Histogramme.ipynb

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    "id": "WmbE4DmsqqRS"
   },
   "source": [
    "# <center>**COMMENT BIEN DECRIRE UN HISTOGRAMME ?**\n",
    "\n",
    "<center>Par Josué AFOUDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uFP92ljElOL4"
   },
   "source": [
    "Les histogrammes sont utilisés pour visualiser la distribution des variables quantitatives continues. \n",
    "\n",
    "Dans ce tutoriel, je vous montrerai comment :\n",
    "\n",
    "* 1- **calculer des paramètres statistiques pour décrire une variable** ;\n",
    "\n",
    "* 2- **construire un histogramme pour représenter la distribution d'une variable** ;\n",
    "\n",
    "* 3- **décrire la distribution d'une variable en se basant sur son histogramme**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "voy7zco-rOXG"
   },
   "source": [
    "# <font color=red> Importation des outils et des données"
   ]
  },
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   "source": [
    "# Importation des librairies\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 2,
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     "output_type": "execute_result"
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   ],
   "source": [
    "# Importation du jeu de données 'tips'\n",
    "\n",
    "tips_df = sns.load_dataset('tips')\n",
    "\n",
    "tips_df.head()"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   total_bill  244 non-null    float64 \n",
      " 1   tip         244 non-null    float64 \n",
      " 2   sex         244 non-null    category\n",
      " 3   smoker      244 non-null    category\n",
      " 4   day         244 non-null    category\n",
      " 5   time        244 non-null    category\n",
      " 6   size        244 non-null    int64   \n",
      "dtypes: category(4), float64(2), int64(1)\n",
      "memory usage: 7.3 KB\n"
     ]
    }
   ],
   "source": [
    "# Structure des données\n",
    "\n",
    "tips_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfLRJfKh7F4i"
   },
   "source": [
    "# <font color=red> Paramètres statistiques usuels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B1Qica-i7fCn"
   },
   "source": [
    "Il est très courant et simple de commencer une analyse des données en calculant des paramètres statistiques standard comme :\n",
    "\n",
    "* **Moyenne** ;\n",
    "\n",
    "* **Médiane** ;\n",
    "\n",
    "* **Minimum** ;\n",
    "\n",
    "* **Maximum** ;\n",
    "\n",
    "* **Mode** ;\n",
    "\n",
    "* **Etendue** ;\n",
    "\n",
    "* **Variance et Ecart-type**.\n",
    "\n",
    "Calculons ces paramètres au niveau de la variable *tip*."
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9982786885245902"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pourboie moyen \n",
    "\n",
    "tips_df['tip'].mean()"
   ]
  },
  {
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   "execution_count": 5,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pourboie médian\n",
    "\n",
    "tips_df['tip'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "# Pourboie minimum\n",
    "\n",
    "tips_df['tip'].min()"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "# Pourboie maximum\n",
    "\n",
    "tips_df['tip'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "# Etendue de la variable 'tip'\n",
    "\n",
    "tips_df['tip'].max() - tips_df['tip'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    {
     "data": {
      "text/plain": [
       "1.9144546380624725"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "# Variance de la variable 'tip'\n",
    "\n",
    "tips_df['tip'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1607013979453,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3836381890011826"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Ecart-type de la variable 'tip'\n",
    "\n",
    "tips_df['tip'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    {
     "data": {
      "text/plain": [
       "True"
      ]
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     "execution_count": 11,
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   ],
   "source": [
    "# Vérifions que l'écart-type est bel et bien la racine caréée de la variance\n",
    "\n",
    "tips_df['tip'].std() == np.sqrt(tips_df['tip'].var())"
   ]
  },
  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.785943</td>\n",
       "      <td>2.998279</td>\n",
       "      <td>2.569672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.902412</td>\n",
       "      <td>1.383638</td>\n",
       "      <td>0.951100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.347500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.795000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.127500</td>\n",
       "      <td>3.562500</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill         tip        size\n",
       "count  244.000000  244.000000  244.000000\n",
       "mean    19.785943    2.998279    2.569672\n",
       "std      8.902412    1.383638    0.951100\n",
       "min      3.070000    1.000000    1.000000\n",
       "25%     13.347500    2.000000    2.000000\n",
       "50%     17.795000    2.900000    2.000000\n",
       "75%     24.127500    3.562500    3.000000\n",
       "max     50.810000   10.000000    6.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "# Table de statistiques descriptives (une seule ligne de code)\n",
    "\n",
    "tips_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MSXOkUfnJ8ue"
   },
   "source": [
    "Vous pouvez aussi calculer la gamme interquartile (Interquartile Range ou IQR en Anglais). L'IQR est la différence entre le 3è quartile et le 1er quartile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2216,
     "status": "ok",
     "timestamp": 1607013979462,
     "user": {
      "displayName": "J.A DATATECH CONSULTING",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64",
      "userId": "06410945177748020218"
     },
     "user_tz": -60
    },
    "id": "L9Boay-NXtxy",
    "outputId": "595b5bb9-5a5e-41fa-c243-59dd118d7715"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.779999999999998"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IQR de la variable 'total_bill'\n",
    "\n",
    "tips_df['total_bill'].quantile(0.75) - tips_df['total_bill'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wr5_TcpHOH81"
   },
   "source": [
    "# <font color=red> Construction d'un histogramme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "executionInfo": {
     "elapsed": 2197,
     "status": "ok",
     "timestamp": 1607013979463,
     "user": {
      "displayName": "J.A DATATECH CONSULTING",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64",
      "userId": "06410945177748020218"
     },
     "user_tz": -60
    },
    "id": "pMw4MEBNOHYi",
    "outputId": "fdcde87f-da55-4438-88d4-b960d6392bc6"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogramme du montant total de la consommation\n",
    "\n",
    "sns.distplot(tips_df[\"total_bill\"], kde = False).set_title(\"Consommation totale\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Ub6XfD5QPL1"
   },
   "source": [
    "En ordonnée, on a la fréquence c'est-à-dire le nombre de fois d'occurence d'une valeur. En abscisse, on a les valeurs de la variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_68t49IURX51"
   },
   "source": [
    "Vous pouvez aussi choisir de représenter uniquement la densité de probabilité et non l'histogramme ou les deux ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "executionInfo": {
     "elapsed": 2409,
     "status": "ok",
     "timestamp": 1607013979699,
     "user": {
      "displayName": "J.A DATATECH CONSULTING",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64",
      "userId": "06410945177748020218"
     },
     "user_tz": -60
    },
    "id": "VOrogNgZRsR1",
    "outputId": "9d014969-6e26-41b2-8de6-e501c3875029"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Densité de probabilité du montant total de la consommation\n",
    "\n",
    "sns.distplot(tips_df[\"total_bill\"], kde = True, hist = False).set_title(\"Consommation totale\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "executionInfo": {
     "elapsed": 2796,
     "status": "ok",
     "timestamp": 1607013980096,
     "user": {
      "displayName": "J.A DATATECH CONSULTING",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64",
      "userId": "06410945177748020218"
     },
     "user_tz": -60
    },
    "id": "OTlttHyTRlv7",
    "outputId": "b91e62f1-43a6-42f2-db08-28a2b485788e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogramme et Densité de probabilité du montant total de la consommation\n",
    "\n",
    "sns.distplot(tips_df[\"total_bill\"], kde = True).set_title(\"Consommation totale\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wHjWcTf-SHe0"
   },
   "source": [
    "# <font color=red> Description d'un histogramme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pskJpbDWR518"
   },
   "source": [
    "Pour décrire un histogramme, vous devez faire ressortir nécessairement 04 aspects importants : \n",
    "\n",
    "* **forme** de l'histogramme : c'est l'apparence globale de l'histogramme. Vous devez dire si elle est symétrique, en forme de cloche (loi normale), asymétrique positive (c'est-à-dire forme étalée vers la droite) ou asymétre négative (c'est-à-dire forme étalée vers la gauche.) ;\n",
    "\n",
    "* **centre** de la distribution : médiane ;\n",
    "\n",
    "* **dispersion** des valeurs : à ce niveau, vous devez interpréter la variabilité dans les données en utilisant la variance, l'écart-type ou l'IQR ;\n",
    "\n",
    "* **outliers** (en Anglais) ou valeurs abérantes : ce sont des valeurs dui sortent du lot c'est-à-dire qui sont très loin du centre de gravité des données."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v0VNNqH8TzO7"
   },
   "source": [
    "Avant de décrire la distribution de la consommation totale, faisons un petit rappel sur la loi normale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "executionInfo": {
     "elapsed": 3052,
     "status": "ok",
     "timestamp": 1607013980361,
     "user": {
      "displayName": "J.A DATATECH CONSULTING",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64",
      "userId": "06410945177748020218"
     },
     "user_tz": -60
    },
    "id": "VkktDxeNTwee",
    "outputId": "29c8184b-90da-4a25-9b7a-211607034e9f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exemple de distribution normale\n",
    "\n",
    "sns.distplot(np.random.normal(size=10000), kde=True).set_title(\"Distribution normale\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qjZNi83caiPh"
   },
   "source": [
    "Quelques caractéristiques d'une distribution normale :\n",
    "\n",
    "*  **forme en cloche** ;\n",
    "\n",
    "* **distribution symétrique (donc le coefficient d'asymétrie est égal à 0)** ;\n",
    "\n",
    "* **La moyenne est égale à la médiane** ;\n",
    "\n",
    "* Etc.\n",
    "\n",
    "Voici une description de la distribution de la consommation totale dans le restaurant :\n",
    "\n",
    "**La distribution de la consommation totale dans ce restaurant est unimodale et asymétrique vers la droite centrée à environ 15 u.m (unité de monnaie) avec la plupart des données entre 2 et 50 et apparemment aucune valeur aberrante**.\n",
    "\n",
    "Lorsque vous combinez les statistiques descriptives et l'histogramme, vous pouvez décrire de manière plus précise la distribution du montant total de la consommation dans ce restaurant :\n",
    "\n",
    "**La distribution du montant total de la consommation dans ce restaurant est unimodale et asymétrique vers la droite , centrée à 17,795 u.m avec la plupart des valeurs entre 13,35 u.m (1er quartile) et 24,13 u.m (3è quartile) et apparemment aucune valeur aberrante**.\n",
    "\n",
    "Remarquez l'utilisation du 1er quartile et du 3è quartile plutôt que de la simple étendue (minimum et maximum). En effet, en face d'une distribution asymétrique, il est conseillé d'utilisé l'IQR dans la description plutôt que d'utiliser l'étendue. Par ailleurs, l'IQR est plus robuste aux valeurs abérantes que l'étendue. La médiane est aussi plus robuste aux valeurs abérantes que la moyenne."
   ]
  }
 ],
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  "colab": {
   "authorship_tag": "ABX9TyOFqfFNNxvoGNnRvmrDw5WY",
   "collapsed_sections": [],
   "name": "2_Analyse univariée.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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