matrice de correlation avec Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "949327f2",
   "metadata": {},
   "source": [
    "# <center> **Comment construire une matrice de corrélation avec Python ?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cd54949",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "230c3371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sns.load_dataset('iris')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "217a99f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecfdc921",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(iris.corr()).set_title('Carte de corrélations de Pearson');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c5645b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(6,6))\n",
    "mask = np.triu(np.ones_like(iris.corr(), dtype=bool))\n",
    "sns.heatmap(iris.corr(), mask=mask, center=0, cmap='RdBu', \n",
    "            linewidths=1, annot=True, fmt=\".2f\", vmin=-1, vmax=1)\n",
    "plt.title('Carte des corrélations', fontsize = 15, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "093b2725",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation_matrix(df):\n",
    "    mask = np.triu(np.ones_like(df.corr(), dtype=bool))\n",
    "    sns.heatmap(df.corr(), mask=mask, center=0, cmap='RdBu', \n",
    "                linewidths=1, annot=True, fmt=\".2f\", vmin=-1, vmax=1)\n",
    "    plt.title('Carte des corrélations', fontsize = 15, fontweight='bold')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f034b906",
   "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": [
    "correlation_matrix(iris)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}