Feature Engineering avec Python_original.ipynb

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   "source": [
    "<img align=center src=\"https://afouda-datascience.com/wp-content/uploads/2020/12/afoudalogo.png\"></img>\n",
    "<h1 align=center>FEATURE ENGINEERING AVEC PYTHON</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35446b7e",
   "metadata": {},
   "source": [
    " Par [Josué AFOUDA](https://afouda-datascience.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7ef342",
   "metadata": {},
   "source": [
    "Si vous êtes déjà familier avec l'Apprentissage Automatique (*Machine Learning*), alors vous savez que l'Ingénierie des Fonctionnalités (***Feature Engineering*** en Anglais) est un ingrédient très important pour des modèles de prédictions hautement performants. \n",
    "\n",
    "***Feature Engineering*** (Ingénierie des fonctionnalités) est le processus de transformation des variables brutes pour créer de nouvelles variables. C'est l'une des plus importantes étapes qui influence un modèle de *Machine Learning*. Il y a deux principaux types de *Feature Ingineering* :\n",
    "\n",
    "- Transformation des variables brutes et/ou création de nouvelles variables sous la base des connaissances sur la problématique *business* ;\n",
    "\n",
    "- Transformation des variables brutes et/ou création de nouvelles variables dans une perspective statistique (par exemple normalisation ou standardisation des données).\n",
    "\n",
    "L'objectif de l'ingénierie des fonctionnalités est de produire plus d'information, c'est-à-dire créer de nouvelles variables, pour l'entraînement des algorithmes de Machine Learning. Le fait d'ajouter plus d'information permet très souvent d'améliorer les résultats des modèles. En effet si les ensembles de données sont icomplets ou qu'ils manquent d'importantes variables alors les modèles ne permettront pas d'avoir une bonne compréhension de la problématique en question et peuvent même conduire à de fausses prédictions. C'est pourquoi en tant que Data Scientist, vous devez toujours penser à l'ajout de nouvelles informations pour enrichir votre ensemble de données et par conséquent construire des modèles beaucoup plus précis.\n",
    "\n",
    "La création du meilleur ensemble de fonctionnalités dépend en grande partie de la compréhension du domaine et des intuitions dérivées des données pendant la phase d'analyse exploratoire des données. Il y a beaucoup de créativité dans la création et la transformation de fonctionnalités, et par conséquent, l'ingénierie des fonctionnalités peut être considérée à la fois comme un art et une science.\n",
    "\n",
    "A travers ce tutoriel, vous apprendrez à créer de nouvelles variables en utilisant 04 techniques majeures."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f022cc",
   "metadata": {},
   "source": [
    "# <font color=red> Fusion de plusieurs sources de données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8008b24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c51de8cd",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Count taxable income or loss</th>\n",
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       "      <td>9115</td>\n",
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       "   Postcode  Count taxable income or loss  Average taxable income or loss  \\\n",
       "0      2000                         36185                           47723   \n",
       "1      2006                            83                           80905   \n",
       "2      2007                          4769                           46549   \n",
       "3      2008                          5607                          108816   \n",
       "4      2009                          9726                           82938   \n",
       "\n",
       "   Median taxable income or loss  Count salary and wages  \\\n",
       "0                          18213                   31293   \n",
       "1                          58150                      74   \n",
       "2                          31474                    4153   \n",
       "3                          41151                    5008   \n",
       "4                          50604                    8167   \n",
       "\n",
       "   Average salary and wages  Median salary and wages  Count net rent  \\\n",
       "0                     38710                    17992            3614   \n",
       "1                     82733                    67658              21   \n",
       "2                     47386                    34366             548   \n",
       "3                     53418                    42892             612   \n",
       "4                     74068                    55243            1714   \n",
       "\n",
       "   Average net rent  Median net rent  ...  Median total business income  \\\n",
       "0               558              207  ...                         19684   \n",
       "1              1042             -807  ...                         42054   \n",
       "2             -1242             -794  ...                         19960   \n",
       "3              1818            -1728  ...                         19722   \n",
       "4             -2412            -1520  ...                         26044   \n",
       "\n",
       "   Count  total business expenses  Average total business expenses  \\\n",
       "0                            1331                           222191   \n",
       "1                               4                            48971   \n",
       "2                             334                           639499   \n",
       "3                             481                            32173   \n",
       "4                             666                           222993   \n",
       "\n",
       "   Median total business expenses  Count net tax  Average net tax  \\\n",
       "0                           20773          17264            27555   \n",
       "1                           18508             67            28142   \n",
       "2                            9233           3071            15649   \n",
       "3                            9385           4061            53976   \n",
       "4                           11982           7358            32430   \n",
       "\n",
       "   Median net tax  Count super total accounts balance  \\\n",
       "0            5917                               51394   \n",
       "1           19359                                 137   \n",
       "2            9115                                6711   \n",
       "3           10583                                7676   \n",
       "4           14835                               12718   \n",
       "\n",
       "   Average super total accounts balance  Median super total accounts balance  \n",
       "0                                 68531                                 1110  \n",
       "1                                 90315                                15544  \n",
       "2                                 41670                                 5225  \n",
       "3                                 54245                                10373  \n",
       "4                                126486                                20494  \n",
       "\n",
       "[5 rows x 28 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Données de l'office australien des impôts\n",
    "df_impots = pd.read_csv('taxstats2015.csv')\n",
    "df_impots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f15fba23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2473 entries, 0 to 2472\n",
      "Data columns (total 28 columns):\n",
      " #   Column                                Non-Null Count  Dtype\n",
      "---  ------                                --------------  -----\n",
      " 0   Postcode                              2473 non-null   int64\n",
      " 1   Count taxable income or loss          2473 non-null   int64\n",
      " 2   Average taxable income or loss        2473 non-null   int64\n",
      " 3   Median taxable income or loss         2473 non-null   int64\n",
      " 4   Count salary and wages                2473 non-null   int64\n",
      " 5   Average salary and wages              2473 non-null   int64\n",
      " 6   Median salary and wages               2473 non-null   int64\n",
      " 7   Count net rent                        2473 non-null   int64\n",
      " 8   Average net rent                      2473 non-null   int64\n",
      " 9   Median net rent                       2473 non-null   int64\n",
      " 10  Count total income or loss            2473 non-null   int64\n",
      " 11  Average total income or loss          2473 non-null   int64\n",
      " 12  Median total income or loss           2473 non-null   int64\n",
      " 13  Count total deductions                2473 non-null   int64\n",
      " 14  Average total deductions              2473 non-null   int64\n",
      " 15  Median total deductions               2473 non-null   int64\n",
      " 16  Count total business income           2473 non-null   int64\n",
      " 17  Average total business income         2473 non-null   int64\n",
      " 18  Median total business income          2473 non-null   int64\n",
      " 19  Count  total business expenses        2473 non-null   int64\n",
      " 20  Average total business expenses       2473 non-null   int64\n",
      " 21  Median total business expenses        2473 non-null   int64\n",
      " 22  Count net tax                         2473 non-null   int64\n",
      " 23  Average net tax                       2473 non-null   int64\n",
      " 24  Median net tax                        2473 non-null   int64\n",
      " 25  Count super total accounts balance    2473 non-null   int64\n",
      " 26  Average super total accounts balance  2473 non-null   int64\n",
      " 27  Median super total accounts balance   2473 non-null   int64\n",
      "dtypes: int64(28)\n",
      "memory usage: 541.1 KB\n"
     ]
    }
   ],
   "source": [
    "df_impots.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59f2a8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2473"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nombre de valeurs unique de la variable 'Postcode'\n",
    "df_impots.Postcode.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dfb037f",
   "metadata": {},
   "source": [
    "Etant donné que la variable *Postcode* comporte 2473 valeurs uniques et que la dataframe *df* a aussi 2473 lignes, alors la varibale *Postcode* identifie de manière unique chaque observation de *df*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0255899",
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       "      <td>673244</td>\n",
       "      <td>5465</td>\n",
       "      <td>4356</td>\n",
       "      <td>2632078</td>\n",
       "      <td>552</td>\n",
       "      <td>175697</td>\n",
       "      <td>1885</td>\n",
       "      <td>754097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State/ Territory1 Postcode  Number of individuals\\nno.  \\\n",
       "0               ACT     2600                        5581   \n",
       "1               ACT     2601                        2658   \n",
       "2               ACT     2602                       19457   \n",
       "3               ACT     2603                        6478   \n",
       "4               ACT     2604                        7387   \n",
       "\n",
       "   Taxable income or loss3 \\nno.  Taxable income or loss3 \\n$  \\\n",
       "0                           5557                    569612119   \n",
       "1                           2637                    183288078   \n",
       "2                          19367                   1379212924   \n",
       "3                           6439                    796053195   \n",
       "4                           7341                    637350002   \n",
       "\n",
       "   Tax on taxable income \\nno.  Tax on taxable income \\n$  \\\n",
       "0                         4905                  163820305   \n",
       "1                         1941                   46542933   \n",
       "2                        16595                  326786223   \n",
       "3                         5701                  249451322   \n",
       "4                         6534                  165676071   \n",
       "\n",
       "   Medicare levy \\nno.  Medicare levy \\n$  Medicare levy surcharge \\nno.  ...  \\\n",
       "0                 4384           10548612                            119  ...   \n",
       "1                 1660            3203840                             88  ...   \n",
       "2                14594           24937105                            565  ...   \n",
       "3                 5176           15002971                            131  ...   \n",
       "4                 5787           11512325                            248  ...   \n",
       "\n",
       "   Small business income tax offset \\n$  Low income tax offset \\nno.  \\\n",
       "0                                125198                         1812   \n",
       "1                                 30823                          812   \n",
       "2                                212891                         7623   \n",
       "3                                149987                         2056   \n",
       "4                                 73942                         2391   \n",
       "\n",
       "   Low income tax offset \\n$  People with private health insurance \\nno.  \\\n",
       "0                     527925                                        4597   \n",
       "1                     250431                                        1637   \n",
       "2                    2306744                                       13434   \n",
       "3                     611022                                        5193   \n",
       "4                     673244                                        5465   \n",
       "\n",
       "   Private health insurance - your Australian Government rebate received \\nno.  \\\n",
       "0                                               3557                             \n",
       "1                                               1141                             \n",
       "2                                              10610                             \n",
       "3                                               3913                             \n",
       "4                                               4356                             \n",
       "\n",
       "   Private health insurance - your Australian Government rebate received \\n$  \\\n",
       "0                                            2662024                           \n",
       "1                                             630696                           \n",
       "2                                            6369968                           \n",
       "3                                            2761608                           \n",
       "4                                            2632078                           \n",
       "\n",
       "   Private health insurance rebate \\nno.  Private health insurance rebate \\n$  \\\n",
       "0                                    440                               185991   \n",
       "1                                    213                                63883   \n",
       "2                                   1461                               474942   \n",
       "3                                    442                               154634   \n",
       "4                                    552                               175697   \n",
       "\n",
       "   Excess private health reduction or refund (rebate reduced) \\nno.  \\\n",
       "0                                               1663                  \n",
       "1                                                481                  \n",
       "2                                               3812                  \n",
       "3                                               1849                  \n",
       "4                                               1885                  \n",
       "\n",
       "   Excess private health reduction or refund (rebate reduced) \\n$  \n",
       "0                                             854227               \n",
       "1                                             173475               \n",
       "2                                            1381661               \n",
       "3                                            1004593               \n",
       "4                                             754097               \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Codes postaux\n",
    "postcode_df = pd.read_excel(\n",
    "    'taxstats2016_individual06B_postcode.xlsx', \n",
    "    sheet_name = 'Individuals Table 6B', \n",
    "    header = 2\n",
    ")\n",
    "\n",
    "postcode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf9304e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2567, 150)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postcode_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b54bad47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2567"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nombre de valeurs unique de la variable 'Postcode'\n",
    "postcode_df['Postcode'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164cc704",
   "metadata": {},
   "source": [
    "La variable *Postcode* de la datframe *postcode_df* identifie de manière unique chaque ligne. Par ailleurs, si nous la fusionnons à la dataframe *df_impots*, nous obtiendrons plus d'informations concernant chaque code postal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d9ad15b",
   "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>Postcode</th>\n",
       "      <th>Count taxable income or loss</th>\n",
       "      <th>Average taxable income or loss</th>\n",
       "      <th>Median taxable income or loss</th>\n",
       "      <th>Count salary and wages</th>\n",
       "      <th>Average salary and wages</th>\n",
       "      <th>Median salary and wages</th>\n",
       "      <th>Count net rent</th>\n",
       "      <th>Average net rent</th>\n",
       "      <th>Median net rent</th>\n",
       "      <th>...</th>\n",
       "      <th>Small business income tax offset \\n$</th>\n",
       "      <th>Low income tax offset \\nno.</th>\n",
       "      <th>Low income tax offset \\n$</th>\n",
       "      <th>People with private health insurance \\nno.</th>\n",
       "      <th>Private health insurance - your Australian Government rebate received \\nno.</th>\n",
       "      <th>Private health insurance - your Australian Government rebate received \\n$</th>\n",
       "      <th>Private health insurance rebate \\nno.</th>\n",
       "      <th>Private health insurance rebate \\n$</th>\n",
       "      <th>Excess private health reduction or refund (rebate reduced) \\nno.</th>\n",
       "      <th>Excess private health reduction or refund (rebate reduced) \\n$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>36185</td>\n",
       "      <td>47723</td>\n",
       "      <td>18213</td>\n",
       "      <td>31293</td>\n",
       "      <td>38710</td>\n",
       "      <td>17992</td>\n",
       "      <td>3614</td>\n",
       "      <td>558</td>\n",
       "      <td>207</td>\n",
       "      <td>...</td>\n",
       "      <td>273962.0</td>\n",
       "      <td>10143.0</td>\n",
       "      <td>3466009.0</td>\n",
       "      <td>7710.0</td>\n",
       "      <td>5875.0</td>\n",
       "      <td>3815543.0</td>\n",
       "      <td>984.0</td>\n",
       "      <td>234746.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>1248220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>83</td>\n",
       "      <td>80905</td>\n",
       "      <td>58150</td>\n",
       "      <td>74</td>\n",
       "      <td>82733</td>\n",
       "      <td>67658</td>\n",
       "      <td>21</td>\n",
       "      <td>1042</td>\n",
       "      <td>-807</td>\n",
       "      <td>...</td>\n",
       "      <td>396.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7825.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>25403.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>4769</td>\n",
       "      <td>46549</td>\n",
       "      <td>31474</td>\n",
       "      <td>4153</td>\n",
       "      <td>47386</td>\n",
       "      <td>34366</td>\n",
       "      <td>548</td>\n",
       "      <td>-1242</td>\n",
       "      <td>-794</td>\n",
       "      <td>...</td>\n",
       "      <td>31587.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>716233.0</td>\n",
       "      <td>1763.0</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>744748.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>45656.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>167480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>5607</td>\n",
       "      <td>108816</td>\n",
       "      <td>41151</td>\n",
       "      <td>5008</td>\n",
       "      <td>53418</td>\n",
       "      <td>42892</td>\n",
       "      <td>612</td>\n",
       "      <td>1818</td>\n",
       "      <td>-1728</td>\n",
       "      <td>...</td>\n",
       "      <td>71139.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>854648.0</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1044569.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>85465.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>226170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>9726</td>\n",
       "      <td>82938</td>\n",
       "      <td>50604</td>\n",
       "      <td>8167</td>\n",
       "      <td>74068</td>\n",
       "      <td>55243</td>\n",
       "      <td>1714</td>\n",
       "      <td>-2412</td>\n",
       "      <td>-1520</td>\n",
       "      <td>...</td>\n",
       "      <td>114828.0</td>\n",
       "      <td>3739.0</td>\n",
       "      <td>1146253.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>4415.0</td>\n",
       "      <td>2779136.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>207962.0</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>887040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>20287</td>\n",
       "      <td>79838</td>\n",
       "      <td>53444</td>\n",
       "      <td>17554</td>\n",
       "      <td>73680</td>\n",
       "      <td>56091</td>\n",
       "      <td>2876</td>\n",
       "      <td>-877</td>\n",
       "      <td>-1096</td>\n",
       "      <td>...</td>\n",
       "      <td>305451.0</td>\n",
       "      <td>8412.0</td>\n",
       "      <td>2559863.0</td>\n",
       "      <td>11789.0</td>\n",
       "      <td>9115.0</td>\n",
       "      <td>4935387.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>401064.0</td>\n",
       "      <td>3939.0</td>\n",
       "      <td>1597489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>14945</td>\n",
       "      <td>85971</td>\n",
       "      <td>55019</td>\n",
       "      <td>12444</td>\n",
       "      <td>74984</td>\n",
       "      <td>56944</td>\n",
       "      <td>2232</td>\n",
       "      <td>209</td>\n",
       "      <td>-746</td>\n",
       "      <td>...</td>\n",
       "      <td>261506.0</td>\n",
       "      <td>5838.0</td>\n",
       "      <td>1764846.0</td>\n",
       "      <td>8926.0</td>\n",
       "      <td>7214.0</td>\n",
       "      <td>4502793.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>288138.0</td>\n",
       "      <td>3162.0</td>\n",
       "      <td>1415049.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>7254</td>\n",
       "      <td>72810</td>\n",
       "      <td>60610</td>\n",
       "      <td>6471</td>\n",
       "      <td>73234</td>\n",
       "      <td>64324</td>\n",
       "      <td>1161</td>\n",
       "      <td>-2555</td>\n",
       "      <td>-2237</td>\n",
       "      <td>...</td>\n",
       "      <td>119395.0</td>\n",
       "      <td>3101.0</td>\n",
       "      <td>892958.0</td>\n",
       "      <td>4927.0</td>\n",
       "      <td>4105.0</td>\n",
       "      <td>2187601.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>146548.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>565909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>8281</td>\n",
       "      <td>68611</td>\n",
       "      <td>52731</td>\n",
       "      <td>7227</td>\n",
       "      <td>66146</td>\n",
       "      <td>55890</td>\n",
       "      <td>1163</td>\n",
       "      <td>-1335</td>\n",
       "      <td>-927</td>\n",
       "      <td>...</td>\n",
       "      <td>130549.0</td>\n",
       "      <td>3803.0</td>\n",
       "      <td>1150115.0</td>\n",
       "      <td>4955.0</td>\n",
       "      <td>3997.0</td>\n",
       "      <td>2167997.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>160952.0</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>559162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>12727</td>\n",
       "      <td>62869</td>\n",
       "      <td>53468</td>\n",
       "      <td>11306</td>\n",
       "      <td>65256</td>\n",
       "      <td>57934</td>\n",
       "      <td>1755</td>\n",
       "      <td>-5038</td>\n",
       "      <td>-3687</td>\n",
       "      <td>...</td>\n",
       "      <td>146271.0</td>\n",
       "      <td>6096.0</td>\n",
       "      <td>1804162.0</td>\n",
       "      <td>7594.0</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>3195269.0</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>208202.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>711846.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 177 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode  Count taxable income or loss  Average taxable income or loss  \\\n",
       "0     2000                         36185                           47723   \n",
       "1     2006                            83                           80905   \n",
       "2     2007                          4769                           46549   \n",
       "3     2008                          5607                          108816   \n",
       "4     2009                          9726                           82938   \n",
       "5     2010                         20287                           79838   \n",
       "6     2011                         14945                           85971   \n",
       "7     2015                          7254                           72810   \n",
       "8     2016                          8281                           68611   \n",
       "9     2017                         12727                           62869   \n",
       "\n",
       "   Median taxable income or loss  Count salary and wages  \\\n",
       "0                          18213                   31293   \n",
       "1                          58150                      74   \n",
       "2                          31474                    4153   \n",
       "3                          41151                    5008   \n",
       "4                          50604                    8167   \n",
       "5                          53444                   17554   \n",
       "6                          55019                   12444   \n",
       "7                          60610                    6471   \n",
       "8                          52731                    7227   \n",
       "9                          53468                   11306   \n",
       "\n",
       "   Average salary and wages  Median salary and wages  Count net rent  \\\n",
       "0                     38710                    17992            3614   \n",
       "1                     82733                    67658              21   \n",
       "2                     47386                    34366             548   \n",
       "3                     53418                    42892             612   \n",
       "4                     74068                    55243            1714   \n",
       "5                     73680                    56091            2876   \n",
       "6                     74984                    56944            2232   \n",
       "7                     73234                    64324            1161   \n",
       "8                     66146                    55890            1163   \n",
       "9                     65256                    57934            1755   \n",
       "\n",
       "   Average net rent  Median net rent  ...  \\\n",
       "0               558              207  ...   \n",
       "1              1042             -807  ...   \n",
       "2             -1242             -794  ...   \n",
       "3              1818            -1728  ...   \n",
       "4             -2412            -1520  ...   \n",
       "5              -877            -1096  ...   \n",
       "6               209             -746  ...   \n",
       "7             -2555            -2237  ...   \n",
       "8             -1335             -927  ...   \n",
       "9             -5038            -3687  ...   \n",
       "\n",
       "   Small business income tax offset \\n$  Low income tax offset \\nno.  \\\n",
       "0                              273962.0                      10143.0   \n",
       "1                                 396.0                         26.0   \n",
       "2                               31587.0                       2200.0   \n",
       "3                               71139.0                       2690.0   \n",
       "4                              114828.0                       3739.0   \n",
       "5                              305451.0                       8412.0   \n",
       "6                              261506.0                       5838.0   \n",
       "7                              119395.0                       3101.0   \n",
       "8                              130549.0                       3803.0   \n",
       "9                              146271.0                       6096.0   \n",
       "\n",
       "   Low income tax offset \\n$  People with private health insurance \\nno.  \\\n",
       "0                  3466009.0                                      7710.0   \n",
       "1                     7825.0                                        53.0   \n",
       "2                   716233.0                                      1763.0   \n",
       "3                   854648.0                                      2678.0   \n",
       "4                  1146253.0                                      5628.0   \n",
       "5                  2559863.0                                     11789.0   \n",
       "6                  1764846.0                                      8926.0   \n",
       "7                   892958.0                                      4927.0   \n",
       "8                  1150115.0                                      4955.0   \n",
       "9                  1804162.0                                      7594.0   \n",
       "\n",
       "   Private health insurance - your Australian Government rebate received \\nno.  \\\n",
       "0                                             5875.0                             \n",
       "1                                               37.0                             \n",
       "2                                             1394.0                             \n",
       "3                                             2019.0                             \n",
       "4                                             4415.0                             \n",
       "5                                             9115.0                             \n",
       "6                                             7214.0                             \n",
       "7                                             4105.0                             \n",
       "8                                             3997.0                             \n",
       "9                                             6270.0                             \n",
       "\n",
       "   Private health insurance - your Australian Government rebate received \\n$  \\\n",
       "0                                          3815543.0                           \n",
       "1                                            25403.0                           \n",
       "2                                           744748.0                           \n",
       "3                                          1044569.0                           \n",
       "4                                          2779136.0                           \n",
       "5                                          4935387.0                           \n",
       "6                                          4502793.0                           \n",
       "7                                          2187601.0                           \n",
       "8                                          2167997.0                           \n",
       "9                                          3195269.0                           \n",
       "\n",
       "   Private health insurance rebate \\nno.  Private health insurance rebate \\n$  \\\n",
       "0                                  984.0                             234746.0   \n",
       "1                                    7.0                               1694.0   \n",
       "2                                  227.0                              45656.0   \n",
       "3                                  398.0                              85465.0   \n",
       "4                                  768.0                             207962.0   \n",
       "5                                 1670.0                             401064.0   \n",
       "6                                 1102.0                             288138.0   \n",
       "7                                  572.0                             146548.0   \n",
       "8                                  648.0                             160952.0   \n",
       "9                                 1046.0                             208202.0   \n",
       "\n",
       "   Excess private health reduction or refund (rebate reduced) \\nno.  \\\n",
       "0                                             2358.0                  \n",
       "1                                               18.0                  \n",
       "2                                              453.0                  \n",
       "3                                              667.0                  \n",
       "4                                             1864.0                  \n",
       "5                                             3939.0                  \n",
       "6                                             3162.0                  \n",
       "7                                             1615.0                  \n",
       "8                                             1540.0                  \n",
       "9                                             2183.0                  \n",
       "\n",
       "   Excess private health reduction or refund (rebate reduced) \\n$  \n",
       "0                                          1248220.0               \n",
       "1                                            10150.0               \n",
       "2                                           167480.0               \n",
       "3                                           226170.0               \n",
       "4                                           887040.0               \n",
       "5                                          1597489.0               \n",
       "6                                          1415049.0               \n",
       "7                                           565909.0               \n",
       "8                                           559162.0               \n",
       "9                                           711846.0               \n",
       "\n",
       "[10 rows x 177 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Jointure à gauche de df_impots à postcode_df\n",
    "merge_df = pd.merge(\n",
    "    df_impots, \n",
    "    postcode_df, \n",
    "    how = 'left', \n",
    "    on = 'Postcode'\n",
    ")\n",
    "\n",
    "merge_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63c080ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2473, 177)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87350f9f",
   "metadata": {},
   "source": [
    "La jointure à gauche permet de conserver toutes les observations dans la première table (ou table de gauche) qu'elles apparaissent ou non dans la deuxième table (ou table de droite). Il est donc normal que la dataframe *merge_df* compte 2473 lignes tout comme *df*. De plus, nous avons maintenant 177 variables : 150 colonnes pour *postcode_df* + 28 colonnes pour *df* - 1 colonne commune (colonne 'Poscode').\n",
    "\n",
    "Nous sommes passés de 28 variables à 177 variables ce qui est forcément intéressant pour une analyse des données pour en tirer d'informations utiles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f3fcd3",
   "metadata": {},
   "source": [
    "# <font color=red> Création de nouvelles variables à partir des variables existantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64dea1d8",
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importation des données d'une société de commerce en ligne\n",
    "df = pd.read_excel('Online Retail.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c7ecdf3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0abe2f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany',\n",
       "       'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal',\n",
       "       'Italy', 'Belgium', 'Lithuania', 'Japan', 'Iceland',\n",
       "       'Channel Islands', 'Denmark', 'Cyprus', 'Sweden', 'Austria',\n",
       "       'Israel', 'Finland', 'Bahrain', 'Greece', 'Hong Kong', 'Singapore',\n",
       "       'Lebanon', 'United Arab Emirates', 'Saudi Arabia',\n",
       "       'Czech Republic', 'Canada', 'Unspecified', 'Brazil', 'USA',\n",
       "       'European Community', 'Malta', 'RSA'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pays\n",
    "df.Country.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d453c3",
   "metadata": {},
   "source": [
    "Nous pouvons créer une colonne nommée *Region* qui regroupe plusieurs pays de la même région ou du même continent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4cc799b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regroupement des pays\n",
    "pays_asiatiques = ['Japan', 'Hong Kong', 'Singapore']\n",
    "pays_du_moyen_orient = ['Israel', 'Bahrain', 'Lebanon', 'United Arab Emirates', 'SaudiArabia']\n",
    "pays_en_amerique = ['Canada', 'Brazil', 'USA']\n",
    "pays_europeens = ['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany', \n",
    "                  'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal', \n",
    "                  'Italy', 'Belgium', 'Lithuania', 'Iceland', 'Denmark', 'Sweden', \n",
    "                  'Finland', 'Greece', 'Czech Republic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f28521e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Création de la colonne 'Region'\n",
    "df['Region'] = df['Country']\n",
    "df.loc[df['Country'].isin(pays_asiatiques), 'Region'] = 'Asie'\n",
    "df.loc[df['Country'].isin(pays_du_moyen_orient), 'Region'] = 'Moyen-Orient'\n",
    "df.loc[df['Country'].isin(pays_en_amerique), 'Region'] = 'Amerique'\n",
    "df.loc[df['Country'].isin(pays_europeens), 'Region'] = 'Europe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "61138e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  Region  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  Europe  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  Europe  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca580d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Europe', 'Asie', 'Channel Islands', 'Cyprus', 'Austria',\n",
       "       'Moyen-Orient', 'Saudi Arabia', 'Amerique', 'Unspecified',\n",
       "       'European Community', 'Malta', 'RSA'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Region.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fab5d74",
   "metadata": {},
   "source": [
    "# <font color=red> Manipulation des dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f808c7",
   "metadata": {},
   "source": [
    "Il n'est pas rare de travailler avec des jeux de données ayant une ou plusieurs variables contenant des dates. Vous pouvez créer de nouvelles variables comme par exemple le mois de l'année et le jour de la semaine à partir de ce type de variable.\n",
    "\n",
    "Dans la dataframe *df*, la colonne *InvoiceDate* indique la date et le temps et est stcokée sous format datetime. A partir de cette variable, nous pouvons en créer plusieurs autres variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc7e7a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraction de l'année\n",
    "df['Year'] = df['InvoiceDate'].dt.year\n",
    "\n",
    "# Extraction du jour de la semaine (0 correspond à Lundi)\n",
    "df['Day Of Week'] = df['InvoiceDate'].dt.dayofweek\n",
    "\n",
    "# Extraction du mois\n",
    "df['Month'] = df['InvoiceDate'].dt.month\n",
    "\n",
    "# Extraction du jour du mois\n",
    "df['Day'] = df['InvoiceDate'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14c32bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
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       "      <th>Description</th>\n",
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       "      <th>UnitPrice</th>\n",
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       "      <th>Year</th>\n",
       "      <th>Day Of Week</th>\n",
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       "      <td>2.55</td>\n",
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       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  Region  Year  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  Europe  2010   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  Europe  2010   \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "\n",
       "   Day Of Week  Month  Day  \n",
       "0            2     12    1  \n",
       "1            2     12    1  \n",
       "2            2     12    1  \n",
       "3            2     12    1  \n",
       "4            2     12    1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "027132e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 6, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day Of Week'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78224821",
   "metadata": {},
   "source": [
    "Dans la variable représentant le jour de la semaine, 0 correspond au Lundi et 6 correspond donc au Dimanche. A partir de cette variable, nous pouvons encore créer une autre variable indiquant s'il s'agit d'un jour du week-end ou pas. Le week-end étant composé de Samedi et Dimanche."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc0e4951",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Est-ce un jour de week-end ou pas\n",
    "df['Is Weekend'] = df['Day Of Week'] >= 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c67631d8",
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Is Weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225330</th>\n",
       "      <td>556671</td>\n",
       "      <td>23301</td>\n",
       "      <td>GARDENERS KNEELING PAD KEEP CALM</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-06-13 16:33:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17841.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278375</th>\n",
       "      <td>561209</td>\n",
       "      <td>85163B</td>\n",
       "      <td>BLACK BAROQUE WALL CLOCK</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-07-25 16:57:00</td>\n",
       "      <td>24.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429256</th>\n",
       "      <td>573551</td>\n",
       "      <td>23332</td>\n",
       "      <td>IVORY WICKER HEART LARGE</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-10-31 13:35:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>15044.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86774</th>\n",
       "      <td>543598</td>\n",
       "      <td>48138</td>\n",
       "      <td>DOORMAT UNION FLAG</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-02-10 13:06:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>18230.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150496</th>\n",
       "      <td>549440</td>\n",
       "      <td>48187</td>\n",
       "      <td>DOORMAT NEW ENGLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-04-08 12:42:00</td>\n",
       "      <td>7.95</td>\n",
       "      <td>17162.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                        Description  Quantity  \\\n",
       "225330    556671     23301  GARDENERS KNEELING PAD KEEP CALM          5   \n",
       "278375    561209    85163B          BLACK BAROQUE WALL CLOCK          1   \n",
       "429256    573551     23332           IVORY WICKER HEART LARGE         6   \n",
       "86774     543598     48138                 DOORMAT UNION FLAG        12   \n",
       "150496    549440     48187                DOORMAT NEW ENGLAND         2   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID         Country  Region  \\\n",
       "225330 2011-06-13 16:33:00       1.65     17841.0  United Kingdom  Europe   \n",
       "278375 2011-07-25 16:57:00      24.96         NaN  United Kingdom  Europe   \n",
       "429256 2011-10-31 13:35:00       1.65     15044.0  United Kingdom  Europe   \n",
       "86774  2011-02-10 13:06:00       6.75     18230.0  United Kingdom  Europe   \n",
       "150496 2011-04-08 12:42:00       7.95     17162.0  United Kingdom  Europe   \n",
       "\n",
       "        Year  Day Of Week  Month  Day  Is Weekend  \n",
       "225330  2011            0      6   13       False  \n",
       "278375  2011            0      7   25       False  \n",
       "429256  2011            0     10   31       False  \n",
       "86774   2011            3      2   10       False  \n",
       "150496  2011            4      4    8       False  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560b7cfd",
   "metadata": {},
   "source": [
    "# <font color=red> Aggrégation des données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f431f9d6",
   "metadata": {},
   "source": [
    "Une autre technique utilisée en ingénierie des fonctionnalités pour la création de nouvelles variables est l'aggrégation des données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d8bf2d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Australia                 83653\n",
       "Austria                    4827\n",
       "Bahrain                     260\n",
       "Belgium                   23152\n",
       "Brazil                      356\n",
       "Canada                     2763\n",
       "Channel Islands            9479\n",
       "Cyprus                     6317\n",
       "Czech Republic              592\n",
       "Denmark                    8188\n",
       "EIRE                     142637\n",
       "European Community          497\n",
       "Finland                   10666\n",
       "France                   110480\n",
       "Germany                  117448\n",
       "Greece                     1556\n",
       "Hong Kong                  4769\n",
       "Iceland                    2458\n",
       "Israel                     4353\n",
       "Italy                      7999\n",
       "Japan                     25218\n",
       "Lebanon                     386\n",
       "Lithuania                   652\n",
       "Malta                       944\n",
       "Netherlands              200128\n",
       "Norway                    19247\n",
       "Poland                     3653\n",
       "Portugal                  16180\n",
       "RSA                         352\n",
       "Saudi Arabia                 75\n",
       "Singapore                  5234\n",
       "Spain                     26824\n",
       "Sweden                    35637\n",
       "Switzerland               30325\n",
       "USA                        1034\n",
       "United Arab Emirates        982\n",
       "United Kingdom          4263829\n",
       "Unspecified                3300\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantité totale d'articles vendus par pays\n",
    "df.groupby('Country')['Quantity'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0f14db",
   "metadata": {},
   "source": [
    "Il est également possible de grouper selon deux variables :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f423fa31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country      StockCode\n",
       "Australia    15036        600\n",
       "             20665          6\n",
       "             20675        216\n",
       "             20676        216\n",
       "             20677        216\n",
       "                         ... \n",
       "Unspecified  85049A         1\n",
       "             85179A         1\n",
       "             85179C         1\n",
       "             85180A         2\n",
       "             85180B         1\n",
       "Name: Quantity, Length: 19839, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantité totale d'articles vendus par pays et par produit\n",
    "df.groupby(['Country', 'StockCode'])['Quantity'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750bcf47",
   "metadata": {},
   "source": [
    "A partir de ce résultat, nous pouvons dire par exemple que sur la période étudiée, l'Autralie a vendu la même quantité d'articles pour les produits dont les codes en stock sont 20675, 20676 et 20677."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e139cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Australia</th>\n",
       "      <th>15036</th>\n",
       "      <th>2011-05-17</th>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20665</th>\n",
       "      <th>2011-03-24</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20675</th>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-03</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20676</th>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Unspecified</th>\n",
       "      <th>85049A</th>\n",
       "      <th>2011-07-28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85179A</th>\n",
       "      <th>2011-07-28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85179C</th>\n",
       "      <th>2011-07-28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85180A</th>\n",
       "      <th>2011-07-28</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85180B</th>\n",
       "      <th>2011-07-28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>310015 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Quantity\n",
       "Country     StockCode Invoice_Date          \n",
       "Australia   15036     2011-05-17         600\n",
       "            20665     2011-03-24           6\n",
       "            20675     2011-01-06          72\n",
       "                      2011-03-03         144\n",
       "            20676     2011-01-06          72\n",
       "...                                      ...\n",
       "Unspecified 85049A    2011-07-28           1\n",
       "            85179A    2011-07-28           1\n",
       "            85179C    2011-07-28           1\n",
       "            85180A    2011-07-28           2\n",
       "            85180B    2011-07-28           1\n",
       "\n",
       "[310015 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantité totale d'articles vendus par pays, produits et date\n",
    "df['Invoice_Date'] = df['InvoiceDate'].dt.date\n",
    "df.groupby(['Country', 'StockCode', 'Invoice_Date']).agg({'Quantity':'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "25593311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>15036</td>\n",
       "      <td>2011-05-17</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>20665</td>\n",
       "      <td>2011-03-24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>20675</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>20675</td>\n",
       "      <td>2011-03-03</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>20676</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country StockCode Invoice_Date  Quantity\n",
       "0  Australia     15036   2011-05-17       600\n",
       "1  Australia     20665   2011-03-24         6\n",
       "2  Australia     20675   2011-01-06        72\n",
       "3  Australia     20675   2011-03-03       144\n",
       "4  Australia     20676   2011-01-06        72"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformation du résultat ci-dessus en dataframe\n",
    "df_agg = df.groupby(['Country', 'StockCode', 'Invoice_Date']).agg({'Quantity':'sum'}).reset_index()\n",
    "df_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "21f0270c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310015, 4)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7e95bc",
   "metadata": {},
   "source": [
    "Nous pouvons maintenant fusionner la dataframe *df* avec *df_agg*. Etant donné que nous voulons avoir toutes les observations dans *df*, alors nous ferons une jointure à gauche."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b19e2e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity_x</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Is Weekend</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Quantity_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity_x  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER           6   \n",
       "1    536365     71053                  WHITE METAL LANTERN           6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER           8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE           6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.           6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  Region  Year  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  Europe  2010   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  Europe  2010   \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "\n",
       "   Day Of Week  Month  Day  Is Weekend Invoice_Date  Quantity_y  \n",
       "0            2     12    1       False   2010-12-01         454  \n",
       "1            2     12    1       False   2010-12-01          33  \n",
       "2            2     12    1       False   2010-12-01          40  \n",
       "3            2     12    1       False   2010-12-01          59  \n",
       "4            2     12    1       False   2010-12-01         551  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fusion de df et df_agg\n",
    "df_fusion = pd.merge(\n",
    "    df, \n",
    "    df_agg, \n",
    "    how = 'left', \n",
    "    on = ['Country', 'StockCode', 'Invoice_Date']\n",
    ")\n",
    "\n",
    "df_fusion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1769a4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 16)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fusion.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f04e9ae",
   "metadata": {},
   "source": [
    "La dataframe *df* avait au départ 8 variables et elle a compte 16 à présent. \n",
    "\n",
    "Parce-qu'il y a vait deux colonnes 'Quantity', la fusion a renommée les variables respectivement en *Quantity_x* et *Quantity_y*. Nous pouvons les renommer :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d9ba4f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
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       "      <th>Is Weekend</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Total Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
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       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
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       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
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       "      <td>2010-12-01</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                         Description  Quantity  \\\n",
       "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                 WHITE METAL LANTERN         6   \n",
       "2    536365    84406B      CREAM CUPID HEARTS COAT HANGER         8   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  Region  Year  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  Europe  2010   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  Europe  2010   \n",
       "\n",
       "   Day Of Week  Month  Day  Is Weekend Invoice_Date  Total Quantity  \n",
       "0            2     12    1       False   2010-12-01             454  \n",
       "1            2     12    1       False   2010-12-01              33  \n",
       "2            2     12    1       False   2010-12-01              40  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fusion.rename(\n",
    "    columns = {'Quantity_x':'Quantity', 'Quantity_y':'Total Quantity'}, \n",
    "    inplace = True\n",
    ")\n",
    "\n",
    "df_fusion.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5ac9e5",
   "metadata": {},
   "source": [
    "On peut aussi créer le ratio entre les variables *Quantity* et *Total Quantity*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "941db928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
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       "      <th>Year</th>\n",
       "      <th>Day Of Week</th>\n",
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       "      <th>Day</th>\n",
       "      <th>Is Weekend</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Total Quantity</th>\n",
       "      <th>Quantity Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>454</td>\n",
       "      <td>0.013216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>33</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>40</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                         Description  Quantity  \\\n",
       "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                 WHITE METAL LANTERN         6   \n",
       "2    536365    84406B      CREAM CUPID HEARTS COAT HANGER         8   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  Region  Year  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  Europe  2010   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Europe  2010   \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  Europe  2010   \n",
       "\n",
       "   Day Of Week  Month  Day  Is Weekend Invoice_Date  Total Quantity  \\\n",
       "0            2     12    1       False   2010-12-01             454   \n",
       "1            2     12    1       False   2010-12-01              33   \n",
       "2            2     12    1       False   2010-12-01              40   \n",
       "\n",
       "   Quantity Ratio  \n",
       "0        0.013216  \n",
       "1        0.181818  \n",
       "2        0.200000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fusion['Quantity Ratio'] = df_fusion['Quantity'] / df_fusion['Total Quantity']\n",
    "\n",
    "df_fusion.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655e714b",
   "metadata": {},
   "source": [
    "Félicitations à vous pour avoir suivi ce tutoriel :)\n",
    "\n",
    "Vous êtes désormais capable de réaliser l'étape de ***Feature Engineering*** en utilisant différentes techniques ce qui vous permettra d'enrichir votre ensemble de données et d'améliorer par conséquent vos modèles de Machine Learning. \n",
    "\n",
    "Ne vous arrêtez pas en si bon chemin dans votre apprentissage ! Continuez votre formation en Data Science et particulièrement en Machine Learning. Je vous conseille de vous proccurer mon livre intitulé : [Machine Learning par la pratique avec Python: Projets réels dans les Finances, l'Immobilier, le Trading, la Santé, le Marketing, etc.](https://www.amazon.fr/gp/product/B08F4NZZ1C/ref=dbs_a_def_rwt_hsch_vapi_tkin_p1_i3) disponible en versions papier et numérique sur Amazon."
   ]
  },
  {
   "attachments": {
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AScj/AGSPQ1Y/Zl+I/wDwg/jVbHUZtmhasywzlj8sMnRJfYZOD7HPYV9gfFrwPa/EHwTfaLc7UuCPNtJ2H+pmUHa305IPsTX5z6rp91pOp3Wn6jC0F5aytDNE/VHU4I/OgD9SaK8S/Zb+I/8AwmPg4aPqc27W9HRY2LH5poOiP7kfdP0BP3q9toAK+Gf2pPiR/wAJj4wOjaZNu0PR3aNSp+Wefo8nuB90fQkfer6C/ab+JH/CEeCzp+mzbNd1ZWhgKn5oYujy+xwcD3Of4TXwvp9nc6jf29lYwvPdXEixRRIMs7scAD3JNAHpX7PPw5b4g+OI1vIidD07bcXzdnGfli+rEfkGr9AURY0VEUKijAUDAA9BXEfBzwHb/DzwPZ6RHse9f9/ezL/y0mIGefQcKPYepNdxQAUUUUAfIP7b/wDyNPhn/rzk/wDQ6439kr/ktWm/9e1x/wCizXZftv8A/I0+Gf8Arzk/9Drjf2Sv+S1ab/17XH/os0AfeFVNY/5BN7/1wf8A9BNW6qax/wAgm9/64P8A+gmgD8tq/QT9mT/khvhf/cn/APSiWvz7r9BP2ZP+SG+F/wDcn/8ASiWgD1CvlP8Abm/1ngr6Xv8A7Qr6sr5T/bm/1ngr6Xv/ALQoA8o/Zk/5Ll4X/wB+f/0nlr9A6/Pz9mT/AJLl4X/35/8A0nlr9A6ACiiigD4X/a//AOSxzf8AXjB/I10H7En/ACPev/8AYN/9qpXP/tf/APJY5v8Arxg/ka6D9iT/AJHvX/8AsG/+1UoA+yKKKKACvPP2hP8AkjHiv/r0/wDZ1r0OvPP2hP8AkjHiv/r0/wDZ1oA/O6v088G/8ihof/XjB/6LWvzDr9PPBv8AyKGh/wDXjB/6LWgDYooooAbI6xozyMqIoLMzHAAHc1+f37QHxLn+IfjCX7NKw0CwZorGLoHHQykerY/AYHrn6i/ao8WP4Z+FV1b2shS91eQWKEHkIQTIf++QV/4EK+DkRpHVEUszHAAGST6UAdj8K/h5q/xG8RLpukqIoIwHuryRSY7dPU+rHsvf2AJH3B8OvhF4S8CW0R03To7nUVALahdqJJi3qpIwg9lx+PWpfgn4Eg+H/gKx0zy1GoyqLi/kHJeZhyM+i/dH0z3Nd5QAVU1XTLDV7J7PVbK2vbV/vQ3ESyIfqCMVbooA+V/jh+znbxWVzrvw9hdHiBkn0nJYMOpMJPOf9g5z2xwD8qqzxyBlLI6nIIOCCK/VKvh/9rLwFF4W8bRa1psIj03W90pRRxHcLjzB7BshvqW9KAPf/wBmn4mP498ItaarLv1/SwsdwxPM8Z+5L9Tgg+4z3Few1+d/wB8WP4P+KWjXjSFLO6kFldDPBikIGT7K21v+A1+iFABRRRQB8f8A7b3/ACNnhv8A68ZP/RlfOlnY3N6ly1rC8q20Rnm2/wAEYIBb6AsK+i/23v8AkbPDf/XjJ/6Mrlf2S7O31H4o3NlewrNa3GlXMUsTjIdG2gg/UGgDzXwF4ovfBni3Tde04/vrSUMyZwJUPDofYqSPxr9I/Dms2XiHQrDV9Ll82yvYVmibvgjofQjoR2INfnb8WvBVx4B8dahok25rdW820lb/AJawNnY316g+6mvc/wBjf4g+VPceCNTm+STdc6cWPRuskQ+o+cD2b1oA+sKKKKAPzw/aH/5LT4r/AOvof+gLXvX7EH/Ir+Jv+vyL/wBANeC/tD/8lp8V/wDX0P8A0Ba96/Yg/wCRX8Tf9fkX/oBoA+laKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA85/aC8Vf8Ij8KdavIpNl5dJ9htiDg+ZJkZHuF3N/wGvz606zn1HULWys0MlzcyrDEg6s7EAD8yK+iv20/FX2zxLpPhi3kzFp8RurgA/8ALWThQfcIM/8AbSuT/ZP8L/8ACQfFa2vZk3WmjxNePkcGT7sY+u47v+AUAfa3hDQ4PDXhbStFtcGGwtkgDAY3FQAW+pOT+NM8a6DD4o8I6volxgR31s8AY/wMR8rfg2D+FbVFAH5ZXtrNZXk9rdIY7iCRopEPVWU4IP4iv0K+APir/hLvhVol9LJvvLeP7FcknJ8yP5cn3K7W/wCBV8n/ALVPhf8A4R34tX1zDHttNXRb+PA43tkSD671Lf8AAhXbfsVeKfsuv6x4XuJMRXsQvLcE8eYnDge5Ug/8AoA+vaKKKACiiigCC/tIb+xuLO7jEltcRtFKh6MrAgj8jX5meNNBn8L+LNW0S5yZLC5eDceN6g/K34jB/Gv06r42/bQ8Lf2f4x03xHBHiHVIPJmIH/LaLABP1QqB/uGgD039jnxR/a3w7udDnfdcaNcEICefJkyy/wDj3mD6AV75Xwb+yr4o/wCEd+LNlazSbbTV0axfJ43nmM/XcAv/AAI195UAFcF8dfFP/CIfC3XdRjk2XckP2W2IOD5snygj3AJb/gNd7XyX+2x4p87UdD8LQSZSBDf3Kg8b2ykYPuAHP/AhQB8wKpdgqgsxOAAMkmv0m+E/hceDvh3oeiFQs9vbhrjHeZ/mk/8AHmI+gFfEv7OXhb/hK/izo8Mse+zsW+33HGRtjIKg+xcoPoa/QegAooooAKKKKAPy513/AJDeof8AXxJ/6Ea+3f2Q/wDkjVr/ANfk/wD6EK+Itd/5Deof9fEn/oRq/pHi/wAS6NZi00fxDrFhaBiwhtb2WJAT1O1WAzQB+m9fK37c0jgeDIgx8s/bGK54JHkgH9T+dfPv/CxfG3/Q4eI//BnP/wDFVl654i1vXzD/AG7rGo6n5G7yvtl083l5xnbuJxnAzj0FAHq37Icjx/GS2VGIWSynVgO4wDg/iAfwr7pr4T/ZG/5LPZf9elx/6BX3ZQBkJ4Y0CPUPt8eh6Wt9u3/aFtIxJu9d2M5rXoooAKKKKAPzr+PmrNrPxi8VXDNuEd41qvoBEBFx/wB8V6t+xJoaXHiXxDrcigmzto7aMnsZWJJHviPH/Aq8D8aSmfxjr0zctJfzufqZGNfVn7EkKr4I8QTgDc+ohCfZYlI/9CNAH0dRRRQAUUUUAVNX0+31bSb3TrxS1reQPbyqDglHUqw/ImvmB/2Sv+Jidni7Fhuzg2OZdvpnfjPv+navqqigChoGlW2haFp+k2IYWljbx20W45O1FCjJ7nAq/RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVw3x0/5I/4u/wCwfL/Ku5rhvjp/yR/xd/2D5f5UAfnJX6ZfDj/knnhf/sF2v/ola/M2v0y+HH/JPPC//YLtf/RK0AdFRRUV1cwWkDz3U0cEKDLSSMFUD3JoAlr4D/ai1eHV/jPrX2Zg8dmsVpuH95EG8fgxYfhX0D8Y/wBobQ/D2nXOn+DruHVtckUos8JD29t/tFujsOwGR6nsfiq4mlubiWe4keWaVi7u5yzMTkknuSaAPf8A9iuxeb4k6reY/dW+mOhP+08keP0Vq+0a8R/ZP8DTeFPAD6nqMRi1HW2W4KMMMkCg+WD7nLN9GHpXt1ABRRRQAV8rfthfDfIj8c6RDyNsGpqg/BJv5If+A+9fVNVtUsLXVdNurDUIUns7mNoZon6OjDBB/A0Afm38NvGF74F8Zafr2n5YwPiaLOBNEeHQ/UdPQgHtX6IReK9Gk8GjxSLxBoptftnnntHjPT17Y65461+ffxc8DXXw+8b3uiz73ts+dZzsP9bAxO0/UcqfcGqq+O9cX4ev4MFyf7Fa6+1FOd3rsz/c3YbH94ZoAf8AFHxpeePfGl/rt7uRJW2W8JOfJhX7iflyfUknvXvH7H3w386Z/HGrw/u4y0OmI4+83R5fw5Ue+70FeFfCnwTd+P8AxrY6Jabkhc+ZdTgf6mFcbm+vQD3Ir9GdH0200bSrTTdNhWCytIlhhjXoqqMAUAXKKKKACiiigD5G/bgt5Rr/AIXuCjeS1tNGHxxuDqSPyIrjf2RbeWb4zWckaMyQWk7yEDhQV2gn8WA/GvtzWtG0zXLP7JrWnWeoWud3lXUKypn1wwIz71BoHhvQ/DsciaDo+n6asmN/2S3SLfjpnaBn8aANaqmsf8gm9/64P/6Cat1U1j/kE3v/AFwf/wBBNAH5bV+gn7Mn/JDfC/8AuT/+lEtfn3X6Cfsyf8kN8L/7k/8A6US0AeoV8sfty28rQ+DbgRsYEa7jZwOAx8kgH6hW/I19T1T1bS9P1myaz1extb60Y5aG5iWVCfXawIoA+Df2XbeWf44eHWiRmWL7RJIQOFXyJBk/iQPxFff1Y2geFtA8OmRtB0XTdNaQYdrS2SIuPQlQCa2aACiiigD4X/a//wCSxzf9eMH8jXQfsSf8j3r/AP2Df/aqVz/7X/8AyWOb/rxg/ka8k0TXNW0KeSfQ9Uv9NmkXY8lncPCzLnOCVIyM9qAP1Dor81P+Fi+Nv+hw8R/+DOf/AOKo/wCFi+Nv+hw8R/8Agzn/APiqAP0rrzz9oT/kjHiv/r0/9nWvhf8A4WL42/6HDxH/AODOf/4qq+oeN/FepWUtnqPifXLu0lG2SCfUJZEcehUtg0Ac9X6eeDf+RQ0P/rxg/wDRa1+Ydfp54N/5FDQ/+vGD/wBFrQBsUUUUAfH/AO25qzTeK/DukBvktrJ7oj3kfb/KL9a8u/Z+0RNf+MPhq0mUNDHcfanBGRiJTIAfqVA/Guq/bAlaT4xSqekdjAg+nzH+tTfscwrL8XndgCYtNncex3Iv/sxoA+4aKKKACiiigArx79q/RE1f4OajcbQZtMmivIzjn72xv/HXJ/CvYa434zQrP8JfGCMAQNKuH59VjLD+VAH5vAkEEHBFfpv4G1U674K0HVXOXvbCC4c/7TRgn9Sa/Mev0S/Z9lM3wZ8KMxyRabPwVmA/lQB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    }
   },
   "cell_type": "markdown",
   "id": "40a62ce5",
   "metadata": {},
   "source": [
    "![couverture_jpeg.jpg](attachment:couverture_jpeg.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ea1d73",
   "metadata": {},
   "source": [
    "A travers ce livre, vous apprendrez à :\n",
    "\n",
    "✅ Nettoyer un jeu de données et la rendre prête pour la modélisation (Traitement des valeurs manquantes, Détection et suppression des outliers, Encodage des variables catégorielles, Normalisation des données, etc.) ;\n",
    "\n",
    "✅ Construire un modèle de classification (LogisticRegression, RandomForestClassifier, DecisionTreeClassifier, KNeighborsClassifier, etc.) et d'un modèle de régression (LinearRegression, RandomForestRegressor, DecisionTreeRegressor, KNeighborsRegressor, etc.) ;\n",
    "\n",
    "✅ Evaluer la performance d’un modèle (Données d’entraînement et d’évaluation, Choix de la métrique, Validation croisée, Robustesse du modèle, etc.)\n",
    "\n",
    "✅ effectuer des prédictions ;\n",
    "\n",
    "✅ modéliser des séries temporelles et effectuer des prévisions ;\n",
    "\n",
    "✅ rechercher les hyperparamètres optimaux d'un modèle en utilisant les méthodes Grid Search et Random Search ;\n",
    "\n",
    "✅ automatiser la sélection du meilleur modèle avec l'outil TPOT ;\n",
    "\n",
    "✅ automatiser le flux de travail de vos projets de Machine Learning ;\n",
    "\n",
    "✅ effectuer des segmentations avec des algorithmes comme KMeans ;\n",
    "\n",
    "✅ réduire la dimension de grand ensembles de données en utilisant l'ACP, ...etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3e11fd",
   "metadata": {},
   "source": [
    "Vous pouvez aussi regarder ma vidéo YouTube [Comment construire un modèle de Machine Learning avec Python et Scikit-Learn | Tutoriel complet](https://youtu.be/idPDzWybHMw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d1df24c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\josue\\anaconda3\\lib\\site-packages\\IPython\\core\\display.py:717: UserWarning: Consider using IPython.display.IFrame instead\n",
      "  warnings.warn(\"Consider using IPython.display.IFrame instead\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/idPDzWybHMw\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/idPDzWybHMw\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1250f157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"https://afouda-datascience.com/\" width=\"1200\" height=\"1000\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mon site web\n",
    "HTML('<iframe src=\"https://afouda-datascience.com/\" width=\"1200\" height=\"1000\"></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b21a97",
   "metadata": {},
   "source": [
    "A bientôt pour de nouveaux contenus édifiants en Data Science. \n",
    "\n",
    "[Josué AFOUDA](https://www.amazon.fr/Josu%C3%A9-AFOUDA/e/B08F17S1V8/ref=dp_byline_cont_pop_ebooks_1)\n",
    "\n",
    "- Mon site web : https://afouda-datascience.com/\n",
    "\n",
    "- Ma chaîne YouTube : https://www.youtube.com/channel/UCpd56FfjlkKbkHlbgY6XE3w\n"
   ]
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