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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# matplotlib - graphiques en Python\n",
      "\n",
      "Ce TD propose une d\u00e9couverte de la biblioth\u00e8que Python de dessin `matplotlib`. Avant de commencer, vous allez cr\u00e9er un compte sur le site <http://cloud.sagemath.com>, qui offre gratuitement, entre autres, la possiblit\u00e9 de cr\u00e9er des notebooks IPython (Python 2).\n",
      "\n",
      "Une fois connect\u00e9s, cr\u00e9ez un nouveau projet (bouton _\"create new project\"_), ouvrez-le, cliquez sur _\"Create new files in home directory of project\"_, et cr\u00e9ez un nouveau notebook en cliquant sur le bouton *\"IPython Notebook\"*.\n",
      "\n",
      "Vous pouvez cr\u00e9er autant de notebooks que vous le souaitez dans un projet, ainsi que d'autres outils (feuilles de calcul Sage, terminaux Unix, fichiers LaTeX, ...)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Prise en main de `matplotlib`\n",
      "\n",
      "Le point de d\u00e9part pour l'apprentissage de `matplotlib` est le tutoriel http://matplotlib.org/users/pyplot_tutorial.html.\n",
      "\n",
      "Une introduction utile est le tutoriel http://github.com/jrjohansson/scientific-python-lectures.\n",
      "\n",
      "de J.R. Johansson (robert@riken.jp) http://dml.riken.jp/~rob/\n",
      "\n",
      "`matplotlib` est une biblioth\u00e8que Python de dessin tr\u00e8s populaire dans le monde scientifique. Parmi ses caract\u00e9ristiques, il y a\u202f:\n",
      "\n",
      "- Support $\\LaTeX$,\n",
      "- Sortie de haute qualit\u00e9 en PNG, PDF, SVG, EPS, PGF, ...\n",
      "- Interactivit\u00e9.\n",
      "\n",
      "Elle s'int\u00e8gre parfaitement avec le notebook IPython. Le notebook typique commence par une ligne de configuration\u202f:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cette commande active l'affichage des graphiques matplotlib\n",
      "# dans le notebook. Elle doit appara\u00eetre une fois, au d\u00e9but du\n",
      "# notebook\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ensuite on importe les biblioth\u00e8ques `matplotlib.pyplot` et `numpy`. Par commodit\u00e9 et convention, on donne les alias `plt` et `np` \u00e0 ces biblioth\u00e8ques\u202f:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On commence imm\u00e9diatement avec un exemple: on approxime une parabole par morceaux de droites"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([0, 1, 4, 9, 16, 25, 36, 49])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f0b275ab510>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x7f0b298fda90>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Expliquons ce qui se passe ici\u202f:\n",
      "\n",
      "  - `plt.plot` prend en param\u00e8tre une liste de points, ce sont les ordonn\u00e9es,\n",
      "  - les abscisses sont par d\u00e9faut $0, 1, 2, \\dots$\n",
      "  - `plot.plot` renovie un objet de type `lines.Line2D`. IPython sait comment afficher cet objet, gr\u00e2ce \u00e0 la commande magique `%matplotlib` que nous avons saisie touten haut.\n",
      "\n",
      "Voyons maintenant comment donner des abscisses autres que $0, 1, 2, ...$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([1, 3, 5, 7], [10, 2, 8, 5], 'r--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f0b27505a50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f0b275d8690>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ceci m\u00e9rite quelques explications\u202f:\n",
      "\n",
      "- Le premier param\u00e8tre, est la liste d'abscisses,\n",
      "- Le deuxi\u00e8me param\u00e8tre, est la liste des ordonn\u00e9es,\n",
      "- Le troisi\u00e8me param\u00e8tre est le type de ligne\u202f: `r` pour *\"red\"*, `--` pour les tirets. La liste des couleurs et styles disponibles est ici\u202f:  http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot\n",
      "\n",
      "Mais ce dessin est d\u00e9form\u00e9\u202f! R\u00e9m\u00e9dions\u202f!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([1, 3, 5, 7], [10, 2, 8, 5], 'go-', linewidth=2)\n",
      "plt.axis([0,11,0,11])\n",
      "plt.xlabel('abscisses')\n",
      "plt.ylabel('ordonnees')\n",
      "plt.title('des donnees')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x7f0b2748d510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f0b2754b450>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On voit dans cet exemple que les fonctions de `plt` s'appliquent de fa\u00e7on incr\u00e9mentale au dessin.\n",
      "\n",
      "Voici un dernier exemple avec plusieurs courbes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([0, 1, 4, 9, 16, 25], 'bo', label=\"parabole\")\n",
      "plt.plot([0, 1, 27, 64, 125], 'rx-.', label=\"cubique\")\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.legend.Legend at 0x7f0b274c37d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JJeTuO80sHVgaKbncDeDuD0favQpMdfeV1a6nkotIPHr5ZbjwQujUKehIklJLlVzmA+Mi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\nCCV0EZEE8f8BtMRjdxwhddgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0b2749d310>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Exercices\n",
      "\n",
      "1. Dessinnez le graphe de votre nombre d'heures de cours par jour de la semaine (num\u00e9roter les jours de 1 \u00e0 7).\n",
      "\n",
      "1. Dessinner un pentagone (pas n\u00e9cessairement r\u00e9guilier).\n",
      "\n",
      "1. Dessinner la courbe d'\u00e9quation $y = x^3$ entre $-10$ et $10$ en utilisant $200$ \u00e9chantillons (c'est \u00e0 dire $200$ valeurs pour $x$). Aidez-vous avec une boucle `for`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Compr\u00e9hensions\n",
      "\n",
      "Les boucles `for` ne sont pas la seule fa\u00e7on de construire des gros ensembles de donn\u00e9es. Python offre une syntaxe tr\u00e8s pratique, dite des *compr\u00e9hensions de listes*. En voici un exemple"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = [x**2 for x in range(20) if x % 2 == 0]\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[0, 4, 16, 36, 64, 100, 144, 196, 256, 324]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Exercices\n",
      "\n",
      "1. Dessinnez \u00e0 nouveau la courbe d'\u00e9quation $y = x^3$, avec $20$ points d'\u00e9chantillonnage, en utilisant une seule ligne de code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Dessinner des fonctions\n",
      "\n",
      "`numpy` et `matplotlib` offrent une syntaxe encore plus simple pour repr\u00e9senter des fonctions math\u00e9matiques. Observez le code ci-dessous."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(10)\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = range(10)\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(a), type(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(numpy.ndarray, list)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.arange(0, 10, 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([0, 2, 4, 6, 8])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La fonction `np.arange(a,b,c)`, comme la fonction `range`, renvoie une liste comprise entre `a` et `b` avec des incr\u00e9ments de `c`. Mais l'objet renvoy\u00e9 n'est pas une liste normale Python\u202f: il s'agit d'un **array numpy**.\n",
      "\n",
      "Les arrays numpy se comportent diff\u00e9remment des listes Python. Lisez et comprenez ces exemples\u202f:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2 * a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2 * b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a * a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b * b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "can't multiply sequence by non-int of type 'list'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-15-aa818ac5610b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'list'"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Les op\u00e9rations arithm\u00e9tiques sont ex\u00e9cut\u00e9es composante par composante sur les arrays numpy, ceci nous permet de dessinner des fonctions math\u00e9matiques avec une syntaxe tr\u00e8s intuitive.\n",
      "\n",
      "Voici la fonction $\\sin x$ entre $0$ et $6\\pi$."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 2 * np.pi * np.arange(0, 3, 0.01)   # np.pi est la constante \u03c0\n",
      "y = np.sin(x)\n",
      "plt.plot(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f0b2727d090>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f0b27302cd0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Exercices\n",
      "\n",
      "1. Dessinner la fonction $\\cos$ entre $-\\pi$ et $\\pi$.\n",
      "\n",
      "1. Dessinner la fonction $y = x^3$ entre $-10$ et $10$ en utilisant un array numpy.\n",
      "\n",
      "1. On rappelle que le cercle de rayon $1$ a pour \u00e9quation $x^2 + y^2 = 1$. La fonction racine carr\u00e9e s'appelle `np.sqrt`. Dessinner un cercle.\n",
      "\n",
      "4. On rappelle que le cercle unit\u00e9 est param\u00e9tris\u00e9 par les fonctions $(\\cos(x), \\sin(x))$, avec $-\\pi\\le x\\le\\pi$. Dessinner un cercle en utilisant les fonctions `np.sin` et `np.cos`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Sous-graphes\n",
      "\n",
      "On peut afficher plusieurs graphes c\u00f4te \u00e0 c\u00f4te avec la fonction `plt.subplots`. Celle ci prend le nombre de lignes, le nombre de colonnes, et le graphe sur lequel travailler. Voici un exemple."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 2 * np.pi * np.arange(0, 3, 0.01)   # np.pi est la constante \u03c0\n",
      "y = np.sin(x)\n",
      "\n",
      "plt.subplot(1,2,1)\n",
      "plt.plot(x, y, 'r--')\n",
      "plt.subplot(1,2,2)\n",
      "plt.plot(y, x, 'g*-');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cVV/HoPBfvDcf/549kJ8Pw4Z5zih3s3w57NgBK1d62hJtvuTDD5u2NDYtTcuk6oVpNrwh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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0b272d0690>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Exercices\n",
      "\n",
      "1. Reproduire le dessin ci-dessous (suggestion\u202fla premi\u00e8re ressemble m\u00e9chamment \u00e0 $e^{-x} \\cos(cx)$... \u00e0 vous de trouver la constante $c$):\n",
      "\n",
      "   ![](http://matplotlib.org/mpl_examples/subplots_axes_and_figures/subplot_demo.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Autres types de graphes\n",
      "\n",
      "Voici quelques autres types de graphe offerts par `matplotlib`:\n",
      "\n",
      "- scatter\u202f: la m\u00eame chose qu'un plot sans lignes, mais permet de controler taille et couleur des points.\n",
      "- step\u202f: graphe en escalier.\n",
      "- bar\u202f: graphe en barres.\n",
      "- fill\u202f: remplissage entre deux courbes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = np.array([0,1,2,3,4,5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.subplot(2, 2, 1)\n",
      "plt.scatter(n, -n, s=10*n+1)\n",
      "plt.title(\"diffusion\")\n",
      "\n",
      "plt.subplot(2, 2, 2)\n",
      "plt.step(n, n**2)\n",
      "plt.title(\"escalier\")\n",
      "\n",
      "plt.subplot(2, 2, 3)\n",
      "plt.bar(n, n**2)\n",
      "plt.title(\"barres\")\n",
      "\n",
      "plt.subplot(2, 2, 4)\n",
      "plt.fill_between(x, x**2, x**3, color=\"green\");\n",
      "plt.title(\"remplissage\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+DtzqFyob5Eswl1RN6E8fTbhx+ATwupldmst65UJK3SyWfDpvc1+bJ+h3+egw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\n/inqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0b27101050>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On peut aussi faire des g\u00e2teaux"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.pie([5, 10, 5, 80], labels=['a', 'b', 'c', 'd'], colors=['r', 'w', 'b', 'y'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "([<matplotlib.patches.Wedge at 0x7f0b26db8050>,\n",
        "  <matplotlib.patches.Wedge at 0x7f0b26db8a10>,\n",
        "  <matplotlib.patches.Wedge at 0x7f0b26dc23d0>,\n",
        "  <matplotlib.patches.Wedge at 0x7f0b26dc2d50>],\n",
        " [<matplotlib.text.Text at 0x7f0b26db85d0>,\n",
        "  <matplotlib.text.Text at 0x7f0b26db8fd0>,\n",
        "  <matplotlib.text.Text at 0x7f0b26dc2990>,\n",
        "  <matplotlib.text.Text at 0x7f0b26dce350>])"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PjqislFVDhnD4kXk/Pa5y6Y9/xEyaJBIMBEwikRDL48ECDojFOIb0dlP6A1Jp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\n2m8jcJaNVVIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0b2721cf90>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Histogrammes\n",
      "\n",
      "Un histogramme est une repr\u00e9sentation de la fr\u00e9quence d'occcurence d'une valeur dans un ensemble."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = [1, 2, 2, 3, 4, 10, 2, 1]\n",
      "\n",
      "plt.hist(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(array([ 2.,  3.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
        " array([  1. ,   1.9,   2.8,   3.7,   4.6,   5.5,   6.4,   7.3,   8.2,\n",
        "          9.1,  10. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f0b27229c10>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Par d\u00e9faut un histogramme est s\u00e9par\u00e9 en au plus 10 plages de valeurs. Cela peut \u00eatre contr\u00f4l\u00e9 par le param\u00e8tre `bins`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = [x % 30 for x in range(10000) if np.sqrt(x).is_integer()]\n",
      "print n\n",
      "\n",
      "plt.subplot(2,1,1)\n",
      "plt.hist(n)\n",
      "\n",
      "plt.subplot(2,1,2)\n",
      "plt.hist(n, bins=30)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0, 1, 4, 9, 16, 25, 6, 19, 4, 21, 10, 1, 24, 19, 16, 15, 16, 19, 24, 1, 10, 21, 4, 19, 6, 25, 16, 9, 4, 1, 0, 1, 4, 9, 16, 25, 6, 19, 4, 21, 10, 1, 24, 19, 16, 15, 16, 19, 24, 1, 10, 21, 4, 19, 6, 25, 16, 9, 4, 1, 0, 1, 4, 9, 16, 25, 6, 19, 4, 21, 10, 1, 24, 19, 16, 15, 16, 19, 24, 1, 10, 21, 4, 19, 6, 25, 16, 9, 4, 1, 0, 1, 4, 9, 16, 25, 6, 19, 4, 21]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f0b26d80fd0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Exercices\n",
      "\n",
      "1. Dessinner le g\u00e2teau de vos heures de cours par jour de la semaine.\n",
      "\n",
      "1. Dessinner l'histogramme de vos notes au premier semestre.\n",
      "\n",
      "## Analyse de donn\u00e9es\n",
      "\n",
      "Pour terminer, nous allons travailler sur des donn\u00e9es r\u00e9elles. Copiez-collez ce code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import urllib2\n",
      "import json\n",
      "prenoms = urllib2.urlopen('http://opendata.paris.fr/api/records/1.0/download?dataset=liste_des_prenoms_2004_a_2012&format=json')\n",
      "donnees_brutes = json.load(prenoms)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La variable `donnees_brutes` contient la liste des tous les noms enregistr\u00e9s par la mairie de Paris entre 2004 et 2014 (extr\u00eames inclus), avec le nombre d'occurrences par ann\u00e9e et par pr\u00e9nom.\n",
      "\n",
      "Il s'agit d'un grosse liste\u202f!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(donnees_brutes)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "13546"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chaque \u00e9l\u00e9ment de la liste est un dictionnaire Python."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "donnees_brutes[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "{u'datasetid': u'liste_des_prenoms_2004_a_2012',\n",
        " u'fields': {u'annee': 2011,\n",
        "  u'nombre': 12,\n",
        "  u'prenoms': u'Zachary',\n",
        "  u'sexe': u'M'},\n",
        " u'record_timestamp': u'2015-03-16T17:33:33.316694',\n",
        " u'recordid': u'aa8805d011cdcc1b9cf1abcd8febd8946028050b'}"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Le seul champs int\u00e9ressant est `fields`, qui contient les champs suivants\u202f:\n",
      "\n",
      "- `prenoms`\u202f: pr\u00e9onom enregistr\u00e9,\n",
      "- `annee`\u202f: ann\u00e9e de l'enregistrement,\n",
      "- `nombre`\u202f: nombre de fois que le nom a \u00e9t\u00e9 donn\u00e9,\n",
      "- `sexe`\u202f: genre du pr\u00e9nom\u202f: `M`, `F` et `X` (apr\u00e8s 2010 `X` n'est plus utilis\u00e9, et les pr\u00e9noms unisex sont s\u00e9par\u00e9s en enregistrements masculins et enregistrements f\u00e9minins)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "donnees_brutes[0]['fields']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "{u'annee': 2011, u'nombre': 12, u'prenoms': u'Zachary', u'sexe': u'M'}"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Par cons\u00e9quent, on ne va garder que ce contenu, dans un liste nomm\u00e9e `donnees`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "donnees = [d['fields'] for d in donnees_brutes]\n",
      "donnees[50:60]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "[{u'annee': 2012, u'nombre': 5, u'prenoms': u'Annabelle', u'sexe': u'F'},\n",
        " {u'annee': 2012, u'nombre': 46, u'prenoms': u'Anouk', u'sexe': u'F'},\n",
        " {u'annee': 2012, u'nombre': 8, u'prenoms': u'Anya', u'sexe': u'F'},\n",
        " {u'annee': 2012, u'nombre': 10, u'prenoms': u'Ariane', u'sexe': u'F'},\n",
        " {u'annee': 2012, u'nombre': 10, u'prenoms': u'Aristide', u'sexe': u'M'},\n",
        " {u'annee': 2012, u'nombre': 7, u'prenoms': u'Arthus', u'sexe': u'M'},\n",
        " {u'annee': 2012, u'nombre': 7, u'prenoms': u'Assetou', u'sexe': u'F'},\n",
        " {u'annee': 2012, u'nombre': 6, u'prenoms': u'Assil', u'sexe': u'F'},\n",
        " {u'annee': 2012, u'nombre': 5, u'prenoms': u'Astou', u'sexe': u'F'},\n",
        " {u'annee': 2012, u'nombre': 17, u'prenoms': u'Augustine', u'sexe': u'F'}]"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Exercices\n",
      "\n",
      "1. Dessinner l'histogramme du nombre d'enregistrements par ann\u00e9e.\n",
      "\n",
      "1. Dessinner, sur un m\u00eame graphe, le nombre d'enregistrements par ann\u00e9e de ces noms\u202f:\n",
      "    \n",
      "    - Alexandre\n",
      "    - Gabriel\n",
      "    - Adam\n",
      "    - Louise\n",
      "    - Arthur\n",
      "    - Gautier\n",
      "    - Annabelle\n",
      "    - votre nom\n",
      "    \n",
      "1. Trouver le nom le plus populaire pour chaque ann\u00e9e.\n",
      "\n",
      "1. Dessiner le nombre de naissances de gar\u00e7ons et de filles en fonction de l'ann\u00e9e."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}