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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutoriel improvis\u00e9: Manipuler des donn\u00e9es en Python avec pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Session de ~2h avec pour objectifs:\n",
      "\n",
      "* \"d\u00e9marrage \u00e0 froid\" en Python: installation ([Anaconda](https://store.continuum.io/cshop/anaconda/)), prise en main du [Notebook IPython](http://ipython.org/notebook.html) [0h30]\n",
      "* exploration rapide de Python num\u00e9rique: calcul avec [numpy](http://www.numpy.org/), et trac\u00e9s avec [Matplotlib](http://matplotlib.org/) [1h]\n",
      "* objectif final: charger, manipuler et tracer les donn\u00e9es d'un capteur de temp\u00e9rature et de CO2*, avec [pandas](http://pandas.pydata.org/) [1h]\n",
      "\n",
      "\n",
      "**5 f\u00e9vrier 2015**, 14h-16h30, anim\u00e9 par Pierre Haessig \n",
      "\n",
      "\\* *mise en ab\u00eeme*: la session a lieu dans la salle o\u00f9 est install\u00e9e ce capteur..."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1) D\u00e9marrage"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "a) Installation de Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La distribution **Ananconda**, de *Continuum Analytics*, contient tous les paquets n\u00e9cessaires pour une utilisation scientifique classique.\n",
      "\n",
      "[lien direct de t\u00e9l\u00e9chargement](http://continuum.io/downloads) (300 Mo, gratuit)\n",
      "\n",
      "\u2192 choisir la version adapt\u00e9e au syst\u00e8me d'exploitation (e.g. 32 vs. 64 bits)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "b) Notebook IPython"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Sous Windows, dans le Menu D\u00e9marrer, lancer \"Anaconda/IPython Notebook\"*.\n",
      "\n",
      "\u2192 Ouvre une fen\u00eatre de commande (le serveur) et une fen\u00eatre de navigateur: l'interface du Notebook avec laquelle on va interagir"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "tour de l'interface"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Essayer dans le menu: \"Help/User Interface Tour\"\n",
      "* introduction rapide au shell IPython: http://ipython.org/ipython-doc/dev/interactive/tutorial.html"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cellule de type \"Code\":"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = 1\n",
      "a+2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cellule de type \"Markdown\":\n",
      "\n",
      "* pour y \u00e9crire du texte (avec un peu de *mise en forme*, avec la syntaxe Markdown)\n",
      "* et les \u00e9quations Latex:  $\\theta = 1$\n",
      "\n",
      "Aide sur la syntaxe: [\"Markdown basics\"](https://help.github.com/articles/markdown-basics/)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2) Exploration de Python num\u00e9rique: Numpy et Matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Importer des modules (avec un alias court, plus pratique)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Importer juste un \u00e9l\u00e9ment:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import pi\n",
      "pi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "3.141592653589793"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Activer l'affichage des graphiques dans le Notebook (image statique)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Activer l'affichage dans une fen\u00eatre s\u00e9par\u00e9 (interactif)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib qt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Un petit trac\u00e9 Matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vecteur de donn\u00e9es \u00e0 tracer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = np.linspace(0, 4*pi)\n",
      "y = np.sin(t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Syntaxe de graphs avec Matplotlib: tr\u00e8s proche de Matlab (volontairement)\n",
      "\n",
      "Galerie d'exemples, tr\u00e8s pratique pour d\u00e9couvrir: http://matplotlib.org/gallery.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(t, y) # affiche y=f(t)\n",
      "plt.title('sinus')\n",
      "plt.xlabel('temps (s)')\n",
      "plt.grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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Mgoh6/1dn8URdf3Vk0r90KWzZAi1bhq+n2GjQjxFJ9PUnToS2bV2riDdJ9PXf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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1e1c428e50>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3) Lecture de donn\u00e9es"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La \"magic\" `%ls` permet de lister le contenu d'un r\u00e9pertoire (comme `ls` sour Linux, `dir` sous Windows)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%ls *.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "log-20150128-092830.csv\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nom du fichier \u00e0 lire (qui est ici dans le r\u00e9pertoire courant o\u00f9 le Notebook a \u00e9t\u00e9 ex\u00e9cut\u00e9)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "u'/home/pierre'"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fname = 'log-20150128-092830.csv'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Affichage du contenu du fichier:\n",
      "\n",
      "* `head` et `tail` sous Linux (appel au shell avec le signe `!`)\n",
      "* sinon magic `%more`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tail $fname"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "05/02/2015;15:19:19.813;indoor relative humidity;34.0;%\r",
        "\r\n",
        "05/02/2015;15:19:23.623;indoor temperature;23.5;\ufffdC\r",
        "\r\n",
        "05/02/2015;15:19:30.553;volatile organic compounds;0.18;ppm\r",
        "\r\n",
        "05/02/2015;15:19:35.253;carbon dioxide;2100;ppm\r",
        "\r\n",
        "05/02/2015;15:20:19.215;indoor relative humidity;34.0;%\r",
        "\r\n",
        "05/02/2015;15:21:20.317;indoor relative humidity;34.0;%\r",
        "\r\n",
        "05/02/2015;15:22:18.520;indoor relative humidity;34.0;%\r",
        "\r\n",
        "05/02/2015;15:22:25.140;indoor temperature;23.5;\ufffdC\r",
        "\r\n",
        "05/02/2015;15:22:28.230;volatile organic compounds;0.18;ppm\r",
        "\r\n",
        "05/02/2015;15:22:35.361;carbon dioxide;2160;ppm\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# affiche *tout* le fichier dans le pager\n",
      "%more $fname"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "a) Lecture (parsing) du ficher CSV:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "la fonction qui fait le travail: `read_csv`.\n",
      "\n",
      "* (+) Pratique,\n",
      "* (-) mais avec *beaucoup* d'options"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Afficher l'aide (syntaxe sp\u00e9cifique \u00e0 la console IPython)\n",
      "pd.read_csv?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Appel simple, en pr\u00e9cisant juste le s\u00e9parateur des champs:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = pd.read_csv(fname, sep=';')\n",
      "d.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
        "      <th>time</th>\n",
        "      <th>type</th>\n",
        "      <th>value</th>\n",
        "      <th>unit</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 28/01/2015</td>\n",
        "      <td> 09:28:45.871</td>\n",
        "      <td>   indoor relative humidity</td>\n",
        "      <td> 36.00</td>\n",
        "      <td>   %</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 28/01/2015</td>\n",
        "      <td> 09:29:44.274</td>\n",
        "      <td>   indoor relative humidity</td>\n",
        "      <td> 36.00</td>\n",
        "      <td>   %</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 28/01/2015</td>\n",
        "      <td> 09:30:46.377</td>\n",
        "      <td>   indoor relative humidity</td>\n",
        "      <td> 37.00</td>\n",
        "      <td>   %</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 28/01/2015</td>\n",
        "      <td> 09:30:50.697</td>\n",
        "      <td>         indoor temperature</td>\n",
        "      <td> 21.20</td>\n",
        "      <td>  \ufffdC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 28/01/2015</td>\n",
        "      <td> 09:30:55.908</td>\n",
        "      <td> volatile organic compounds</td>\n",
        "      <td>  0.06</td>\n",
        "      <td> ppm</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "         date          time                        type  value unit\n",
        "0  28/01/2015  09:28:45.871    indoor relative humidity  36.00    %\n",
        "1  28/01/2015  09:29:44.274    indoor relative humidity  36.00    %\n",
        "2  28/01/2015  09:30:46.377    indoor relative humidity  37.00    %\n",
        "3  28/01/2015  09:30:50.697          indoor temperature  21.20   \ufffdC\n",
        "4  28/01/2015  09:30:55.908  volatile organic compounds   0.06  ppm"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u2192 Lecture sans erreur, mais les dates ne sont pas analys\u00e9es (elles restent sous forme de cha\u00eenes de caract\u00e8res)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d['date'][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "'28/01/2015'"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(d['date'][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "str"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Appel de `read_csv` avec des options adapt\u00e9es \u00e0 ce fichier:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = pd.read_csv(fname, sep=';',\n",
      "                parse_dates=[[0,1]], # aller chercher la date dans les 2 premi\u00e8re colonnes\n",
      "                index_col=0, # utiliser la date comme index des donn\u00e9es\n",
      "                dayfirst=True, # ne pas confondre le 5 f\u00e9vrier avec le 2 mai (format am\u00e9ricain MM-DD-YYYY)\n",
      "                )\n",
      "d.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>type</th>\n",
        "      <th>value</th>\n",
        "      <th>unit</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>date_time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2015-01-28 09:28:45.871000</th>\n",
        "      <td>   indoor relative humidity</td>\n",
        "      <td> 36.00</td>\n",
        "      <td>   %</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-01-28 09:29:44.274000</th>\n",
        "      <td>   indoor relative humidity</td>\n",
        "      <td> 36.00</td>\n",
        "      <td>   %</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-01-28 09:30:46.377000</th>\n",
        "      <td>   indoor relative humidity</td>\n",
        "      <td> 37.00</td>\n",
        "      <td>   %</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-01-28 09:30:50.697000</th>\n",
        "      <td>         indoor temperature</td>\n",
        "      <td> 21.20</td>\n",
        "      <td>  \ufffdC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-01-28 09:30:55.908000</th>\n",
        "      <td> volatile organic compounds</td>\n",
        "      <td>  0.06</td>\n",
        "      <td> ppm</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "                                                  type  value unit\n",
        "date_time                                                         \n",
        "2015-01-28 09:28:45.871000    indoor relative humidity  36.00    %\n",
        "2015-01-28 09:29:44.274000    indoor relative humidity  36.00    %\n",
        "2015-01-28 09:30:46.377000    indoor relative humidity  37.00    %\n",
        "2015-01-28 09:30:50.697000          indoor temperature  21.20   \ufffdC\n",
        "2015-01-28 09:30:55.908000  volatile organic compounds   0.06  ppm"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "b) Manipulation des donn\u00e9es"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "les donn\u00e9s sont dans un `pandas.DataFrame`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On r\u00e9cup\u00e8re une colonne par indexation. Objet de type `Series`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals = d['value']\n",
      "vals.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "date_time\n",
        "2015-01-28 09:28:45.871000    36.00\n",
        "2015-01-28 09:29:44.274000    36.00\n",
        "2015-01-28 09:30:46.377000    37.00\n",
        "2015-01-28 09:30:50.697000    21.20\n",
        "2015-01-28 09:30:55.908000     0.06\n",
        "Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(vals)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "pandas.core.series.Series"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tout les type de mesures issues du capteur"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type_meas = d['type']\n",
      "type_meas.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "array(['indoor relative humidity', 'indoor temperature',\n",
        "       'volatile organic compounds', 'carbon dioxide'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "R\u00e9cup\u00e9rer seulement un type de mesures: la temp\u00e9rature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp = vals[d['type'] == 'indoor temperature']\n",
      "temp.head(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "date_time\n",
        "2015-01-28 09:30:50.697000    21.2\n",
        "2015-01-28 09:33:49.494000    21.4\n",
        "2015-01-28 09:36:51.682000    21.2\n",
        "Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Exo: faire la m\u00eame chose pour le CO2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "co2 = vals[d['type'] == 'carbon dioxide']\n",
      "co2.head(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "date_time\n",
        "2015-01-28 09:31:01.408000    390\n",
        "2015-01-28 09:33:59.015000    390\n",
        "2015-01-28 09:37:01.512000    390\n",
        "Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Statistique par groupes: `groupby`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Par type de mesure"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.groupby('type').mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>value</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>type</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>carbon dioxide</th>\n",
        "      <td> 441.214809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>indoor relative humidity</th>\n",
        "      <td>  29.069816</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>indoor temperature</th>\n",
        "      <td>  20.549659</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>volatile organic compounds</th>\n",
        "      <td>   0.059591</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 172,
       "text": [
        "                                 value\n",
        "type                                  \n",
        "carbon dioxide              441.214809\n",
        "indoor relative humidity     29.069816\n",
        "indoor temperature           20.549659\n",
        "volatile organic compounds    0.059591"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Statistiques par jour"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp.groupby(temp.index.dayofyear).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "28    21.091349\n",
        "29    21.766250\n",
        "30    21.304792\n",
        "31    19.128750\n",
        "32    18.576458\n",
        "33    20.560417\n",
        "34    21.048125\n",
        "35    20.702917\n",
        "36    21.225649\n",
        "Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Changer la fr\u00e9quence: r\u00e9\u00e9chantillonage"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sous \u00e9chantillonage: moyennes journali\u00e8res"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp.resample('1D')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "date_time\n",
        "2015-01-28    21.091349\n",
        "2015-01-29    21.766250\n",
        "2015-01-30    21.304792\n",
        "2015-01-31    19.128750\n",
        "2015-02-01    18.576458\n",
        "2015-02-02    20.560417\n",
        "2015-02-03    21.048125\n",
        "2015-02-04    20.702917\n",
        "2015-02-05    21.225649\n",
        "Freq: D, Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sur \u00e9chantillonage: il faut combler les trous"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp.resample('1Min').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "date_time\n",
        "2015-01-28 09:30:00    21.2\n",
        "2015-01-28 09:31:00     NaN\n",
        "2015-01-28 09:32:00     NaN\n",
        "2015-01-28 09:33:00    21.4\n",
        "2015-01-28 09:34:00     NaN\n",
        "Freq: T, Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp.resample('1Min', fill_method='bfill').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "date_time\n",
        "2015-01-28 09:30:00    21.2\n",
        "2015-01-28 09:31:00    21.4\n",
        "2015-01-28 09:32:00    21.4\n",
        "2015-01-28 09:33:00    21.4\n",
        "2015-01-28 09:34:00    21.2\n",
        "Freq: T, Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp.resample('1Min').interpolate().head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "date_time\n",
        "2015-01-28 09:30:00    21.200000\n",
        "2015-01-28 09:31:00    21.266667\n",
        "2015-01-28 09:32:00    21.333333\n",
        "2015-01-28 09:33:00    21.400000\n",
        "2015-01-28 09:34:00    21.333333\n",
        "Freq: T, Name: value, dtype: float64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "c) Trac\u00e9 des donn\u00e9es capteur"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp.plot()\n",
      "plt.ylabel(u'temp\u00e9rature (\u00b0C)') # u'' pour des textes accentu\u00e9s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "<matplotlib.text.Text at 0x7f208c9ec410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HaSx/tv3rtvHGpQ0/g20t7r23vv63a3HvTFTVe7zhNuBgYHTtp6yN++8vlt6P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\nFO3t9sTA+4QVFt94o5C1jEiCDen8hn0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NtusL6BRPS0FVF/rnwzRomamq7Z6mZ4BrROQ6\nTGlrctJamlFfM2i0XV+zaEwczajyIGzA1I738Ka7hKzv4m1zKzAmsLwfcC4mCuW8DDT2tFmjd76u\nEctt0beyN+5WQWd34CFgtbJ122Eq0FZJUd/OwAbB/90mfd559sYYox42asQYw72i/ue89Xnn+QFw\nWIX1uWtMe8jvxPBDoIDxnw0JLC+/kVfGxLv6N/omgT8nmDKgg0FOQONJmM++G4ENbdOICb+8LEpX\n3vq8Y44G3gZej6FzMPC4N70LcEQG9+EOmFLcPzCf8UcAfWzR551rJ0yM+CTgPs9w9bBM48YYV8gL\nFNt8dPifc9S3HfAIJrxyZJVtc9GY1ZCLe0dEDgf2wzxg7wKni8huAOpd6QBDMf7oDUXkMeAYEenh\nbbvY+xzzP9GS1Dga2BeTnuA94NcisrctGkXkMuAMYFMp9kYW5XvP6xr2xOQVvxBoFZGTvFVdI3bZ\nGeguIlcAF2N8rakhIisDvwRuUNXdgBswLS/726DP07gC8DNggqqOwURbbej/bzZo9JgLXI95nv3I\nFyv0iUg/4ErgP6q6u6pOEdNHNxH1VXldw2zI6u1CoBSJif2+NDD/d0y8+AZaVkIAvoVJW/oM8J2U\nNXYLTB8KPOHr8TRfQLGUnLnGMn2jgBGYiIMHKbpPwlxQmV1D73yDgV7e9DBvvDMmQVX3kPvBj4k+\nDfgU+EXK+tYM6Au6S/oAbwHrl22fqb7ANeztTfcLLL8OuBfzclrRv5Y5XMPBlH4lbotpfboRptV2\nn8C6rlnrC7mGPwWuwqQ/Ph04y7snB+Z1DfMaMinpi8gZwCVS7H3pQ0BFZIQ3PwtYgjFkqKoG3sA9\nMD7nHVT1Du94iesWkxjpOhE51lv0PPCeiGyh5k54BBOmtW0eGsv1qeqLqjoVmAbMAE6osHtW1/D/\nROQDTJzz7Z7ON7zxU5jKr6u9zcPOPxnYVFUvSFpbmb7fB/RNUVNq7oEJb30d81USVgJMVV+ZxiuA\n2zyN87x1vwUGeTp+5A1o6RdaVtfwCuCOwHX6HFOSfhNTCHlcRK709AXbdWR9De/wNFyKeSE9g/ny\nXYh5Zk7w1md2DXMn5TftVsAU4CZMBcijmDjYAcA5wOPAv4DfYTrs+KWWlQzKjhdaCdigxq0x8ewT\ngH0w9QwHA2tgSvY/DGx7Cl6lJ4GSQZoaQ/RNIlAR5V2rFkxukBHeMr/EElbqT/waesftj4kKavHm\nH8SUqNYKbLMG8BUw2Jtfwf8Nad6HFfSdgWmL4G8zCng4MN8zK31VNK7jzQdL/AdgWob6X3ipp7mO\n0Hcm5mW5I3AZpqHSsxh3z0nedt2z0FdB468xuaM2J5Dm2LuG1+FVzGalMe8h7ZL+YOBCVT1aVa/F\ntG7bFvhKTccCPwV+oKqnYl4Ifkm/Q4tPP3wwBY29gB+r6jhV/SvmJfSaqs4BXsK0xtvT2/YJYFdP\ny9KMNIbpm+qvVNNY6jXMy+BQLwTz62Jazpb46FO8hqjpBHoA5qsCjMEfhmkP0M3TMweT0O0+r07i\ne15dQmINvmrUtxGwvYh095Zti7mOfp3JAVnpq6JxGxHppl6J32MR5qX+P2/f8nqmrPRtCOyBMfJj\nMRWl4zHP9hHefm1Z6KugcSjwDcxz/bfA5m2YF/qn3r6ZaMybVHrO8h4UxZTkuwfm1wBafWOkqtO8\n7QdhvgTujzpmuQFLUOPTqqoi0hdTsj8eWFlE3lfVc8Xkx75AROZjOhp5ClO67qAnSY1V9K0kIv9T\n1dO97T4RkRcwkR3HYb4EOhiqpK9hmd5+mMiNQSLSXVVfFZEpmKiJSf6DhflCGo15eV2R1YNWRd9T\nmCb06wL/JyIHe8v+nKUhqHYNReQzTPuKo4EjgUuy0lZB31RMYe1NTKn/IVVtE9NnbFff/ZPz//wS\n3jUEPvUqx78PHEXG19AGEivpB32g/h+sqvPUNAzy1wnG9xfcb29Mxc8sPB9mWkRo9G/G1TG5ZlbG\nGNeTRWSUql6DqQA6HGgFztT08pPE1Xch8H0R2cp7IYwGbsFEn6yqqg+loc/T6DdeKcErhc4GtgA2\n9RbfhQl5G+jtuzcmx88wVf1xGl8ddeob4M1virkPD1TVU9Iq4der0Xtpn4IJM91fVSdYoO9OTDDB\nPFW9zzP43VS1VVWvU4+cNZbch5ivkJ0wnQHdlLQ262nEN+T9l0KZ7zhk3vcxPwps702P9MY9gHWj\n9k1iiKMxZJ/L8HyS3nz3wHSiPt469V0KnBiYD/rO06j7GIznP/b/t/Jrg6lkvArTvsHvaOJ2YM+0\n/tuE9Q1IS18CGvfwpvtari9Vv3hC/3OvNDXaPjRU0vf8tKqqS0VkExH5noj00jI3gqqq5yZpxURG\n3A2c78VJt6vqeyLS1feVN6KpXo1l+2yKKSVMCvyGNjF00WSTjtWrbzim0tl3Bc0KXMM0/Pa3APuL\nydx5PXCLiPwCll2bLmoycd6Fyftzk4hcjSlxvextl5p7KSF9c1PU16jGV7zt5luuL203ThL/8/IV\nd18jDfn0VXWJiPQCvoPxNbcCm4vI7ar6r4BfGkwN/76YSpU/qurV5cdqREsSGsX0bn885lPwj1qW\nRMnbLtGbOgl9/vqkr6GIdA0c9yqMH3QYJgLnj5gQUlHV8/FceKo6WUSexzRqWw3YTk3lWuLYrq8Z\nNNqur1k0NhW1fBZQ5tbAVGjeALzszffB5HM5m2I4nt8d3whM5EafqOMlMTSocQPgx5SGxiXdWYjV\n+vxjhv03mAfsRWAzb/5rmBQL/id0F1J04TSLvmbQaLu+ZtHYjENN7h31SpIisqGIDPTm7waGiMha\natKO/hNTMba7v5u371RVPVMD6Xo1hdJ9gxrfUtUrVXWe5yoJfqks9/pEZHUR6a2GJWJy698sIj/1\nKovPBhYD/UWkj6q+ivmsP9LTt1TTjRCyWl8zaLRdX7NobGaqGn0RuVREfuVNbyTGH389cKuY/NKP\nYmrwf+Xt8jTwATBWRNYsN0q+zzlhY5W0RtFke9iyXV9XETkX01pxmLdsW0z3b08AczCRVYswlfHH\nYEpXeMueSUJHs+prBo2262sWjcsF1T4FMKFNn2Fauv0BONZbXsAYp57A+pjY2O28dVsTSOOb9mC7\nRpv1YRrWfAz8P0ojgI7G1B1sjYmpv9JbviKmAc5jwF8xL6vQrJSdQV8zaLRdX7NoXF6Gan+EH2p5\nL3CNNz0a08z6Ms9IneYtPxt4KvMfYLnGJtC3DbA0MN+Caa5+NKb09BdgB29dX8zX4ZGYrIXrdHZ9\nzaDRdn3NonF5Gar9Eb7BWhn4EtOw5kfAud7yH2C63lsXUwG5cXC/jG4WqzXars871z2YT+jLMS+h\nsZgoq6eAXb1tVsPkUNo7K13Noq8ZNNqur1k0Lg9DxZBNVVUxceT/E5Mx7x5MpEkvEVnfM1T/wjQY\neQ94XVKItW9mjbbr8zgG04rxVlUd7S8UkWuAP4jIJExemoe0NHeJ09c8Gm3X1ywamx6/FBpvY5E3\nMWlH/4tp7fY7Vf1dStrqwnaNtuoTkbOBnVR1VzEJyJaoaTC2PqaD6BdVdZbT17wabdfXLBqbnjif\nAxTjxA8E3vSmVwyszyT1bDNrtF2fp+E94CBvOrQ7PqevuTXarq9ZNDbzELuk78eEi8jjmArJP4vp\nciyx0MFGsV1jE+g7BLhFVXtU3TgHbNcH9mu0XR80h8ZmJnYaBs9Y9cdUOr7jLUslN3u92K6xCfTd\nJSKDxGQvVBteREFs1wf2a7RdHzSHxmamVp9+C7ArcLZm1LFErdiu0XZ9Dodj+aYmo+9wOByO5iaT\njtEdDofDYQfO6DscDkcnwhl9h8Ph6EQ4o+9wOBydCGf0HQ6HoxPhjL7D4XB0IpzRd3QKRORsETml\nwvr9RGSTOo9dsq+InCMiu9ZzLIcjbZzRd3QWqjVIOQDYtM5jl+yrqmep6uN1HsvhSBVn9B3LLSJy\npoi8ISJPU+x+73si8ryITBWRiSLSW0S2B/YFLhaRKSKynogMFZGHReQFEXlKRIZFnCO470ti+nOd\nICLf9Na/KyL/zzvuCyKypYg8KiJvichxgeOc5uma5mWadDhSwRl9x3KJiIwCvg1sAewNbIUp7d+r\nqlur6ghgOnCMqv4TeAA4VVVHquoM4Drgx2ryup+G6eayA2X7bqmq73jn8b8sFHhPVUdiOgOZgPky\n2BY4x9M6FthAVbcGRgKjRGSnRC+Iw+ERO+Gaw9Fk7IQx8AuBhSLyACDAZiLyW2AA0A/4e2AfARCR\nfsB2wJ9FxF9XLeOjVFj3gDd+BdNZznxgvogsEpEBmB6ixorIFG+7vsAGmP6THY5EcUbfsbyihBvi\n8cB+qvqKiByJ6Ys1uA+YL+AvvNJ5LeeLYpE3XgosDixfSvEZPF9Vr6vhfA5HXTj3jmN55SlgfxHp\n5aWz3tdb3h/40OuV6XCKxvorYAUAVf0SmCEiB4HpB0FENq9wrmX7ViHsJaTAI8DR/7+9O1ZNKAbD\nMPx+c2cnp/aC3JwcvQaXQufeiagX4OIqCIJoS+8nDjmCOshpKRSa9xkPJPmnj+QPnCR56tYbJhn0\nmE/6NkNf/1Ip5QgsgQ9gDeypAftGfZN4S+3pXyyAWZJDkmdgAkyTnIAvYPRgueuxL4/K4vZEULpa\nN8Ac2CX5BFbU1pP06/y1siQ1xJ2+JDXEi1yppySvwPju86qU8v4X9Ug/YXtHkhpie0eSGmLoS1JD\nDH1JaoihL0kNMfQlqSFnyzkdCBWy8LgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f208c8872d0>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Deux trac\u00e9s sur le m\u00eame axe: `twinx` (pb de grille non superpos\u00e9e)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp.plot()\n",
      "plt.twinx()\n",
      "co2.plot(style='r');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XiPHRuiOCZDMyu+wCj7o8FjNmODWZESN03vDhehnG6P877tBLE4Ua9L3yYqZL\ncNdk/vQnx8elmlp4qP+wMA/k888H7xMUKtvL5MkJ32mMo+I3v8lvf28brg4Mmjtbb61Lm/X1jpG5\n9978zpGNuPs7IP4ac9WXSxOSX8eYMIyMn0bTNOw3wHfsWP3suY0M6Bekd1ZXr5Exz2o+kcSD7uHq\n1br2f+ONTuHTO01GNpYvd2pcZtCkIQyfjJmTZ599nE4U8+cHn8d9X0x0gNpaaN3UgkP9h4nXt+DH\njTfmdq5LL83cRbo507Gj7gljGDDAv5CjVOE1JEv0mBd4jx65GYqxY9PzOnWKpiaT6X222Wa6IHjf\nfalG5bXXUo3K3/+ebmQ23TT7+XPF3YPUNOutWweXXJK5oOpm3Dh/YwjhRFLwq1llqm1deaVeLl5M\nMjK7bS4rihtuSE1vv72zbqZ6PfDA1LS3tBFMZeHCCsQ4291x14IoxpdQXa3/HIZnnnFKTN9/Dzvt\n5GybPr3gy8Te3wHx15iLvgcfzC8enQ67rzGjyIvBT2PfvqkFGTemiRdIay3wvqy96XPOCUcfaONq\nOve0bq111dbCyJF6lttsHHqofpkHBfIMwydTaJfoNL+V13EUA5qFkfEOPHT3LjNO8C23TE3HGfMM\n5Ftlz5fu3VO7TXfs6MxOuMkmsP/+qfuPHw/vvqs/lvjRs2d+tZFddnHWO3bUE/3lUrDJh+XLdRwy\nP9yGI5tRyXRssb3M1qxJ7UG6zTaOwf322/RmqeWeWMW/+Y32D82f7687DJ9MkJH59FOn4OylocHn\nHVJvazJ5c8wxui86OAH3fve79LD82R7aYBKFHlgww4bp5cMPZ983kUisf5CMIQ0Lb1fw4cP1/Or7\n7uvUeHIh7v4OiL/GXPR17Zq7kdl//9QSdqdO8MknjqO+EPw0Hndc5lqwMUDe67ZqldoBxXQT9uOm\nmwrXB+lGplMnx8h88YXuCeceYd+jh7N+8MH6O3z1FRx7bPE1mSCN3v+2uW877JA60Ns91mnsWB0R\nJU2MNTL58dxzTu3krLP08pBDWB/kzlC4kSk9W2+tl0GhI7zU1EC3brrUFSaZatVRjGi2FM4TT+Te\n5NW1q/7fuLu5mhptVKP+gzA9u0zUMEPr1qkdUDJF8Zg1qzgN3qgefvfRL0rIokW6du/2AZv3jPu7\nhOGTaddOzzdlCBq7N2yYHrQJusk7bfr5euv4L4rNNgve1qtXoWetLPTAosklXlplZSXvv5//PDS5\nkOme5VOjSEexAAAgAElEQVRriru/A+KvMZu+zTfP3kNswgQdx2/lSl0Sdjcdm5Lx2WcXPt1EIffQ\nPOPezjreZh5T8PLLmzkzt2sF6TvzTPjsMyfdvn169+hBg3TPTXcvOTO0wTQxg//7e86c3PRl0lhb\nmzrWyDu4FbS2Z591alqXXupzopbYXKaU2lIpVaWUmqWUmqmUuiiZf1Iyr1EptVu283z9dXBwwAUL\n4NprnfSDDzpNbHFlt91y79H10EPRaLjsMt3OPHFiNOe3FI8pMQ8fnr2H2L33wn/+A0OG6JfhI484\n3VvdnTxezRrSMHxMbzFTKzGDCl97Tb84P/oo/RiTV2ytesUKmDvXSf/kJ/5ROR58MDVtXvrbuqZX\n9MZYg/yaloPwGpnx44P3NeNo1q7VrgPQTY+zZtFiHf/1wCUiMgjYC7hAKbUjMAM4HvAJgJJO//7B\nk2317ZsaoG/IED0aN1ukVP0HTuRy+dDZfvvcxrwkEgnfGEZh0KYNbLGF8wLwkmvzXNz9HRB/jUH6\njNNbKV2TWbEi2HlvXj7GoHTs6PQwc9eaR4/OPeJEJo1ffKGXQc+PG6PZaDPO8uHD4cQT/QdEmxpE\nrkbG7x76xfgaODDV6Bi8Xb/NO8X93nE7+YcM0ct8Bo0G/c6LF6feA29UE3fTuluP+R133DF5b1ti\nTUZEFovItOT6amAO0EdE5orIZ5mPLoz+/XWba7aH35QKb06LNRo9b78Nf/1rbvtG3durTx//HkJ+\nVXZLaXHHGzPvjjFj/Pc1v1fPnpnP+dZb4dT0TQn/llsy73fyyXDKKU56v/2cTjy5UEwvzH//Wy/d\nU15svbW/D8hbI/Er1Lpr/Saywr//XXyvvY8/Tm9SvPpqZ30L11Rh7t/OvBsOPjiZEUPHf6knIesP\nfAN0duVVAbsF7B84wU82cpmcaNEikZ49C75EURx9dO6TJxUz0VKhlOOalnR69Ur9HUDk7rv99/3H\nP/T2N94IPt/CheH9tqV4RkCkXbvCjx85Up9j7drU/EMOSdW/446pk5rddpu/Fu/3ffxxnbdyZeEa\nRUR22UVk6tTga3qv/fzzTt5TT7kO2GILkW++8Z20rFyfkjn+lVKdgWeB34mu0ZSdVauC+/hHjaka\nZxtxHOW87Jb4Y3wqbi66KN1/AE6oEb/gqAZ3s7JShUcgD2q6joJCazJnneVM1OYdDOrtlWU6GZjW\nj1wnMTT30y9icq5UV+vu5Zl+Ny9ut0uKCyaGzWUl8RAppdoAY4B/ici4bPu7GTFiBP2T4YO7d+9O\nRUXF+h4apn3TLz1gAHz9dYJEwn87wMSJCdasmQZcnPV8Yaf1A5XgF7+A1auD93//fa1v881Lq0+T\n+f6525crKytLri+ftFdrufXko8/0gHSnf/1r2G671PPNnavTHTpkvt6kSZXstRdAItlknJvekSNH\npvz/IJH0HRT//TOlC9X32GPBx+vvnUhO/lfJI4/ArbcmGDYMjj66ktat/f8Pu++eer6ePaF160pW\nrMjt+0ybNo2LL05938ybp7dPnZpg0aLU/TfdFL7/XqePO875Px52GHTpkmDVKq3X7P/T2lraxMzx\nX4omMgWMAu4M2F4FDAnYFly/zMKwYboq2dQUvM+0aSJbb11V8DWK4dxztb6uXTPvN25clYDI9tuX\nRpchn6aQbPPTx4G4awzS5/0dMs1Tf/XVOn/27OzXK2S+e7dGc+wee+R+fCEU8xxm+o4DBwafF0Tu\nucc//7zz0vMrK0XefLMwjSIif/6zPveSJen7b7WV8x0efjh129ChPt+ha1eR5ctj1VxWCiOzL9AE\nTAOmJj+HA8cB84FaYDHwis+x6Xc9Rx55RH+7Z54J3mfKFJHddiv4EkUxYYLW17595v1MG/pjj5VE\n1nrMg+334FtKB4h06JCaNp+lS9P3zdU/UIiR8Tv+8ccLOz7f68yZU/ixRxyRvu2xx0T+9Cf/4wYP\nFvnkk/T8ww8Xef319Pzhw0VefTV/fV6dXr+RiMh994lsuaXe/sQTqdt2393n9+vYUWT16lgZmcjr\nVSIykeBebHk1neXDUUfp5YIFwfsERVUtBSa0TLb5Qerq9MBIM4dFqfnuu+y9lSzRcu65/vlBY2Zy\niVIuEo5fxW/OkygoZqzMSy+l52X6P338sX9+UEietm3DiZDhN+XI+efr/99JJ6X/Xr7TINgR/6XD\nONb9+skbGhthzZpESfQUysSJiRRnbakws3sG/eHcuP0JcSXuGv30mZdKUOcU95xJfi/SsDEaV66M\n/lpechnX476HpQzymo+R8f7O7vhtQZgOAd6OAe5IBOuJYRfmDdbImB8kk5EpZ00mV+rrKYuRmTVL\nT5/g17vJUlr+7/+c9QkTnHX37K/HHJP/eXMpQPgRxpTd+eIbQiUDZnLAf/0rfC1e2rQpvCZjZsqt\nqgre5/DDdey6E05IzX/2WU+vNlO1aRWv13q81ISIMeaZRss3NMCmm1aWRE+hVFRUlqVg0ru3juSb\nyzTWTu+b+BJ3jW59NTWpAwPdzSjDhjmTVM2erQcV1tRkLkwFMXiwfh/l2k3eaMx11tkwyaUbs/se\nfvWVXprpiqNko41yn6sn6DnM9Hi2agW/+lW67ejRwzNgOobdl2EDNjKGW28N3hbDMD9plKsmA7pt\nPxcjYwmXTp1SJxzzvlzMiO+zz9bRGtxjXcwo9Fxp1y7/UnhQDMEoMO/MbL5LNy+8UPwEbfnQ1FTc\nOJnQiGFTGWzgRmabbTJvb2iAH39MlESLHytXZh8MN2lSeXwykLuRibu/A+KvMR99O+8cvC1TocqP\ndu1yH+xYjntoDKDkELbF6DO1mFKx7ba5F1b97uGhh4YkJIZOf9jAjYx5OQfNodHYWF6fTOvWusQV\nNPMdlLdw0qWLbpoJex4bSzC77pqel8sLtlDC6hkVJ665prTXy8dQuzGTsbknSSsKW5MpPcbIvP66\n//aGBujVq7JkerwYA/fii8H77LRTZdlqMma+mX/+M/N+cfd3QPw1Gn2ffJK+LVcj454ELFfyeUF6\n72GmicbC5MQT4ZJLsu9n9JmmslI4/SE/Q+2+h9dfr5eZ5snKC1uTKT3GYRo0HqDcPhljZDIVPsrp\nk+nXTy9j+NxafPD2PsqF1q3zq8m4Q90XPlFgADU1viGd99ijsJpCKZz+oN8zuU6L7UcyalbxWMd/\n6fnVr/Tyl7/0397QAD/8kCiZHi/GyGQaFPfxx4myPTfG+ZwtiGjc/R0Qf42JRCJwxkrv3CJBmLFN\n+bBuXe4+jEQiYv9gIgFXXpmWPWGC09U38+GJ0CXlwoIFcP/9ue3rp3H//UMSYpvLSs/552feXu5x\nMkrBeedlLgU1NJSvJtOpk26miPtYog2FoEn2gjqH7L57aiB4v4m/srHbbsXN1xIqAe1v+XaZHjYM\n3ngjBD054jfLZq6ceqoz+VnR2Oay+NHYCH37VpZVQ01N5lJaly6VZX3Jd+mSfTR5Lv6OffeF994L\nR1MhNAefzIoVpb9up065d/cdNKgyUi28/75vtqlJZ3uZV1ZWsnatHthYiMEtlHz+n+Y5fPppne7Y\nMUQhtiZTHsyoZr9+9uX2yYCek93Me+7HmjXQrVvp9HgZNsx//vV8efddbWgswbi7ixcyPXIhdO6c\ne03h4Yed9UhG/QdU6Z98Ui+feir7KSZN0ss+fULSlAOFDLA3M4XeeWeIQjLUZJRSjyqlliilZrjy\neiilxiulPlNKva6U6u7adpVS6nOl1Fyl1HBX/hCl1IzktrtykbXBG5nBg3UXQb9SUEMDLF2aKLkm\nN9l6lkyfnmC77UqjxY+ddspeKszWFm56R5Uz2kVz8MmY5rKTTtJNKAccEP1186nJVFcn1q/reVVC\nZvly32wToDVbjcH9G8fVyHifw1yCmeZMZsf/Y8BhnrwrgfEish3wZjKNUmon4BRgp+Qx/1Bqvef4\nn8DZIrItsK1SynvONDZ4IwPauPt1w21oKH+Yn4su0ssg5/+nnxY+e2EYtG+vQ/PsuWfh5zBhS3yj\nxlrWY7raH5b82x57LMnJxaIjn5pMqE07fgQYGUMuNfpf/EIv/SIaR4VpiZ06Nbf9I/sfZGguE5F3\nAO8NPgZ4Irn+BHr6FYBjgdEiUi8i84AvgKFKqd5AFxGZnNxvlOuYQFqEkbnqKnzbuxsaYKutKkuu\nx80VV2Te3qVLJbvsUhotfphAo5maR7L5O+Iw2K85+GSamnST0Fln6bxLLgl0U4RGPjWZn/ykktNP\nj3BwaAYj87OfZW/arqysLNnYHTcnnqiXX3+dfV/jN4qE/B3/m4uIGaq+BDDBjPoA7klSFgB9ffKr\nk/kZaRFGZuON4Z57YP781PzGxvL7ZLIxZ05hXVPDIoz7Ewcj0xx4+unS11o7d4a//jU3w7F2bX7z\n0OdNBiPTrh188EGE1y4C0woxY0bm/QyPPRaRkCJ6l+nJ04ik+BC5kVFKbamUqlJKzVJKzVRKXZTM\nD3Q6uXG3YSYSiYLSZtDYaaelbv/00wRTp44s+vxRpauqEqxaNXL9YK1y6Hnrrez7m7yg7c4AvvLd\nT6/WUl8/F32Q4IMPcj9+5crir//ttzrd0JB9/1deGcn330fz/QESy5bhpFK3b7stTJmS+fiRI0cC\nwdujTt9wQ/b9R44cyYUXrs8J9fkZccstjJg7lxtuuIEcWaKU6gWQbAozsb+rgS1d+22BrsFUJ9fd\n+dVZrxL11JtAL6Aiud4Z+BTYEbgVuCKZ/wfgbz7Hps9HWgCffqpHEpx8cmr+n/8sctppVaFcoxjM\nSIemptT8mhqRNm2qyqLJTbZpeoPmpzfMn1/cVL9hkE1juXnjjSoBkeeey21/0NPvFsuzzwZP/evl\nnHOq5Iorir+mL/X1Wkjbtr6bX3pJ5LDDMp+iqqqqbM9Zrtd1awxd5/PPixx5ZFJP+vTLQH9ghit9\nK/CH5PqV5h2MdvhPA9oCA4AvAZXc9gEwFFDAy8Bh3ut4P5HXZERksYhMS66vBuag2/GCnE6hY0au\nm77phvp6GDiwMqrL5o13ANnq1dC1a2VZtLg5/vjM2zP5O665Rk8fbXjwQd28UKouuoa4+2R2370S\n0CX2XBgyRE9mVSzmeu+9l33fPn0qoxt/snx5xp4FuUQEb2ioDFdTnuTS1Ol+DvfZJ2QBGXqXKaVG\nA+8B2yul5iulzgT+BhyilPoMODCZRkRmA08Ds4FXgPOThgvgfOBh4HPgCxF5NZusknoklFL9gcFo\naxjkdAqdoF4pa9eWdwyKwcy3PmkSHHKIk79mTfaQLqVgzBjdy2zt2vwHuZna/XHHwbhxzgyAn3wS\nUTfYZsqaNXqiuB13zG3/sIy06VQyfboeE5WJ2toI/YM//KD7Ki9Y4Lu5S5fs0z5/+qleBpwiUv78\n5/znsAl9iugMRkZETg046uCA/W8GbvbJ/wjIMNlEOiVz/CulOgNjgN+JSEqZxFTvSqNDPwyjRulY\nfO6+/+XmxhtT03PmwDffJMqixY1S+tOnj39p0t1O7MWUkE048//8Ry9LPU98Jo1xYNSoRFl6Rhn+\n/e/s+0yenIjO8f/DD3rQWMAUn126aMf6mWcGn+LuuxPr9y01HTroDhTZQvSMGpUo/mJffeXfD7ol\nj/hXSrVBG5gnRWRcMjvI6ZTCiBEjuOGGG7jhhhsYOXJkwY4x3RSVABLceiuccYZOd+s2raDzRZFu\nbExNH3VUAt00Wn59hx4Ky5cnuPzy/I43jtju3cHcf4Attij//Y5T+qqrIExHcL7pKVOy779kyTQW\nLIhIz7Jl/NCqlTYyyZYZ93YdETzB448n1r/Ivef77LNpQGK9kSnl/TvqKK3vppsy73/GGfr//Npr\nRVxv4EB4/PG07XNnzGDx998TO7I5bYr9oB1Eo4A7Pfm+TifPPsU5wjwYZ9vvf19+R7SXc85J1xMn\njQ89pLXcdFN+x5nvcP31kuLwfPzxSGQ2W8r5W+d67YMOEhk/PiIRTz0lcsopIkqJNDb67mJ0Llrk\nf4py/19A5O67s+9TtEYQue++9PwHHxQ5++zkLumO/3J9SlGT+SlwOjBMKTU1+TmMAKdTlJyabJW8\n446or5Q/Bx6ol0ppv8zs2eXV42WHHfTyuuuC9zHNau6PwRvmY8SIzFMcWErPE09k3r56dYTjeGpq\ntOPf1GYy0Lt3RBpC4KKL4O67g7dvsklIF2rbNj2vpc4nIyITRaSViFSIyODk51URWSYiB4vIdiIy\nXER+jFrLU085ky5Nn66fZXd1s5z8/OfO+htvwNy5et3dPFVO9t1Xvwdat04fuJftHn7zDZxzjj6u\nocH5bqUkLr9zMImyXVkELr88u8N8yZJEdB1R3EYmIO5KtnAsffsmyvJsGd55Ry8zTTOw++4JHnkk\nhIv5ldBaqpGJG2ZAbLdu8S5Jm1AV5Y6t5qZDB/1Hd08XPWkSTJzo1BL96NjRudetW6cGOtS+CEu5\naWyEa6/NvE9tbYQ1mdpa/aC0bh1oTbL9F9atizgiQRZM5SKT83/hwgg7JrRkx3/cePBB7XiGeI2f\nOP309Lxrr60suY5sHHOMs7733vDHP1au7zXmh1/N3vC3yBtJNXH6nf2p5KGHynd104TsraW6aWqq\njL65bO3aIixZhON4csAUYCdODN6nsbEynGETQTWZGMbJapFG5pxz4jnbo5k3w00cxskUi9fIxLkG\nWS722Sf3MTJRkmnA4+rVET6PxsgUQW1teWsyphKRrVkvsmkIbHNZPIlbW72JU2aImz5DqmM/kXFf\n73PvnbPenCto+uEwiOt9NEyZkoi2aTTLIJxdd9XLoCkdmpqgtjbEcTLr1qVO8pSnkfHrG1Bbm4hF\nTSZTc9myZSHdQ+uTsRSKGWdVUxPP+Vcy9S6D9D//bbel1xo32wx+/ev0Y4uZK72506lTavidUKmr\ny1p8NnOhmFHzXkxLTGiG8JxzUrta5WhkPv9cL73/jcZG/eyVs7Uol2vX1UVY27JGJp7Era1eKf1H\n7tBBL+Omz9/wVa5f8xawgsL8+7X9R1mSz3Yf16zRoW8y+SSipE2byuhekGYCkwxfLlsTZl0dtGtX\nGZ6mmTNT0zkaGVOI8Tbr1ddD27aVZW2KzdYE39gIy5aF5Dfy+6JxmE/ehxZvZCz58ZvfZN/HjKkB\nZwIuL37NHWYGzXJw+unw3HNOSbnURFoIzcHIAPzylwRO9V1Xl7kDR954p+PM0cgMGKCXt96amh+6\nvgLIVkD55hu9jNTxb2sy8SPubfVx0+fX6nLBBYkUp/WcOXq5666sn8vHi98fMsrJzbLdRxPcsFwT\nrNXWJqI3MlkGOZ52WrpP0KDHlyXC01SgkTH3yJmjyEkrlQhHW4GYZ3rgQP/tNTXQv38iuk5H1shY\nNgT8ClC9eumuzO6R2JttBkOHBp9np53S83bZRZ/f+wIpBePH6+XOO8N++8Gxx5b2+o2NEb4fjCc6\ni5Hp1Alef92/W3ldXcgtMdmMTJZqgTdqR+j6cqG+Xs+VkNRqxr98+aXzPLmpqdEzfEaqJ4ZGJn4N\neCUmbj4PL3HWt2SJfjF16qTnp3e/w6qrM7dRX3IJXHihfxPH2rXh/1fyuY+ZxjlERVNTCXwyWYyM\n8RXcfjtceWXqtro66NKlMjxNfkbG7RE381/kSF0ddO5cGY62XKmpgS++0H+EXr3YfHPdcWfrrXW2\ne9oOs3uvXhFqtIMxLRsaPXs64+ZatUotSbZpk9mRr1Tw/2Hs2NLOCRLUo6pUiOiaTGRGxtRkstQO\njE9s+fL0baH7PERSJydasyZ1EGYWg+ilLD4ZU+V23TBTsPIbbxTqOB47GLP5EDefh5dQ9PXtCx9+\nWPx5kuy3Hynxl4rReOWVcO+9qXkjRoTfnTeTRndHhXJQXw+tWiWi6xlljEyWqKtmAjMT0shNfT3U\n1SXC1eUe2emNvpnFyHgnvKuvh/r6RHjacsFY5Zqa9VmmydjPyNTUwJo1iXCubR3/llixcKEOMhYS\nb78d3GssX/76V7jggnDO1VyJvABqmss++ijjbh07wsiRznTlbiLxebiNjKnJnHCCTmeodd1/Pwwe\nnK6v5O9XU5NxTYnZpo32FwXVZIr2yZj74nd/rJGJJ3H2eUD89UF0Gh9+OLxzFaJRqZRCamQ0NED7\n9pXRXcDUZGprs+5aXQ333KN9C27q6mCTTSrD1WWMjIhjZMxc3xlqMtOmwUMPpRbm6+pg441D1pcN\nn5oM6Pe8dxiQ2W3AgMrirmnuizUylthRrlGGOfKHP6TneZvRosavBP9j5BNQlLAmY5YZ+OILvTQR\nAAyh+jzMs2iax9at0zfAvCCzzCnj13OrrD4ZV00GYJtt/Md8heKTMaOhgwaaWZ9M/GgRPpmICUOj\nX+Dd6dOLPu16gjR+8omz3rNn+vYXXvAvlYZJfT2IJKK7gKnJ5BCnyER6qa5Oza+vh9WrE4VreP99\nPdoVnJ5lpiridfrX1GQMZOd+pxt7VV8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HHwbPWtexo14edJBu6zzlFB12Zvfdg8/Xr58O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\nS9cYd33J6yngKuCsZPoyYCRwgmufjugS4rHJdCd0zfwiYHh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       "text": [
        "<matplotlib.figure.Figure at 0x7f208c333650>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La temp\u00e9rature et le CO2, superpos\u00e9s: `subplots`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1, ax2) = plt.subplots(2,1, sharex=True)\n",
      "\n",
      "temp.plot(ax=ax1)\n",
      "\n",
      "co2.plot(ax=ax2, style='r')\n",
      "plt.xlim('2015-02-03','2015-02-04')\n",
      "ax2.set_ylim(200, 1200);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ssRar+PFH+Phje2BPnWpB5pdfLv+xPvnE9vPZZ/Zg7tzZrnWHDusmkJs82R7a8+aZ6yfp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\nw42D4ziOU4z/B9Mbu5zDiROuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f208c467d50>"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remarque: gr\u00e2ce au param\u00e8tre `sharex`, le zoom est synchronis\u00e9 (utile en mode interactif, avec `%matplotlib qt`)."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}