Les_graphiques.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Les graphiques",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPmerrrwipXA4OnubAlOKii",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GeoLabUniLaSalle/Python/blob/main/Les_graphiques.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAClmti3eNXe"
      },
      "source": [
        "# **Les graphiques**\n",
        "\n",
        "Dans ce chapitre, nous allons voir comment tracer des graphiques en Python.\n",
        "\n",
        "Débutons par importer le module nécessaire à la production de graphiques."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZqnZQ5reLgR"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9LMYN4AWh89"
      },
      "source": [
        "En cas d'erreur sur cette ligne, lancez l'installation du module matplotlib, redémarrez le noyau, puis re-exécutez l'import du module."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pNJVy4UWqz3",
        "outputId": "fffb35bf-efc3-4e5f-da55-1b2d0455a8ac"
      },
      "source": [
        "import sys  \n",
        "!{sys.executable} -m pip install --user matplotlib"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.10.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.19.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.4.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wKrNMvLren10"
      },
      "source": [
        "Prenons un jeu de données stocké dans une liste, et traçons son contenu."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "d1XDNFYZenXR",
        "outputId": "392591df-37e2-406f-dab4-14caaf453383"
      },
      "source": [
        "l = [7,1,-1,5]\n",
        "plt.plot(l)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f85ffcfda10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXwU9f0G8OeTkHCEO4QzhBBuhCRoDChnoigKAsohtLb1KvanWBEErFcV6wEKeNQLWxSrrXIqKIcoAQQEDZiEIxzhDiAJV0gIBJJ8fn/sBiMGWHZnd2Z2n/frlVdzLN/5DGMfhmF2HlFVEBGRfQWZPQAREXmGQU5EZHMMciIim2OQExHZHIOciMjmKpmx0Xr16ml0dLQZmyYisq3169cfUdWIC79vSpBHR0cjNTXVjE0TEdmWiOyt6Pu8tEJEZHMMciIim2OQExHZHIOciMjmGORERDbncZCLSBsRSSv3cVJERhkxHBERXZ7Htx+q6jYA8QAgIsEADgCY5+m6RETkGqMvrdwAYKeqVnivo6fW7z2Gt5dneWNpIiLbMjrIhwH4X0U/EJERIpIqIqm5ubluLb4g/RAmLd6G1VlHPJmRiMivGBbkIhIKoD+AWRX9XFWnqWqCqiZERPzmHaYuGd+nLWLqhWHsrHScPHPOg2mJiPyHkWfktwDYoKqHDVzzV6qGBmPy0Dj8fPIMJizY4q3NEBHZipFBPhwXuaxipE5RdfBgr5aYvT4bX2/+2dubIyKyPEOCXETCAPQGMNeI9S7nrze0QvtGNfHEvI04WlDki00SEVmWIUGuqqdUNVxV84xY73JCKwVhyp1xOHm6GE/O2wQWSBNRILPtOzvbNqyJ0Te1xuLNP+PztANmj0NEZBrbBjkA/Ll7DBKa1cEzX2zGobzTZo9DRGQKWwd5cJDg1SFxKC5RjJudwUssRBSQbB3kABBdLwxP9G2H73Ycwcfr9pk9DhGRz9k+yAHgrs5R6N6qHl78KhN7jpwyexwiIp/yiyAXEUwaHIuQYMGYWekoKeUlFiIKHH4R5ADQqFZVTBjQAev3Hse0lbvMHoeIyGf8JsgBYEB8Y9zSoSGmLt2OrT+fNHscIiKf8KsgFxH8Y2AH1KxaCY9+lo6zxaVmj0RE5HV+FeQAEF69Ml66IxaZh07i9W+3mz0OEZHX+V2QA0Dv9g0w+JpIvLN8JzbsO272OEREXuWXQQ4Az9zWHo1qVcVjM9Nx+myJ2eMQEXmN3wZ5zSoheGVILHYdOYWJi7eaPQ4Rkdf4bZADwPUt6uHu66Px4Zo9rIcjIr/l10EOsB6OiPyf3wd5+Xq45+azHo6I/I9RDUG1RWS2iGwVkUwRuc6IdY3SKaoOHkpqiTkbWA9HRP7HqDPy1wEsVtW2AOIAZBq0rmEeTm6FqxqzHo6I/I/HQS4itQD0APBvAFDVs6p6wtN1jRZaKQhThsazHo6I/I4RZ+TNAeQC+EBEfhKRfznLmH9FREaISKqIpObm5hqw2SvXpmEN1sMRkd8xIsgrAbgawDuq2gnAKQCPX/giVZ2mqgmqmhAREWHAZt1Tvh7u4AnWwxGR/RkR5NkAslV1nfPr2XAEuyUFBwkmD41DSali/BzWwxGR/Xkc5Kr6M4D9ItLG+a0bAFj6Pr9m4WF44lZnPdzavWaPQ0TkEaPuWnkYwCcikgEgHsCLBq3rNb/vHIUerSPw4sKtrIcjIlszJMhVNc15/TtWVQeqquUfOSgimDTIUQ83emYa6+GIyLb8/p2dl9KwVhVMGNABG/adwHsrd5o9DhGRWwI6yIFf18NlHmI9HBHZT8AHeVk9XK2qoRg9k/VwRGQ/AR/kgKMe7uU7OrIejohsiUHudGP7BhjCejgisiEGeTll9XBjWA9HRDbCIC+nhrMebveRU3h5keUe4EhEVCEG+QWub1EP93SNxozv97IejohsgUFegfF92iImgvVwRGQPDPIKVAkJxpSh8TicX8R6OCKyPAb5RcQ3rY0He7XAnA3ZWMJ6OCKyMAb5JZyvh5u7EUdYD0dEFsUgv4Syerj8M8V4ct5GPruciCyJQX4ZbRrWwJibWmPJ5sOY9xPr4YjIehjkLri/ewyuja6Dv89nPRwRWQ+D3AXBQYJXhzjq4cbNzkApn11ORBZiSJCLyB4R2SgiaSKSasSaVlNWD7cq6wg+Xsd6OCKyDiPPyJNUNV5VEwxc01J+qYfLxG7WwxHRFcrKKfDKury0cgXK6uFCg4MwhvVwRHQFVm7PRe+pK7Bo4yHD1zYqyBXA1yKyXkRGVPQCERkhIqkikpqbm2vQZn2vYa0qeH4g6+GIyHV5hecwbnYGWkRUR1Lb+oavb1SQd1PVqwHcAuAhEelx4QtUdZqzoDkhIiLCoM2ao39cY9zakfVwROSaZxdsxpGCIkwdGo8qIcGGr29IkKvqAef/5gCYByDRiHWtylEP1xG1qobi0c/SUFTMZ5cTUcUWbTyEeT8dwMjklugYWcsr2/A4yEUkTERqlH0O4CYAmzxd1+rqhoXi5Ts6YuvP+Xj9mx1mj0NEFpSbX4Qn5m1Exya18FBSS69tx4gz8gYAVolIOoAfAHylqosNWNfybmzfAEMTIvHuip1Yv5f1cET0C1XF3+ZuxKmzJZgyNA4hwd67t8TjlVV1l6rGOT+uUtUXjBjMLp7u56iHe2xWOgrPFps9DhFZxOz12fgm8zDG3dwGrRrU8Oq2ePuhh8rXw01ctNXscYjIAg6cOI0JC7agc/O6uLdrc69vj0FugPL1cKt2sB6OKJCVlirGzkpHqSpeHRKHoCDx+jYZ5AY5Xw83Ox15p1kPRxSoZny/B2t2HsXT/dqjad1qPtkmg9wgZfVwOflFeG7BZrPHISIT7MwtwMuLtiK5bX3ceW1Tn22XQW6g+Ka18VCvFpi74QDr4YgCTHFJKUbPTEfV0GC8fEdHiHj/kkoZBrnBRrIejiggvbtiJ9L3n8DzAzqgfs0qPt02g9xgrIcjCjybDuThtW924La4xrgtrrHPt88g9wLWwxEFjqLiEoyZmY66YaF4fsBVpszAIPeS8/VwX7AejsifTVm6HdsO52PioFjUrhZqygwMci85Xw+nrIcj8lepe45h2spdGJ7Y1CuPp3UVg9yLmoWH4cm+rIcj8keniooxZlY6IutUxZN925s6C4Pcy36XGIWerIcj8jsvLcrEvmOFeHVwHKpXrmTqLAxyLxMRTCxXD1dcUmr2SETkoRXbc/Hx2n24v1tzdI4JN3scBrkv/LoebpfZ4xCRBxy1beloVb86xtzUxuxxADDIfaZ/XGP07dgIr32zHVsOsh6OyK7+Pn8TjhacxRQv1ba5w7AgF5FgEflJRL40ak1/IiJ4fmAH1KoaitEzWQ9HZEeLNh7C52kHvVrb5g4jz8gfAZBp4Hp+p25YKCYOYj0ckR2V1bbFRnq3ts0dhgS5iEQC6AvgX0as589uaMd6OCK7cdS2Zfikts0dRk3zGoBxAC56S4aIjBCRVBFJzc3NNWiz9lRWDzdmZhrr4YhsYNb6bHyTmYNxN7dBy/rerW1zh8dBLiL9AOSo6vpLvU5Vp6lqgqomREREeLpZW6tRJQSvDonDnqOFeJn1cESWln280Ke1be4w4oy8K4D+IrIHwKcAkkXkYwPW9WvXtQjHvV2b4yPWwxFZlqO2LQPqw9o2d3gc5Kr6N1WNVNVoAMMALFPVuzyeLACM69MGLVgPR2RZM77fg+93+ba2zR3WumIfYFgPR2RdWTnm1La5w9AgV9XlqtrPyDX9XVy5erjFm1gPR2QFxSWlGDPLnNo2d/CM3ALK6uGenMd6OCIreGe5o7btHwN9X9vmDga5BYRWCsLUO+ORX1SMJ+ayHo7ITJsO5OH1bx21bf1ifV/b5g4GuUW0blADj93UGl9vOYy5G1gPR2QGK9S2uYNBbiH3dYtBYnRdPDuf9XBEZjhf2zbYvNo2dzDILaR8PdzY2emshyPyoR/P17ZFIamNebVt7mCQW0xUeDU82bcdVmcdxX/Wsh6OyBdOFRVjzMyy2rZ2Zo9zxRjkFlRWD/fSokzsyi0wexwiv/fiwkzsP16IyUPiTa9tcweD3IJEBJMGx6JypWCMmZXOejgiL1qxPRefrHPUtiU2r2v2OG5hkFtUg5pVMGHAVfiJ9XBEXmPF2jZ3MMgtjPVwRN5VVts29U7r1La5g0FuYayHI/Kehc7atoeTW6FDE+vUtrmDQW5x5evhXmM9HJEhcvLP4ElnbduDSS3MHsdjDHIbuKFdA9yZ0BTvrdiJ9XuPmT0Oka2pKp6Yu9GytW3usP8eBIin+rVz1sOlsx6OyANWr21zB4PcJlgPR+Q5O9S2uYNBbiPl6+G+2xHYBdZEV6q0VPHYrHQAsHRtmzuMKF+uIiI/iEi6iGwWkeeMGIwqdr4eblYG6+GIrsCHa/Zg7a5jeLpfO0vXtrnDiDPyIgDJqhoHIB5AHxHpYsC6VIGyerjcgiI8N5/1cESuyMopwMTFjtq2oQnWrm1zhxHly6qqZQ8ECXF+8LF9XhTXtDYeSmqJuT+xHo7ocuxW2+YOQ66Ri0iwiKQByAGwVFXXVfCaESKSKiKpubm8vuuph5NbokMT1sMRXU5ZbdsLAzvaorbNHYYEuaqWqGo8gEgAiSLSoYLXTFPVBFVNiIiIMGKzAS0kOAhThjrq4f7GejiiCpXVtvWPa4y+sY3MHsdrDL1rRVVPAEgB0MfIdaliZfVwS7ccxhzWwxH9yplzJRg9Mw11w0IxwUa1be4w4q6VCBGp7fy8KoDeAHijs4+U1cM9N38zDrAejui8qUu3Y/vhAtvVtrnDiDPyRgBSRCQDwI9wXCP/0oB1yQXl6+HGsR6OCICztu07e9a2ucOIu1YyVLWTqsaqagdVnWDEYOS6qPBqeKpve9bDEeGX2ramdarhKRvWtrmD7+z0E8MTm6JXG9bDEb3grG17dUgcwmxY2+YOBrmfEBFMHMR6OApsy7fl4L/r9uHP3WNsW9vmDga5H2lQswqeH9iB9XAUkPIKz2H8nAy0blAdo3u3Nnscn2KQ+5my+2VZD0eB5hlnbduUofaubXMHg9wP/WNAB9Suxno4ChwLNx7CF35S2+YOBrkfqsN6OAogZbVtcX5S2+YOBrmfSm7Lejjyf6qKv83ZiMKzJZg8NN4vatvcEZh7HSDK6uFGsx6O/NSs1Gx8uzUH4/q0Rcv61c0exzQMcj9Wo0oIJg+Nw75jhXhpIZ+aQP5l/7FCTPhyC7rE1MU910ebPY6pGOR+rkuMox7uP2tZD0f+o7RUMXa2o7btlcH+VdvmDgZ5ABh7cxu0rF+d9XDkN8pq257p197vatvcwSAPAI56uDjWw5FfKKttu6FtfQxJiDR7HEtgkAeI2Mjy9XCHzB6HyC3FJaUYMzMN1UKD8dIg/6xtcweDPICU1cM9MW8TcvNZD0f28/bynUjPzsM/BnZE/Rr+WdvmDgZ5AAkJDsLUofEoKCrGE/NYD0f2sulAHt4IgNo2dzDIA0yrBjUw9qY2rIcjWymrbQuv7v+1be4wouqtqYikiMgWEdksIo8YMRh5z73dmrMejmxlSllt2yD/r21zhxFn5MUAxqhqewBdADwkIu0NWJe8hPVwZCc/7D6G97/bhd91jkKvAKhtc4cRVW+HVHWD8/N8AJkAmni6LnlXVHg1PN3PUQ/30fd7zB6HqEKniorx2CxHbduTtwZGbZs7DL1GLiLRADoBWFfBz0aISKqIpObm8h2GVjDsWkc93MuLt7IejiwpEGvb3GFYkItIdQBzAIxS1d80GqjqNFVNUNWEiIgIozZLHihfDzd6JuvhyFpSArS2zR2GBLmIhMAR4p+o6lwj1iTfKKuHS9vPejiyjhOFZzF+dmDWtrnDiLtWBMC/AWSq6hTPRyJfK18Pt/lgntnjEOGZLzbj2KnArG1zhxFn5F0B/AFAsoikOT9uNWBd8qHz9XCfpbMejkz1VcYhzE8/iL/eEJi1be4w4q6VVaoqqhqrqvHOj4VGDEe+U1YPt+1wPqYuZT0cmSMn/wye+txZ29YrMGvb3MF3dtJ5yW0bYNi1TTFtJevhyPcurG2rFKC1be7g7xT9ylP92qNxbdbDke+V1baND/DaNncwyOlXqleuhFeHsB6OfGv/sUI8t2AzrosJx90BXtvmDgY5/Ub5eriV2/nmLfKu0lLFY7PSISJ4ZUhswNe2uYNBThUqq4cbNzsDeYWshyPv+WDNHqzb7ahti6zD2jZ3MMipQuXr4Z5dwHo48o6snAJMYm2bxxjkdFGxkbUxMqkl5rEejrzgXEkpRrO2zRAMcrqkkckt0bFJLdbDkeHeTtmJjOw8vHA7a9s8xSCnSwoJDsKUoXGshyNDbczOw5vLdmBAfGPc2pG1bZ5ikNNltWpQA+NudtTDzV6fbfY4ZHO/qm3r38HscfwCg5xccm/X5khsXhcTFmxhPRx5ZMrS7diR46htq1UtxOxx/AKDnFwSFCSYPCQOpaoYO4v1cOQe1rZ5B4OcXNa0bjU81a891uxkPRxduYKiYoyZlcbaNi9gkNMVYT0cueuFrzKRffw0Jg9lbZvRGOR0RVgPR+5I2ZaD//2wDyO6x+DaaNa2Gc2oqrfpIpIjIpuMWI+srUHNKviHsx7u3RU7zR6HLK58bdujrG3zCqPOyD8E0MegtcgGbotrjH6xjfD6tztYD0eXxNo27zMkyFV1JQA2EQSY58vVw505x3o4+q0vMw5ifvpBPMLaNq/y2TVyERkhIqkikpqby0ej+oM6YaGYNCgW2w7nI+nV5fhw9W4GOp2Xc/IMnvp8E+Ka1sb/sbbNq3wW5Ko6TVUTVDUhIiLCV5slL0tqWx+f3N8ZkXWq4tkFW9B9UgreX7kLp4rYLhTIVBWPz92I02dLMHlIHGvbvIy/u+Sxri3rYdZfrsenI7qgdYPqeGFhJrpNXIa3UrKQf4bPMg9EM1P3Yxlr23yGN3OSYbrEhKNLTDjW7z2Ofy7bgVeWbMN7K3binq7NcU/XaNSuFmr2iOQD+48VYsKCLaxt8yGjbj/8H4DvAbQRkWwRuc+IdcmermlWBx/ck4gFI7uhS0w4Xv92B7pNTMHExVtxpICPwvVnrG0zhyFn5Ko63Ih1yL90jKyFaX9MQOahk3grJQvvrtiJD1bvxu87N8MDPWJQvyafQe1vpq/ejXW7j2HS4FjWtvmQmPF86YSEBE1NTfX5dslcWTkFeHt5Fr5IO4jgIMGwa5vigZ4t0KR2VbNHIwNk5eTj1jdWoUerenj/jwls/PECEVmvqgm/+T6DnHxt79FTeGf5TszZ4Hi2+aCrI/Fgr5aICucZnF2dKynFoHfWYP+xQix5tAcbf7zkYkHOu1bI55qFh+HlQbFYPjYJwxOjMPenA0iavByjZ6YhK4cP4rIj1raZi2fkZLrDJ89g2spd+GTdXhQVl6Jvx0YYmdwSbRvWNHs0csHG7Dzc/vZq9I1thNeHdTJ7HL/GSytkeUcKivDvVbvx0Zo9OHW2BDe1b4CHk1uhYyTf2m1VZ86V4LY3V+HkmXP4elRPNv542cWCnPeRk2XUq14Z4/u0xQM9YvDB6j34YPVufL3lMJLaRGBkcitc06yO2SPSBSZ/vQ07cgow495EhriJeI2cLKd2tVA82rs1Vj2ejLE3t0Ha/hMY9M4a/P5fa7F211GzxyOndbuO4l+rduP3naPQszUfu2EmXlohyztVVIz/rtuH91buwpGCIiRG18XI5Jbo3qoeb3EzSUFRMW55fSUEgkWPdGfjj4/wrhWyrbDKlfDnHjFYNT4Jz97WHvuOFeKP03/AwLfX4NvMwzDjZCTQsbbNWhjkZBtVQoJxd9fmWDGuF168vSOOFhThvhmp6PvGKizaeAilpQx0X0jZ6qxt68HaNqvgpRWyrXMlpfgi7SDeSsnC7iOn0Kp+dYxMbol+sY0RzGd8eMWJwrO4aepK1KkWivkPd0XlSmz88SVeWiG/ExIchMHXROKb0T3x+rB4iACPfJqGG6eswKzU/TjHYmjDPe2sbZs8NI4hbiEMcrK94CDBgPgmWPxID7x719WoFhqMsbMzkPTqcuebjNhaZIQvMw5iAWvbLImXVsjvqCpStuXgjW+zkLb/BBrWrIIHesZgeGIUy3/dlHPyDG56bSWahYdhzl+uY+OPSXhphQKGiCC5bQPMe/B6/Oe+RETVrYbnFmxBt4kpmLZyJ2vorpCqYvycDJw+W4IpQ1nbZkU8IuS3RATdW0Vg5l+uw2cjuqBtwxp4ceFWdJu4DP9ctgMnWUPnks9+3I+Ubbl4/Ja2aBHB2jYrMqohqI+IbBORLBF53Ig1iYzUOSYcH9/fGXP+73p0iqqDV7/ejm4vL8OUpdtxovCs2eNZ1v5jhXj+S0dt25+uizZ7HLoIj6+Ri0gwgO0AegPIBvAjgOGquuViv4bXyMlsmw7k4c1lO7Bk82GEhQbjD9dF4/7uzVGvemWzR7OM0lLF8PfXYvPBk1g8qjsbfyzAm9fIEwFkqeouVT0L4FMAAwxYl8hrOjSphff+kIDFo7ojuV0DvLdyJ7pNXIYJC7bg8MkzZo9nCWW1bc/c1p4hbnFGBHkTAPvLfZ3t/N6viMgIEUkVkdTc3FwDNkvkubYNa+LN4Z3wzeie6NuxMWZ8vwfdJ6Xg6c83Ift4odnjmWbH4XxMWrINN7arjyHXRJo9Dl2Gz/6xU1WnqWqCqiZERPBJaWQtLSKqY/LQOKSM6YVBVzfBpz/uQ69XlmP87AzsPXrK7PF86lxJKUbPTEf1ypXw0h2xfDCZDRgR5AcANC33daTze0S2ExVeDS/d4aih+33nKMxLO4DkySsw+rPAqaF7KyULGw/k4YWBHRBRg/9mYAdGBPmPAFqJSHMRCQUwDMB8A9YlMk2T2lXx3IAOWDUuCfdcH41Fm35G76kr8NB/NyDz0Emzx/Oajdl5+OeyLAyMb4xbOjYyexxykSHv7BSRWwG8BiAYwHRVfeFSr+ddK2Q3R8tq6L7fi4KiYvRu3wB/9bMaurLatvwzxVgyqgcbfyyInZ1EBjhReBYfrtmD6at24+SZYvRqE4GHk1vimmb2f5zrC19twfvf7caMexPZ+GNRfIs+kQFqVwvFqBtbY7Wzhi4jOw+D3vkev3t/Lb7fedS2JRdrnbVtd3VhbZsd8YycyAOFZ3+pocvNL8K10XUwMrkVetiohq6gqBh9XluJ4CBHbVu1UDb+WBXPyIm8oFpoJdzfPQbfjUvCc/2vQvbx0/jT9B8w8K3V+GaLPWroXvhqCw6cOI3JQ+IY4jbFICcyQJWQYPzp+misGJuEl+7oiGOFZ3H/R6m49Y1VWGjhGjpHbdt+jOgRgwTWttkWL60QeUFZDd3bKVnYdeQUWtavjpFJLdEvtpFlHgN7/NRZ3Pwaa9vshJdWiHyorIZu6eieeGN4JwQJMOozRw3dTIvU0D39xSYcLzyLKXeyts3uGOREXhQcJOgf19hZQ3cNwipXwrjZGej1ynJ8vNa8GroF6QfxZcYhPHJDK1zV2H/uhQ9UvLRC5EMX1tA1qFkZD/RogeGJUaga6puz4rLatujwMMxmbZut8NIKkQWUr6H7+L7OaBYehglfbkH3Scvw3grv19CV1badOVeCyaxt8xs8ikQmEBF0a1UPMx9w1NC1a1QTLy3aiq4Tl+HNb71XQ1dW2za+D2vb/AkvrRBZxIZ9x/HWsix8uzUHNapUwj3XR+Oers1RJyzUkPX3HytEn9dWIjayNj65vzOCguzxhiX6BZ+1QmQTmw44nkC4ePPPCAsNxl3XNcOfu8d4VENXWqoY9v5aZB48icWP9kCT2lUNnJh85WJBzrdxEVlMhya18O4frsG2n/PxVkoW3l+5CzPW7MHwxCg80KMFGtaqcsVrTl+9Gz/sPoZXBscyxP0Qz8iJLG5XbgHeXr4T8346gGARDL02En/p2cLlHs0dh/PR981V6NEqAu//8RrbPAOGfouXVohsbv+xQry9fCdmr98PVeCOq5vgwV4tEV0v7KK/5lxJKe54ew0OnDiNJaN6sPHH5nj7IZHNNa1bDS/d0RErxibhri7N8EXaQSRPXo5HP0tDVk5+hb+GtW2BwaMgF5EhIrJZREpF5Dd/ShCR8RrXropn+1+F78Yn4b5uzbF408/oPXUlHvpkA7Yc/KWGLiP7BN5cloXbOzVhbZuf8+jSioi0A1AK4D0Aj6mqS9dLeGmFyDhHC4owffVuzFjjqKG7sV0DPNAzBn+buxEFZ4qx5NEeqFWVtW3+wCt3rahqpnNxT5YhIg+EV6+MsTe3xYjuLfDBmt2Yvmo3vsk8DAD46N5EhngA8NnthyIyAsAIAIiKivLVZokCRq1qIRh1Y2vc1605/rtuH6qGBqMHa9sCwmWDXES+AdCwgh89qapfuLohVZ0GYBrguLTi8oREdEVqVAnBAz1bmD0G+dBlg1xVb/TFIERE5B7efkhEZHOe3n54u4hkA7gOwFcissSYsYiIyFWe3rUyD8A8g2YhIiI38NIKEZHNMciJiGyOQU5EZHMMciIimzPlMbYikgtgr5u/vB6AIwaOYybui/X4y34A3Ber8mRfmqnqb96ua0qQe0JEUit6aIwdcV+sx1/2A+C+WJU39oWXVoiIbI5BTkRkc3YM8mlmD2Ag7ov1+Mt+ANwXqzJ8X2x3jZyIiH7NjmfkRERUDoOciMjmLBvkItJHRLaJSJaIPF7BzyuLyGfOn68TkWjfT+kaF/blbhHJFZE058f9Zsx5OSIyXURyRGTTRX4uIvKGcz8zRORqX8/oChf2o5eI5JU7Hs/4ekZXiUhTEUkRkS3OIvRHKniNXY6LK/ti+WMjIlVE5AcRSXfux3MVvMbY/FJVy30ACAawE0AMgFAA6QDaX/CaBwG86/x8GIDPzJ7bg325G8A/zZ7VhX3pAeBqAJsu8vNbASwCIAC6AFhn9sxu7kcvAF+aPaeL+9IIwNXOz2sA2F7Bf192OS6u7Ivlj43z97m68/MQAOsAdLngNYbml1XPyBMBZKnqLlU9C+BTAAMueM0AADOcn88GcINYswXalX2xBVVdCeDYJV4yAMBH6rAWQG0RaeSb6Vznwnw4FeUAAAJsSURBVH7YhqoeUtUNzs/zAWQCaHLBy+xyXFzZF8tz/j4XOL8McX5ceFeJofll1SBvAmB/ua+z8dsDev41qloMIA9AuE+muzKu7AsADHL+tXe2iDT1zWiGc3Vf7eA651+NF4nIVWYP4wrnX887wXEGWJ7tjssl9gWwwbERkWARSQOQA2Cpql70mBiRX1YN8kCzAEC0qsYCWIpf/qQmc2yA45kWcQDeBPC5yfNclohUBzAHwChVPWn2PJ64zL7Y4tioaomqxgOIBJAoIh28uT2rBvkBAOXPSiOd36vwNSJSCUAtAEd9Mt2Vuey+qOpRVS1yfvkvANf4aDajuXLcLE9VT5b91VhVFwIIEZF6Jo91USISAkfwfaKqcyt4iW2Oy+X2xW7HRlVPAEgB0OeCHxmaX1YN8h8BtBKR5iISCsc/Bsy/4DXzAfzJ+flgAMvU+S8HFnPZfbngemV/OK4N2tF8AH903iXRBUCeqh4ye6grJSINy65XikgiHP8/seJJApxz/htApqpOucjLbHFcXNkXOxwbEYkQkdrOz6sC6A1g6wUvMzS/POrs9BZVLRaRkQCWwHHXx3RV3SwiEwCkqup8OA74f0QkC45/uBpm3sQX5+K+/FVE+gMohmNf7jZt4EsQkf/BcddAPXGUbv8djn/Igaq+C2AhHHdIZAEoBHCPOZNemgv7MRjA/4lIMYDTAIZZ9CQBALoC+AOAjc5rsgDwBIAowF7HBa7tix2OTSMAM0QkGI4/aGaq6pfezC++RZ+IyOasemmFiIhcxCAnIrI5BjkRkc0xyImIbI5BTkRkcwxyIiKbY5ATEdnc/wOEW/HFUh21YwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEN2WBiefInQ"
      },
      "source": [
        "Il est possible de tracer le contenu de plusieurs listes dans une même figure."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BLhNUfUqfAfM",
        "outputId": "bc6b1907-7d62-412c-8903-13e612b3846d"
      },
      "source": [
        "m = [2, 9, 3, 1]\n",
        "plt.plot(l)\n",
        "plt.plot(m)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f85fd9f3210>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKBiviApfWsE"
      },
      "source": [
        "Ces deux listes peuvent être tracées dans deux figures distinctes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "WqPo3XCqfT_p",
        "outputId": "8813e1d3-854a-4ac9-fc58-92bf780dab62"
      },
      "source": [
        "plt.plot(l)\n",
        "plt.figure()\n",
        "plt.plot(m)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f85fd482250>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FoFEaRh_fkg_"
      },
      "source": [
        "Il est possible d'enrichir nos figures d'informations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "N_6RFRgdfd5m",
        "outputId": "dc1f78bc-3cf0-4e07-8f31-313164b274d7"
      },
      "source": [
        "plt.plot(l, 'r--', label='liste l') # r = red, -- = pointillés\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('l')\n",
        "plt.title('Exemple titre')\n",
        "plt.legend(loc='lower right')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f85fd9d3310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZ9Zey7qf-ir"
      },
      "source": [
        "Consultez ici l'ensemble des paramétrages possibles."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hus1T4Imf00B",
        "outputId": "a62391fc-9e7d-49a3-b254-c1fd9492f762"
      },
      "source": [
        "help(plt.plot)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on function plot in module matplotlib.pyplot:\n",
            "\n",
            "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
            "    Plot y versus x as lines and/or markers.\n",
            "    \n",
            "    Call signatures::\n",
            "    \n",
            "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
            "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
            "    \n",
            "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
            "    \n",
            "    The optional parameter *fmt* is a convenient way for defining basic\n",
            "    formatting like color, marker and linestyle. It's a shortcut string\n",
            "    notation described in the *Notes* section below.\n",
            "    \n",
            "    >>> plot(x, y)        # plot x and y using default line style and color\n",
            "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
            "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
            "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
            "    \n",
            "    You can use `.Line2D` properties as keyword arguments for more\n",
            "    control on the appearance. Line properties and *fmt* can be mixed.\n",
            "    The following two calls yield identical results:\n",
            "    \n",
            "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
            "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
            "    ...      linewidth=2, markersize=12)\n",
            "    \n",
            "    When conflicting with *fmt*, keyword arguments take precedence.\n",
            "    \n",
            "    \n",
            "    **Plotting labelled data**\n",
            "    \n",
            "    There's a convenient way for plotting objects with labelled data (i.e.\n",
            "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
            "    the data in *x* and *y*, you can provide the object in the *data*\n",
            "    parameter and just give the labels for *x* and *y*::\n",
            "    \n",
            "    >>> plot('xlabel', 'ylabel', data=obj)\n",
            "    \n",
            "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
            "    `pandas.DataFame` or a structured numpy array.\n",
            "    \n",
            "    \n",
            "    **Plotting multiple sets of data**\n",
            "    \n",
            "    There are various ways to plot multiple sets of data.\n",
            "    \n",
            "    - The most straight forward way is just to call `plot` multiple times.\n",
            "      Example:\n",
            "    \n",
            "      >>> plot(x1, y1, 'bo')\n",
            "      >>> plot(x2, y2, 'go')\n",
            "    \n",
            "    - Alternatively, if your data is already a 2d array, you can pass it\n",
            "      directly to *x*, *y*. A separate data set will be drawn for every\n",
            "      column.\n",
            "    \n",
            "      Example: an array ``a`` where the first column represents the *x*\n",
            "      values and the other columns are the *y* columns::\n",
            "    \n",
            "      >>> plot(a[0], a[1:])\n",
            "    \n",
            "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
            "      groups::\n",
            "    \n",
            "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
            "    \n",
            "      In this case, any additional keyword argument applies to all\n",
            "      datasets. Also this syntax cannot be combined with the *data*\n",
            "      parameter.\n",
            "    \n",
            "    By default, each line is assigned a different style specified by a\n",
            "    'style cycle'. The *fmt* and line property parameters are only\n",
            "    necessary if you want explicit deviations from these defaults.\n",
            "    Alternatively, you can also change the style cycle using\n",
            "    :rc:`axes.prop_cycle`.\n",
            "    \n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    x, y : array-like or scalar\n",
            "        The horizontal / vertical coordinates of the data points.\n",
            "        *x* values are optional and default to `range(len(y))`.\n",
            "    \n",
            "        Commonly, these parameters are 1D arrays.\n",
            "    \n",
            "        They can also be scalars, or two-dimensional (in that case, the\n",
            "        columns represent separate data sets).\n",
            "    \n",
            "        These arguments cannot be passed as keywords.\n",
            "    \n",
            "    fmt : str, optional\n",
            "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
            "        section for a full description of the format strings.\n",
            "    \n",
            "        Format strings are just an abbreviation for quickly setting\n",
            "        basic line properties. All of these and more can also be\n",
            "        controlled by keyword arguments.\n",
            "    \n",
            "        This argument cannot be passed as keyword.\n",
            "    \n",
            "    data : indexable object, optional\n",
            "        An object with labelled data. If given, provide the label names to\n",
            "        plot in *x* and *y*.\n",
            "    \n",
            "        .. note::\n",
            "            Technically there's a slight ambiguity in calls where the\n",
            "            second label is a valid *fmt*. `plot('n', 'o', data=obj)`\n",
            "            could be `plt(x, y)` or `plt(y, fmt)`. In such cases,\n",
            "            the former interpretation is chosen, but a warning is issued.\n",
            "            You may suppress the warning by adding an empty format string\n",
            "            `plot('n', 'o', '', data=obj)`.\n",
            "    \n",
            "    Other Parameters\n",
            "    ----------------\n",
            "    scalex, scaley : bool, optional, default: True\n",
            "        These parameters determined if the view limits are adapted to\n",
            "        the data limits. The values are passed on to `autoscale_view`.\n",
            "    \n",
            "    **kwargs : `.Line2D` properties, optional\n",
            "        *kwargs* are used to specify properties like a line label (for\n",
            "        auto legends), linewidth, antialiasing, marker face color.\n",
            "        Example::\n",
            "    \n",
            "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
            "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
            "    \n",
            "        If you make multiple lines with one plot command, the kwargs\n",
            "        apply to all those lines.\n",
            "    \n",
            "        Here is a list of available `.Line2D` properties:\n",
            "    \n",
            "        Properties:\n",
            "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
            "        alpha: float or None\n",
            "        animated: bool\n",
            "        antialiased or aa: bool\n",
            "        clip_box: `.Bbox`\n",
            "        clip_on: bool\n",
            "        clip_path: Patch or (Path, Transform) or None\n",
            "        color or c: color\n",
            "        contains: callable\n",
            "        dash_capstyle: {'butt', 'round', 'projecting'}\n",
            "        dash_joinstyle: {'miter', 'round', 'bevel'}\n",
            "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
            "        data: (2, N) array or two 1D arrays\n",
            "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
            "        figure: `.Figure`\n",
            "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
            "        gid: str\n",
            "        in_layout: bool\n",
            "        label: object\n",
            "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
            "        linewidth or lw: float\n",
            "        marker: marker style\n",
            "        markeredgecolor or mec: color\n",
            "        markeredgewidth or mew: float\n",
            "        markerfacecolor or mfc: color\n",
            "        markerfacecoloralt or mfcalt: color\n",
            "        markersize or ms: float\n",
            "        markevery: None or int or (int, int) or slice or List[int] or float or (float, float)\n",
            "        path_effects: `.AbstractPathEffect`\n",
            "        picker: float or callable[[Artist, Event], Tuple[bool, dict]]\n",
            "        pickradius: float\n",
            "        rasterized: bool or None\n",
            "        sketch_params: (scale: float, length: float, randomness: float)\n",
            "        snap: bool or None\n",
            "        solid_capstyle: {'butt', 'round', 'projecting'}\n",
            "        solid_joinstyle: {'miter', 'round', 'bevel'}\n",
            "        transform: `matplotlib.transforms.Transform`\n",
            "        url: str\n",
            "        visible: bool\n",
            "        xdata: 1D array\n",
            "        ydata: 1D array\n",
            "        zorder: float\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    lines\n",
            "        A list of `.Line2D` objects representing the plotted data.\n",
            "    \n",
            "    See Also\n",
            "    --------\n",
            "    scatter : XY scatter plot with markers of varying size and/or color (\n",
            "        sometimes also called bubble chart).\n",
            "    \n",
            "    Notes\n",
            "    -----\n",
            "    **Format Strings**\n",
            "    \n",
            "    A format string consists of a part for color, marker and line::\n",
            "    \n",
            "        fmt = '[marker][line][color]'\n",
            "    \n",
            "    Each of them is optional. If not provided, the value from the style\n",
            "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
            "    the data will be a line without markers.\n",
            "    \n",
            "    Other combinations such as ``[color][marker][line]`` are also\n",
            "    supported, but note that their parsing may be ambiguous.\n",
            "    \n",
            "    **Markers**\n",
            "    \n",
            "    =============    ===============================\n",
            "    character        description\n",
            "    =============    ===============================\n",
            "    ``'.'``          point marker\n",
            "    ``','``          pixel marker\n",
            "    ``'o'``          circle marker\n",
            "    ``'v'``          triangle_down marker\n",
            "    ``'^'``          triangle_up marker\n",
            "    ``'<'``          triangle_left marker\n",
            "    ``'>'``          triangle_right marker\n",
            "    ``'1'``          tri_down marker\n",
            "    ``'2'``          tri_up marker\n",
            "    ``'3'``          tri_left marker\n",
            "    ``'4'``          tri_right marker\n",
            "    ``'s'``          square marker\n",
            "    ``'p'``          pentagon marker\n",
            "    ``'*'``          star marker\n",
            "    ``'h'``          hexagon1 marker\n",
            "    ``'H'``          hexagon2 marker\n",
            "    ``'+'``          plus marker\n",
            "    ``'x'``          x marker\n",
            "    ``'D'``          diamond marker\n",
            "    ``'d'``          thin_diamond marker\n",
            "    ``'|'``          vline marker\n",
            "    ``'_'``          hline marker\n",
            "    =============    ===============================\n",
            "    \n",
            "    **Line Styles**\n",
            "    \n",
            "    =============    ===============================\n",
            "    character        description\n",
            "    =============    ===============================\n",
            "    ``'-'``          solid line style\n",
            "    ``'--'``         dashed line style\n",
            "    ``'-.'``         dash-dot line style\n",
            "    ``':'``          dotted line style\n",
            "    =============    ===============================\n",
            "    \n",
            "    Example format strings::\n",
            "    \n",
            "        'b'    # blue markers with default shape\n",
            "        'or'   # red circles\n",
            "        '-g'   # green solid line\n",
            "        '--'   # dashed line with default color\n",
            "        '^k:'  # black triangle_up markers connected by a dotted line\n",
            "    \n",
            "    **Colors**\n",
            "    \n",
            "    The supported color abbreviations are the single letter codes\n",
            "    \n",
            "    =============    ===============================\n",
            "    character        color\n",
            "    =============    ===============================\n",
            "    ``'b'``          blue\n",
            "    ``'g'``          green\n",
            "    ``'r'``          red\n",
            "    ``'c'``          cyan\n",
            "    ``'m'``          magenta\n",
            "    ``'y'``          yellow\n",
            "    ``'k'``          black\n",
            "    ``'w'``          white\n",
            "    =============    ===============================\n",
            "    \n",
            "    and the ``'CN'`` colors that index into the default property cycle.\n",
            "    \n",
            "    If the color is the only part of the format string, you can\n",
            "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
            "    (``'green'``) or hex strings (``'#008000'``).\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxX5N2mggKAv"
      },
      "source": [
        "Jusqu'ici les valeurs en abscisses sont choisies automatiquement : 0 pour la première valeure, 1 pour la suivante, etc.\n",
        "\n",
        "Nous pouvons utiliser une liste pour les abscisses :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "7tI45nZlgIQP",
        "outputId": "cd305267-bc06-43da-e059-33a7b35aac27"
      },
      "source": [
        "abscisses = [0, 2, 4, 6]\n",
        "plt.plot(abscisses, l)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f85fd9e9190>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGvFDLOqixwr"
      },
      "source": [
        "Il est possible de tracer des fonctions, bien que cela soit plus simple en utilisant des tableaux (cf. cours sur les tableaux)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "cV6MKRBHiAqO",
        "outputId": "c950c583-f694-46ff-ab27-da86516c3fd8"
      },
      "source": [
        "import math as m\n",
        "\n",
        "x = [a * 0.1 for a in range(0, 100)]\n",
        "y = list(map(m.sin,x))\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('sin(x)')\n",
        "plt.plot(x,y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f85fd3a9bd0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cw9KJiGggs9v"
      },
      "source": [
        "Les figures produites peuvent être sauvegardées, et deviennent accessibles dans les fichiers de ce Jupyter Notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "PTHxv9jFg1R7",
        "outputId": "109867ae-0f55-4485-a4f8-b426d198af2e"
      },
      "source": [
        "plt.savefig(\"fig.png\")\n",
        "plt.savefig(\"fig.pdf\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}