lbAe4E/6uorbuxJ1gLvA36/ex5W+JhPYuj3+koM/UE/9bDmFPXlVLmgqg5BLyCB809xf5ZMkvXAxcA3aGTc3TTHHuAIsAt4HvjbqjrWNVmJ7/n/CvwH4Mfd8zex8sc8q4CvJtnd/UQNjPBeX4lXzlrQTz3ozJfk9cCDwCeq6ke9nb+Vr6peBTYmORt4CPj5Qc2Wt1dLJ8n7gSNVtTvJ5bPlAU1XzJiPc1lVHUxyPrAryXdGWdlK3NP3px7gcJLVAN39kVPcn7FL8hp6gf/FqvpyV17x4+5XVX8L/B96xzXOTjK7E7fS3vOXAR9IcoDedO176e35r+Qx/4OqOtjdH6H3IX8JI7zXV2Lo+1MPvfFu6R5vAR4+hX0Zu24+9y5gX1V9ru+lFT1ugCQT3R4+SX4K+BV6xzS+BvyrrtmKGntV3VJVa6tqPb3/z/+7qv4tK3jMs5L8TJI3zD4GrgT2MsJ7fUV+IzfJNfT2BGZ/6uG2U9ylJZPkPuByej+3ehi4FfgfwAPAm4HvAR+qquMP9p6xkvxz4M+Bp/nJHO+n6M3rr9hxAyT5BXoH7lbR22l7oKo+k+Qt9PaCzwW+Bfy7qjp66nq6NLrpnX9fVe9vYczdGB/qnp4FfKmqbkvyJoZ8r6/I0JckDbYSp3ckSXMw9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JD/j8vMwu5VVA7swAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['VAR_0004_mod50'] = train['VAR_0004'] % 50\n",
    "plt.hist(train['VAR_0004_mod50'],bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FURTHER DEVELOPMENTS:\n",
    "\n",
    "### Outliers\n",
    "\n",
    "If EDA is an art outlier handling is its natural partner. It is a very rich and difficult topic that every data science should be able to deal with. \n",
    "\n",
    "\n",
    "### Dataset Balancing\n",
    "\n",
    "Is  sampling purely random or are there oversampled classes/cluster? Another helpful manipulation could be dataset balancing eg with data augmentation. Understanding how a dataset is generated is crucial to set up a propre validation scheme.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References:\n",
    "\n",
    "\n",
    "https://medium.com/@rrfd/cleaning-and-prepping-data-with-python-for-data-science-best-practices-and-helpful-packages-af1edfbe2a3\n",
    "\n",
    "https://www.coursera.org/lecture/competitive-data-science/exploratory-data-analysis-zvOJc\n",
    "\n",
    "https://stackabuse.com/predicting-customer-ad-clicks-via-machine-learning/"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}