3-Sol.StatisticalLearning-DataEncoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://pythonawesome.com/content/images/2018/05/scikit-learn.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import standard libraries for this lecture\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.preprocessing import MultiLabelBinarizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = OneHotEncoder(handle_unknown='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CatX = [['Ensuite'], ['Outside private toilet'], ['Shared Bathroom inside'], ['Shared bathroom outside']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(categorical_features=None, categories=None, drop=None,\n",
       "              dtype=<class 'numpy.float64'>, handle_unknown='ignore',\n",
       "              n_values=None, sparse=True)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.fit(CatX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['Ensuite', 'Outside private toilet', 'Shared Bathroom inside',\n",
       "        'Shared bathroom outside'], dtype=object)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [['Ensuite'], ['Ensuite'], ['Outside private toilet'], ['Ensuite'], ['Shared Bathroom inside'], ['Shared bathroom outside'], ['Shared bathroom outside'], ['Ensuite']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.],\n",
       "       [0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.transform(X).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Careful!__\n",
    "\n",
    "One has to deal with sparsity of arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = OrdinalEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder(categories='auto', dtype=<class 'numpy.float64'>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.fit(CatX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['Ensuite', 'Outside private toilet', 'Shared Bathroom inside',\n",
       "        'Shared bathroom outside'], dtype=object)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [['Ensuite'], \n",
    "     ['Ensuite'], \n",
    "     ['Outside private toilet'], \n",
    "     ['Ensuite'], \n",
    "     ['Shared Bathroom inside'], \n",
    "     ['Shared bathroom outside'], \n",
    "     ['Shared bathroom outside'], \n",
    "     ['Ensuite']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [3.],\n",
       "       [3.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## My advice: write your own function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider a dataset like this\n",
    "\n",
    "| Name    |       Favourite sports          | Other random skill |\n",
    "|--|---|--|\n",
    "| Alice   | \\[Football, Basketball, Tennis\\]| 1.0             |\n",
    "| Bob     | \\[Tennis, Rugby\\]               | 4.3             |\n",
    "| Charlie | \\[Volleyball, Curling         \\]| 5.6             |\n",
    "| Daniel  | \\[Climbing                    \\]| 0.7             |\n",
    "| Freeda  | \\[Cycling                     \\]| 3.5             |\n",
    "| Gemma   | \\[Football, Running           \\]| 2.1             |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Name':['Alice', 'Bob', 'Charlie', 'Daniel', 'Freeda', 'Gemma'], \n",
    "                   'FavouriteSports': [['Football', 'Basketball', 'Tennis'], \n",
    "                                       ['Tennis', 'Rugby'], ['Volleyball', 'Curling'], \n",
    "                                       ['Climbing'], ['Cycling'], ['Football', 'Running']], \n",
    "                   'OtherSkill': [1.0, 4.3, 5.6, 0.7, 3.5, 2.1],\n",
    "                   'OtherCategSkill': [['a', 'f'], ['c', 'd', 'e'], ['b', 'c', 'a'], ['d', 'c'], ['b', 'c', 'a'], ['b', 'e', 'f'] ]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Questions__:\n",
    "\n",
    "> How would you convert this dataframe into a format that a machine learning algorithm would digest?\n",
    "\n",
    "> Which kind of encoder would you use? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def HomeMadeEncoderElem(df, column):\n",
    "    \"\"\"\n",
    "        :return: an encoded dataframe. \n",
    "        The argument column is replaced by a number of columns corresponding to the hotEncoder classes.\n",
    "    \"\"\"\n",
    "    df_work = df.copy()\n",
    "    mlb = MultiLabelBinarizer()\n",
    "    df_work = df_work.join(pd.DataFrame(mlb.fit_transform(df_work.pop(column)),\n",
    "                          columns=mlb.classes_,\n",
    "                          index=df_work.index))\n",
    "    \n",
    "    return df_work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>OtherCategSkill</th>\n",
       "      <th>OtherSkill</th>\n",
       "      <th>Basketball</th>\n",
       "      <th>Climbing</th>\n",
       "      <th>Curling</th>\n",
       "      <th>Cycling</th>\n",
       "      <th>Football</th>\n",
       "      <th>Rugby</th>\n",
       "      <th>Running</th>\n",
       "      <th>Tennis</th>\n",
       "      <th>Volleyball</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>[a, f]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>[c, d, e]</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>[b, c, a]</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>[d, c]</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Freeda</td>\n",
       "      <td>[b, c, a]</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gemma</td>\n",
       "      <td>[b, e, f]</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name OtherCategSkill  OtherSkill  Basketball  Climbing  Curling  \\\n",
       "0    Alice          [a, f]         1.0           1         0        0   \n",
       "1      Bob       [c, d, e]         4.3           0         0        0   \n",
       "2  Charlie       [b, c, a]         5.6           0         0        1   \n",
       "3   Daniel          [d, c]         0.7           0         1        0   \n",
       "4   Freeda       [b, c, a]         3.5           0         0        0   \n",
       "5    Gemma       [b, e, f]         2.1           0         0        0   \n",
       "\n",
       "   Cycling  Football  Rugby  Running  Tennis  Volleyball  \n",
       "0        0         1      0        0       1           0  \n",
       "1        0         0      1        0       1           0  \n",
       "2        0         0      0        0       0           1  \n",
       "3        0         0      0        0       0           0  \n",
       "4        1         0      0        0       0           0  \n",
       "5        0         1      0        1       0           0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HomeMadeEncoderElem(df, column= 'FavouriteSports')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def HomeMadeEncoder(df, columns):\n",
    "    \"\"\"\n",
    "        :return: an encoded dataframe. \n",
    "        The argument columns is a list and each of its element is replaced by a number of columns \n",
    "        corresponding to the hotEncoder classes.\n",
    "    \"\"\"\n",
    "    \n",
    "    df_work = df.copy()\n",
    "    if isinstance(columns, list):\n",
    "        for col in columns:\n",
    "            df_work = HomeMadeEncoderElem(df_work, col)\n",
    "        return df_work\n",
    "    elif isinstance(columns, str):\n",
    "        return HomeMadeEncoderElem(df_work, columns)\n",
    "    else:\n",
    "        raise ValueError('Dude, columns can only be a string or a list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>OtherSkill</th>\n",
       "      <th>Basketball</th>\n",
       "      <th>Climbing</th>\n",
       "      <th>Curling</th>\n",
       "      <th>Cycling</th>\n",
       "      <th>Football</th>\n",
       "      <th>Rugby</th>\n",
       "      <th>Running</th>\n",
       "      <th>Tennis</th>\n",
       "      <th>Volleyball</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Freeda</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gemma</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  OtherSkill  Basketball  Climbing  Curling  Cycling  Football  \\\n",
       "0    Alice         1.0           1         0        0        0         1   \n",
       "1      Bob         4.3           0         0        0        0         0   \n",
       "2  Charlie         5.6           0         0        1        0         0   \n",
       "3   Daniel         0.7           0         1        0        0         0   \n",
       "4   Freeda         3.5           0         0        0        1         0   \n",
       "5    Gemma         2.1           0         0        0        0         1   \n",
       "\n",
       "   Rugby  Running  Tennis  Volleyball  a  b  c  d  e  f  \n",
       "0      0        0       1           0  1  0  0  0  0  1  \n",
       "1      1        0       1           0  0  0  1  1  1  0  \n",
       "2      0        0       0           1  1  1  1  0  0  0  \n",
       "3      0        0       0           0  0  0  1  1  0  0  \n",
       "4      0        0       0           0  1  1  1  0  0  0  \n",
       "5      0        1       0           0  0  1  0  0  1  1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HomeMadeEncoder(df, columns= ['FavouriteSports', 'OtherCategSkill'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>OtherCategSkill</th>\n",
       "      <th>OtherSkill</th>\n",
       "      <th>Basketball</th>\n",
       "      <th>Climbing</th>\n",
       "      <th>Curling</th>\n",
       "      <th>Cycling</th>\n",
       "      <th>Football</th>\n",
       "      <th>Rugby</th>\n",
       "      <th>Running</th>\n",
       "      <th>Tennis</th>\n",
       "      <th>Volleyball</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>[a, f]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>[c, d, e]</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>[b, c, a]</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>[d, c]</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Freeda</td>\n",
       "      <td>[b, c, a]</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gemma</td>\n",
       "      <td>[b, e, f]</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name OtherCategSkill  OtherSkill  Basketball  Climbing  Curling  \\\n",
       "0    Alice          [a, f]         1.0           1         0        0   \n",
       "1      Bob       [c, d, e]         4.3           0         0        0   \n",
       "2  Charlie       [b, c, a]         5.6           0         0        1   \n",
       "3   Daniel          [d, c]         0.7           0         1        0   \n",
       "4   Freeda       [b, c, a]         3.5           0         0        0   \n",
       "5    Gemma       [b, e, f]         2.1           0         0        0   \n",
       "\n",
       "   Cycling  Football  Rugby  Running  Tennis  Volleyball  \n",
       "0        0         1      0        0       1           0  \n",
       "1        0         0      1        0       1           0  \n",
       "2        0         0      0        0       0           1  \n",
       "3        0         0      0        0       0           0  \n",
       "4        1         0      0        0       0           0  \n",
       "5        0         1      0        1       0           0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HomeMadeEncoder(df, 'FavouriteSports')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the suitable encoder to a set of categorical data of the following dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airbnb = pd.read_pickle('Data/PolReg/airbnb_data.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the columns\n",
    "\n",
    "- check_in_time_start\n",
    "- check_in_time_end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Hint:_ it might be necessary to make some data cleaning on data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Example__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write down the command to make a slice of df_airbnb by the three selected columns\n",
    "cat_cols = ['check_in_time_start', 'check_in_time_end']\n",
    "df_cat_slice = df_airbnb.loc[:, cat_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['15', '9', '11', '17', 'FLEXIBLE', '12', '16', 'NOT_SELECTED',\n",
       "       '10', '18', '13', '14', '8', '19', '20', '0', '2', '3', '6', '21',\n",
       "       '7', '25'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_airbnb.check_in_time_start.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may suppose that a ```'NOT_SELECTED'``` start time is equivalent to ```'FLEXIBLE'```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may proceed as the function above, however, we have to split the column addition from the `pop` phase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having this in mind, let's modify the code above to allow the second argument to be a column list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def HomeMadeEncoderOrd(df, columns):\n",
    "    \"\"\"\n",
    "        :return: an encoded dataframe. \n",
    "        The argument column is replaced by a number of columns corresponding to the OrdinalEncoder classes.\n",
    "    \"\"\"\n",
    "    \n",
    "    df_work = df.copy()\n",
    "    cat = []\n",
    "    enc = OrdinalEncoder()\n",
    "    for i, col in enumerate(columns):\n",
    "        cat.append(df_work[col].unique())\n",
    "        enc.fit(cat[i].reshape(-1,1))\n",
    "        enclist = enc.transform(np.array(df_work[col]).reshape(-1,1))\n",
    "        df_work[col + '_enc'] = enclist\n",
    "    \n",
    "    \n",
    "    return df_work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder(categories='auto', dtype=<class 'numpy.float64'>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ = df_cat_slice['check_in_time_start'].unique()\n",
    "enc.fit(categ.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['15', '9', '11', ..., '14', '17', '10'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df_cat_slice['check_in_time_start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.],\n",
       "       [19.],\n",
       "       [ 2.],\n",
       "       ...,\n",
       "       [ 5.],\n",
       "       [ 8.],\n",
       "       [ 1.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.transform(np.array(df_cat_slice['check_in_time_start']).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>check_in_time_start</th>\n",
       "      <th>check_in_time_end</th>\n",
       "      <th>check_in_time_start_enc</th>\n",
       "      <th>check_in_time_end_enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19970</th>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19971</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19972</th>\n",
       "      <td>14</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19973</th>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19974</th>\n",
       "      <td>14</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19975</th>\n",
       "      <td>15</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19976</th>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19977</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19978</th>\n",
       "      <td>16</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19979</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19980</th>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19981</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19982</th>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19983</th>\n",
       "      <td>15</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19984</th>\n",
       "      <td>15</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19985</th>\n",
       "      <td>15</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19986</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19987</th>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19988</th>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19989</th>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19990</th>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19991</th>\n",
       "      <td>15</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19992</th>\n",
       "      <td>12</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19993</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19994</th>\n",
       "      <td>16</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>13</td>\n",
       "      <td>FLEXIBLE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>NOT_SELECTED</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      check_in_time_start check_in_time_end  check_in_time_start_enc  \\\n",
       "0                      15                21                      6.0   \n",
       "1                       9      NOT_SELECTED                     19.0   \n",
       "2                      11                23                      2.0   \n",
       "3                      17                20                      8.0   \n",
       "4                FLEXIBLE          FLEXIBLE                     20.0   \n",
       "5                      15                22                      6.0   \n",
       "6                      15                21                      6.0   \n",
       "7                FLEXIBLE          FLEXIBLE                     20.0   \n",
       "8                      12                25                      3.0   \n",
       "9                      16          FLEXIBLE                      7.0   \n",
       "10                     15          FLEXIBLE                      6.0   \n",
       "11                     15                21                      6.0   \n",
       "12           NOT_SELECTED      NOT_SELECTED                     21.0   \n",
       "13                     10                18                      1.0   \n",
       "14                     17                21                      8.0   \n",
       "15                     15      NOT_SELECTED                      6.0   \n",
       "16                     16                19                      7.0   \n",
       "17                     15                22                      6.0   \n",
       "18                     15                18                      6.0   \n",
       "19                     16          FLEXIBLE                      7.0   \n",
       "20                     12                15                      3.0   \n",
       "21                     15                20                      6.0   \n",
       "22                     15          FLEXIBLE                      6.0   \n",
       "23                     17                24                      8.0   \n",
       "24                     16                20                      7.0   \n",
       "25                     16      NOT_SELECTED                      7.0   \n",
       "26           NOT_SELECTED      NOT_SELECTED                     21.0   \n",
       "27                     16          FLEXIBLE                      7.0   \n",
       "28               FLEXIBLE          FLEXIBLE                     20.0   \n",
       "29                     16          FLEXIBLE                      7.0   \n",
       "...                   ...               ...                      ...   \n",
       "19970                  14                24                      5.0   \n",
       "19971                  12                20                      3.0   \n",
       "19972                  14          FLEXIBLE                      5.0   \n",
       "19973        NOT_SELECTED      NOT_SELECTED                     21.0   \n",
       "19974                  14          FLEXIBLE                      5.0   \n",
       "19975                  15          FLEXIBLE                      6.0   \n",
       "19976            FLEXIBLE      NOT_SELECTED                     20.0   \n",
       "19977                  12                14                      3.0   \n",
       "19978                  16          FLEXIBLE                      7.0   \n",
       "19979                  16                20                      7.0   \n",
       "19980        NOT_SELECTED      NOT_SELECTED                     21.0   \n",
       "19981                  16                20                      7.0   \n",
       "19982                  15                24                      6.0   \n",
       "19983                  15      NOT_SELECTED                      6.0   \n",
       "19984                  15          FLEXIBLE                      6.0   \n",
       "19985                  15          FLEXIBLE                      6.0   \n",
       "19986                  16                20                      7.0   \n",
       "19987        NOT_SELECTED      NOT_SELECTED                     21.0   \n",
       "19988                  14                20                      5.0   \n",
       "19989                  13                23                      4.0   \n",
       "19990                  14                23                      5.0   \n",
       "19991                  15      NOT_SELECTED                      6.0   \n",
       "19992                  12      NOT_SELECTED                      3.0   \n",
       "19993                  16                20                      7.0   \n",
       "19994                  16      NOT_SELECTED                      7.0   \n",
       "19995                  13          FLEXIBLE                      4.0   \n",
       "19996        NOT_SELECTED      NOT_SELECTED                     21.0   \n",
       "19997                  14                17                      5.0   \n",
       "19998                  17                19                      8.0   \n",
       "19999                  10                12                      1.0   \n",
       "\n",
       "       check_in_time_end_enc  \n",
       "0                       11.0  \n",
       "1                       18.0  \n",
       "2                       13.0  \n",
       "3                       10.0  \n",
       "4                       17.0  \n",
       "5                       12.0  \n",
       "6                       11.0  \n",
       "7                       17.0  \n",
       "8                       15.0  \n",
       "9                       17.0  \n",
       "10                      17.0  \n",
       "11                      11.0  \n",
       "12                      18.0  \n",
       "13                       8.0  \n",
       "14                      11.0  \n",
       "15                      18.0  \n",
       "16                       9.0  \n",
       "17                      12.0  \n",
       "18                       8.0  \n",
       "19                      17.0  \n",
       "20                       5.0  \n",
       "21                      10.0  \n",
       "22                      17.0  \n",
       "23                      14.0  \n",
       "24                      10.0  \n",
       "25                      18.0  \n",
       "26                      18.0  \n",
       "27                      17.0  \n",
       "28                      17.0  \n",
       "29                      17.0  \n",
       "...                      ...  \n",
       "19970                   14.0  \n",
       "19971                   10.0  \n",
       "19972                   17.0  \n",
       "19973                   18.0  \n",
       "19974                   17.0  \n",
       "19975                   17.0  \n",
       "19976                   18.0  \n",
       "19977                    4.0  \n",
       "19978                   17.0  \n",
       "19979                   10.0  \n",
       "19980                   18.0  \n",
       "19981                   10.0  \n",
       "19982                   14.0  \n",
       "19983                   18.0  \n",
       "19984                   17.0  \n",
       "19985                   17.0  \n",
       "19986                   10.0  \n",
       "19987                   18.0  \n",
       "19988                   10.0  \n",
       "19989                   13.0  \n",
       "19990                   13.0  \n",
       "19991                   18.0  \n",
       "19992                   18.0  \n",
       "19993                   10.0  \n",
       "19994                   18.0  \n",
       "19995                   17.0  \n",
       "19996                   18.0  \n",
       "19997                    7.0  \n",
       "19998                    9.0  \n",
       "19999                    2.0  \n",
       "\n",
       "[20000 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HomeMadeEncoderOrd(df_cat_slice, cat_cols)"
   ]
  }
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