Managing missing data 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nettoyez dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# liste = [5,10,20,5]\n",
    "# import numpy as np\n",
    "# np.mean(liste)\n",
    "\n",
    "# liste = [5,10, np.nan ,20,5]\n",
    "# np.nanmean(liste) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identifier les doublons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://bit.ly/tx-data\n"
     ]
    }
   ],
   "source": [
    "fp = \"https://bit.ly/tx-data\"\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50563-403</td>\n",
       "      <td>Jacobo</td>\n",
       "      <td>Haresign</td>\n",
       "      <td>18.144.134.48</td>\n",
       "      <td>$647.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54569-3911</td>\n",
       "      <td>Christy</td>\n",
       "      <td>Derl</td>\n",
       "      <td>254.107.123.242</td>\n",
       "      <td>$460.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59667-0024</td>\n",
       "      <td>Florie</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>148.80.11.233</td>\n",
       "      <td>$565.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60429-123</td>\n",
       "      <td>Danika</td>\n",
       "      <td>Shimuk</td>\n",
       "      <td>233.91.221.158</td>\n",
       "      <td>$794.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0187-0771</td>\n",
       "      <td>Annelise</td>\n",
       "      <td>Antonescu</td>\n",
       "      <td>238.143.41.105</td>\n",
       "      <td>$790.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  transaction_id first_name  last_name       ip_address   amount\n",
       "0      50563-403     Jacobo   Haresign    18.144.134.48  $647.77\n",
       "1     54569-3911    Christy       Derl  254.107.123.242  $460.47\n",
       "2     59667-0024     Florie     Prewer    148.80.11.233  $565.38\n",
       "3      60429-123     Danika     Shimuk   233.91.221.158  $794.76\n",
       "4      0187-0771   Annelise  Antonescu   238.143.41.105  $790.41"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id    object\n",
       "first_name        object\n",
       "last_name         object\n",
       "ip_address        object\n",
       "amount            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>68180-751</td>\n",
       "      <td>Marlon</td>\n",
       "      <td>Fernie</td>\n",
       "      <td>61.133.100.141</td>\n",
       "      <td>$974.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>53942-299</td>\n",
       "      <td>Upton</td>\n",
       "      <td>Emig</td>\n",
       "      <td>103.190.123.125</td>\n",
       "      <td>$759.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60289-247</td>\n",
       "      <td>Corri</td>\n",
       "      <td>Rockcliffe</td>\n",
       "      <td>46.150.208.18</td>\n",
       "      <td>$5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>59667-0024</td>\n",
       "      <td>Florie</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>148.80.11.233</td>\n",
       "      <td>$565.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0066-0508</td>\n",
       "      <td>Jedd</td>\n",
       "      <td>Hartman</td>\n",
       "      <td>21.55.247.91</td>\n",
       "      <td>$749.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id first_name   last_name       ip_address   amount\n",
       "15      68180-751     Marlon      Fernie   61.133.100.141  $974.30\n",
       "19      53942-299      Upton        Emig  103.190.123.125  $759.53\n",
       "20      60289-247      Corri  Rockcliffe    46.150.208.18    $5.68\n",
       "28     59667-0024     Florie      Prewer    148.80.11.233  $565.38\n",
       "32      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "37      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "42      0066-0508       Jedd     Hartman     21.55.247.91  $749.96"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59667-0024</td>\n",
       "      <td>Florie</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>148.80.11.233</td>\n",
       "      <td>$565.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>53942-299</td>\n",
       "      <td>Upton</td>\n",
       "      <td>Emig</td>\n",
       "      <td>103.190.123.125</td>\n",
       "      <td>$759.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60289-247</td>\n",
       "      <td>Corri</td>\n",
       "      <td>Rockcliffe</td>\n",
       "      <td>46.150.208.18</td>\n",
       "      <td>$5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>68180-751</td>\n",
       "      <td>Marlon</td>\n",
       "      <td>Fernie</td>\n",
       "      <td>61.133.100.141</td>\n",
       "      <td>$974.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>68180-751</td>\n",
       "      <td>Marlon</td>\n",
       "      <td>Fernie</td>\n",
       "      <td>61.133.100.141</td>\n",
       "      <td>$974.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0066-0508</td>\n",
       "      <td>Jedd</td>\n",
       "      <td>Hartman</td>\n",
       "      <td>21.55.247.91</td>\n",
       "      <td>$749.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>53942-299</td>\n",
       "      <td>Upton</td>\n",
       "      <td>Emig</td>\n",
       "      <td>103.190.123.125</td>\n",
       "      <td>$759.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60289-247</td>\n",
       "      <td>Corri</td>\n",
       "      <td>Rockcliffe</td>\n",
       "      <td>46.150.208.18</td>\n",
       "      <td>$5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>59667-0024</td>\n",
       "      <td>Florie</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>148.80.11.233</td>\n",
       "      <td>$565.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0066-0508</td>\n",
       "      <td>Jedd</td>\n",
       "      <td>Hartman</td>\n",
       "      <td>21.55.247.91</td>\n",
       "      <td>$749.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id first_name   last_name       ip_address   amount\n",
       "2      59667-0024     Florie      Prewer    148.80.11.233  $565.38\n",
       "5       53942-299      Upton        Emig  103.190.123.125  $759.53\n",
       "7       60289-247      Corri  Rockcliffe    46.150.208.18    $5.68\n",
       "12      68180-751     Marlon      Fernie   61.133.100.141  $974.30\n",
       "14      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "15      68180-751     Marlon      Fernie   61.133.100.141  $974.30\n",
       "16      0066-0508       Jedd     Hartman     21.55.247.91  $749.96\n",
       "19      53942-299      Upton        Emig  103.190.123.125  $759.53\n",
       "20      60289-247      Corri  Rockcliffe    46.150.208.18    $5.68\n",
       "28     59667-0024     Florie      Prewer    148.80.11.233  $565.38\n",
       "32      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "37      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "42      0066-0508       Jedd     Hartman     21.55.247.91  $749.96"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated(keep=False)]   # keep=False pour afficher des doublons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>transaction_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>68180-751</td>\n",
       "      <td>Marlon</td>\n",
       "      <td>Fernie</td>\n",
       "      <td>61.133.100.141</td>\n",
       "      <td>$974.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>53942-299</td>\n",
       "      <td>Upton</td>\n",
       "      <td>Emig</td>\n",
       "      <td>103.190.123.125</td>\n",
       "      <td>$759.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60289-247</td>\n",
       "      <td>Corri</td>\n",
       "      <td>Rockcliffe</td>\n",
       "      <td>46.150.208.18</td>\n",
       "      <td>$5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>59667-0024</td>\n",
       "      <td>Florie</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>148.80.11.233</td>\n",
       "      <td>$565.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0066-0508</td>\n",
       "      <td>Jedd</td>\n",
       "      <td>Hartman</td>\n",
       "      <td>21.55.247.91</td>\n",
       "      <td>$749.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id first_name   last_name       ip_address   amount\n",
       "15      68180-751     Marlon      Fernie   61.133.100.141  $974.30\n",
       "19      53942-299      Upton        Emig  103.190.123.125  $759.53\n",
       "20      60289-247      Corri  Rockcliffe    46.150.208.18    $5.68\n",
       "28     59667-0024     Florie      Prewer    148.80.11.233  $565.38\n",
       "32      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "37      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "42      0066-0508       Jedd     Hartman     21.55.247.91  $749.96"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated(subset=['transaction_id'])]  # par colonne "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>68180-751</td>\n",
       "      <td>Marlon</td>\n",
       "      <td>Fernie</td>\n",
       "      <td>61.133.100.141</td>\n",
       "      <td>$974.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>53942-299</td>\n",
       "      <td>Upton</td>\n",
       "      <td>Emig</td>\n",
       "      <td>103.190.123.125</td>\n",
       "      <td>$759.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60289-247</td>\n",
       "      <td>Corri</td>\n",
       "      <td>Rockcliffe</td>\n",
       "      <td>46.150.208.18</td>\n",
       "      <td>$5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>59667-0024</td>\n",
       "      <td>Florie</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>148.80.11.233</td>\n",
       "      <td>$565.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>43857-0134</td>\n",
       "      <td>Sergio</td>\n",
       "      <td>Whittlesee</td>\n",
       "      <td>76.116.223.149</td>\n",
       "      <td>$346.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>65862-207</td>\n",
       "      <td>Alvira</td>\n",
       "      <td>Heifer</td>\n",
       "      <td>141.170.128.72</td>\n",
       "      <td>$584.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0066-0508</td>\n",
       "      <td>Jedd</td>\n",
       "      <td>Hartman</td>\n",
       "      <td>21.55.247.91</td>\n",
       "      <td>$749.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id first_name   last_name       ip_address   amount\n",
       "15      68180-751     Marlon      Fernie   61.133.100.141  $974.30\n",
       "19      53942-299      Upton        Emig  103.190.123.125  $759.53\n",
       "20      60289-247      Corri  Rockcliffe    46.150.208.18    $5.68\n",
       "28     59667-0024     Florie      Prewer    148.80.11.233  $565.38\n",
       "29     43857-0134     Sergio  Whittlesee   76.116.223.149  $346.11\n",
       "32      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "37      65862-207     Alvira      Heifer   141.170.128.72  $584.13\n",
       "42      0066-0508       Jedd     Hartman     21.55.247.91  $749.96"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated(subset=['first_name'])]  # par colonne "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id    0\n",
       "first_name        0\n",
       "last_name         0\n",
       "ip_address        0\n",
       "amount            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Supprimer les doublons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()  ## compter des doublons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset = ['transaction_id'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.drop_duplicates().duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  # pour vérifier si drop a fonctionné"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sauvegarder des données propres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"transaction.csv\" , sep = \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gérer des valeurs manquantes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://bit.ly/missing-values\n"
     ]
    }
   ],
   "source": [
    "fp_test = 'https://bit.ly/missing-values'\n",
    "print(fp_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(fp_test)\n",
    "df.head()\n",
    "df2 = df\n",
    "df3 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Orange</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    color  rating\n",
       "0     NaN    1.16\n",
       "1     Red     NaN\n",
       "2    Blue    2.54\n",
       "3     Red    1.51\n",
       "4     Red     NaN\n",
       "5    Blue     NaN\n",
       "6     NaN    4.25\n",
       "7    Blue     NaN\n",
       "8     NaN    1.75\n",
       "9  Orange    3.21"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(917, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color      object\n",
       "rating    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Idenfier les NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>color</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>917 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     color  rating\n",
       "0     True   False\n",
       "1    False    True\n",
       "2    False   False\n",
       "3    False   False\n",
       "4    False    True\n",
       "..     ...     ...\n",
       "912  False   False\n",
       "913  False    True\n",
       "914   True   False\n",
       "915  False   False\n",
       "916  False    True\n",
       "\n",
       "[917 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color     129\n",
       "rating    256\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color     0.140676\n",
       "rating    0.279171\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().mean()   # pourcentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "Blue      323\n",
       "Red       234\n",
       "Orange    154\n",
       "Yellow     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'].value_counts() # les effectifs pour chaque modalité"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "Blue      323\n",
       "Red       234\n",
       "Orange    154\n",
       "NaN       129\n",
       "Yellow     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'].value_counts(dropna=False)  # effectifs par catégorie avec NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['rating'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* dropna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df.dropna()  # dropna supprime toutes les lignes contenant au moins une NAN\n",
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(917, 2)\n"
     ]
    },
    {
     "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>color</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  color  rating\n",
       "0   NaN    1.16\n",
       "1   Red     NaN\n",
       "2  Blue    2.54\n",
       "3   Red    1.51\n",
       "4   Red     NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df.dropna(how = 'all',subset = ['color','rating'])   \n",
    "print(df3.shape)\n",
    "df3.head()\n",
    "# all : si toutes les valeurs sont manquante dans subset, jetter la ligne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(788, 2)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>Blue</td>\n",
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       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>1.51</td>\n",
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       "      <th>4</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  color  rating\n",
       "1   Red     NaN\n",
       "2  Blue    2.54\n",
       "3   Red    1.51\n",
       "4   Red     NaN\n",
       "5  Blue     NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.dropna(how = 'any', subset = ['color'])   \n",
    "print(df2.shape)\n",
    "df2.head()\n",
    "# any : si une seulle valeur est manquante dans subset, jetter la ligne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remplire les NaN"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1 Remplir avec la valeur la plus courante\n",
    "\n",
    "2 Remplir avec la moyenne\n",
    "\n",
    "3 Remplir avec une valeur hors de l'echelle de la valeur des non manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://bit.ly/missing-values\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16</td>\n",
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       "      <th>1</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>1.51</td>\n",
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       "      <th>4</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  color  rating\n",
       "0   NaN    1.16\n",
       "1   Red     NaN\n",
       "2  Blue    2.54\n",
       "3   Red    1.51\n",
       "4   Red     NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_test = 'https://bit.ly/missing-values'\n",
    "print(fp_test)\n",
    "\n",
    "df = pd.read_csv(fp_test)\n",
    "df2 = pd.read_csv(fp_test)\n",
    "df3 = pd.read_csv(fp_test)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 1 Remplir avec la valeur la plus courante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['color'].value_counts(dropna=False)   # Blue      323"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Blue\n",
       "1         Red\n",
       "2        Blue\n",
       "3         Red\n",
       "4         Red\n",
       "        ...  \n",
       "912      Blue\n",
       "913       Red\n",
       "914      Blue\n",
       "915    Yellow\n",
       "916      Blue\n",
       "Name: color, Length: 917, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'].fillna('Blue')   # on rample Nan par Blue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color     129\n",
       "rating    256\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>color</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  color  rating\n",
       "0   NaN    1.16\n",
       "1   Red     NaN\n",
       "2  Blue    2.54\n",
       "3   Red    1.51\n",
       "4   Red     NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['color'].fillna('Blue', inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color       0\n",
       "rating    256\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>rating</th>\n",
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       "      <th>2</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2.54</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>1.51</td>\n",
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       "      <th>4</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  color  rating\n",
       "0  Blue    1.16\n",
       "1   Red     NaN\n",
       "2  Blue    2.54\n",
       "3   Red    1.51\n",
       "4   Red     NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2 Remplir avec la moyenne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0666717095310134"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = df['rating'].mean()  # median\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rating'].fillna(m, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>rating</th>\n",
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       "      <td>Blue</td>\n",
       "      <td>2.540000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>1.510000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3.066672</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "  color    rating\n",
       "0  Blue  1.160000\n",
       "1   Red  3.066672\n",
       "2  Blue  2.540000\n",
       "3   Red  1.510000\n",
       "4   Red  3.066672"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[\"rating2\"] = df[\"rating\"]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>rating_double</th>\n",
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       "      <td>6.133343</td>\n",
       "      <td>3.066672</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>2.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>3.020000</td>\n",
       "      <td>1.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Red</td>\n",
       "      <td>6.133343</td>\n",
       "      <td>3.066672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>Blue</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>3.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>Red</td>\n",
       "      <td>6.133343</td>\n",
       "      <td>3.066672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>1.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Yellow</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>2.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>Blue</td>\n",
       "      <td>6.133343</td>\n",
       "      <td>3.066672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>917 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      color  rating_double    rating\n",
       "0      Blue       2.320000  1.160000\n",
       "1       Red       6.133343  3.066672\n",
       "2      Blue       5.080000  2.540000\n",
       "3       Red       3.020000  1.510000\n",
       "4       Red       6.133343  3.066672\n",
       "..      ...            ...       ...\n",
       "912    Blue       6.800000  3.400000\n",
       "913     Red       6.133343  3.066672\n",
       "914    Blue       2.480000  1.240000\n",
       "915  Yellow       5.280000  2.640000\n",
       "916    Blue       6.133343  3.066672\n",
       "\n",
       "[917 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(loc=1, column='rating_double', value=df[\"rating\"]*2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>Red</td>\n",
       "      <td>6.133343</td>\n",
       "      <td>3.066672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>2.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red</td>\n",
       "      <td>3.020000</td>\n",
       "      <td>1.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Red</td>\n",
       "      <td>6.133343</td>\n",
       "      <td>3.066672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  color  rating_double    rating\n",
       "0  Blue       2.320000  1.160000\n",
       "1   Red       6.133343  3.066672\n",
       "2  Blue       5.080000  2.540000\n",
       "3   Red       3.020000  1.510000\n",
       "4   Red       6.133343  3.066672"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color            0\n",
       "rating_double    0\n",
       "rating           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  color  rating_double    rating\n",
       "0  Blue       2.320000  1.160000\n",
       "1   Red       6.133343  3.066672\n",
       "2  Blue       5.080000  2.540000\n",
       "3   Red       3.020000  1.510000\n",
       "4   Red       6.133343  3.066672"
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     "execution_count": 50,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3 Remplir avec une valeur hors de l'echelle de la valeur des non manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['color'].fillna('Missing', inplace = True)   # Autre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color       0\n",
       "rating    256\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['rating'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['rating'].max()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## On peut choisir 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['rating'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     color  rating\n",
       "0  Missing    1.16\n",
       "1      Red    0.00\n",
       "2     Blue    2.54\n",
       "3      Red    1.51\n",
       "4      Red    0.00"
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     "execution_count": 57,
     "metadata": {},
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   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color     0\n",
       "rating    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
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   "source": [
    "df3.isna().sum()"
   ]
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   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://bit.ly/missing-values\n"
     ]
    },
    {
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       "      <td>Blue</td>\n",
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       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>914</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.24</td>\n",
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       "      <th>915</th>\n",
       "      <td>Yellow</td>\n",
       "      <td>2.64</td>\n",
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       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>917 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      color  rating\n",
       "0       NaN    1.16\n",
       "1       Red     NaN\n",
       "2      Blue    2.54\n",
       "3       Red    1.51\n",
       "4       Red     NaN\n",
       "..      ...     ...\n",
       "912    Blue    3.40\n",
       "913     Red     NaN\n",
       "914     NaN    1.24\n",
       "915  Yellow    2.64\n",
       "916    Blue     NaN\n",
       "\n",
       "[917 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = 'https://bit.ly/missing-values'\n",
    "print(fp)\n",
    "\n",
    "df5 = pd.read_csv(fp)\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>913</th>\n",
       "      <td>Red</td>\n",
       "      <td>2.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Yellow</td>\n",
       "      <td>2.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2.640000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>917 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      color    rating\n",
       "0       NaN  1.160000\n",
       "1       Red  1.850000\n",
       "2      Blue  2.540000\n",
       "3       Red  1.510000\n",
       "4       Red  2.423333\n",
       "..      ...       ...\n",
       "912    Blue  3.400000\n",
       "913     Red  2.320000\n",
       "914     NaN  1.240000\n",
       "915  Yellow  2.640000\n",
       "916    Blue  2.640000\n",
       "\n",
       "[917 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['rating'] = df5['rating'].interpolate(method=\"linear\")\n",
    "df5"
   ]
  }
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