Data_Analysis_Module_3_Exploratory_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Etapes préliminaires de l'analyse de données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objetcifs de cette analyse :    \n",
    "-Résumer les principales charactéristiques des données    \n",
    "-Avoir une meilleure compréhension du jeu de données   \n",
    "-Découvrir des relations entre les variables   \n",
    "-Extraire les variables importantes   \n",
    "\n",
    "**Déterminer les caractéristiques qui ont un le plus un impact sur le prix de la voiture**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "url = \"automobile.csv\"\n",
    "df=pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistiques descriptives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable numérique : Informations statistiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>0.834146</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>59.322565</td>\n",
       "      <td>1.245307</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   symboling  wheel-base      length       width      height  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean   102.000000    0.834146   98.756585  174.049268   65.907805   53.724878   \n",
       "std     59.322565    1.245307    6.021776   12.337289    2.145204    2.443522   \n",
       "min      0.000000   -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%     51.000000    0.000000   94.500000  166.300000   64.100000   52.000000   \n",
       "50%    102.000000    1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%    153.000000    2.000000  102.400000  183.100000   66.900000   55.500000   \n",
       "max    204.000000    3.000000  120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \n",
       "count   205.000000   205.000000         205.000000  205.000000   205.000000  \n",
       "mean   2555.565854   126.907317          10.142537   25.219512    30.751220  \n",
       "std     520.680204    41.642693           3.972040    6.542142     6.886443  \n",
       "min    1488.000000    61.000000           7.000000   13.000000    16.000000  \n",
       "25%    2145.000000    97.000000           8.600000   19.000000    25.000000  \n",
       "50%    2414.000000   120.000000           9.000000   24.000000    30.000000  \n",
       "75%    2935.000000   141.000000           9.400000   30.000000    34.000000  \n",
       "max    4066.000000   326.000000          23.000000   49.000000    54.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comptage des variables non numériques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fwd    120\n",
      "rwd     76\n",
      "4wd      9\n",
      "Name: drive-wheels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "drive_wheels_counts=df[\"drive-wheels\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation Target=f(Variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x=df['engine-size'][df[\"price\"]!='?']\n",
    "y=df[\"price\"][df[\"price\"]!='?'].astype(int)\n",
    "plt.scatter(x,y)\n",
    "plt.title('Scatterplot of Engine Size Vs Price')\n",
    "plt.xlabel('Engine Size')\n",
    "plt.ylabel('Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse par groupes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pré-traitement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=df[['drive-wheels','body-style','price']][df[\"price\"]!='?']\n",
    "df_test[\"price\"]=df_test[\"price\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Méthode groupby pour étudier l'influence combinée de deux variables sur le prix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4wd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>7603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>12647.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4wd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>fwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>11595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>fwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>8249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>8396.387755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9811.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>fwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>23949.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>rwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>24202.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>14337.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>21711.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drive-wheels   body-style         price\n",
       "0           4wd    hatchback   7603.000000\n",
       "1           4wd        sedan  12647.333333\n",
       "2           4wd        wagon   9095.750000\n",
       "3           fwd  convertible  11595.000000\n",
       "4           fwd      hardtop   8249.000000\n",
       "5           fwd    hatchback   8396.387755\n",
       "6           fwd        sedan   9811.800000\n",
       "7           fwd        wagon   9997.333333\n",
       "8           rwd  convertible  23949.600000\n",
       "9           rwd      hardtop  24202.714286\n",
       "10          rwd    hatchback  14337.777778\n",
       "11          rwd        sedan  21711.833333\n",
       "12          rwd        wagon  16994.222222"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grp=df_test.groupby(['drive-wheels','body-style'], as_index=False).mean()\n",
    "df_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pivot Table - Présentation plus agréable du résultat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4wd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>12647.333333</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fwd</td>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9811.800000</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>rwd</td>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14337.777778</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  NaN           NaN   7603.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9811.800000   \n",
       "rwd              23949.6  24202.714286  14337.777778  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = df_grp.pivot(index='drive-wheels',columns='body-style')\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heatmap - Représentation visuelle (graduation des axes imparfaites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.pcolor(df_pivot, cmap='RdBu')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse de variance (ANOVA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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