Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -1) imports de librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0) imports de data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = \"/Users/romain/Downloads/formation_python-master/cyclistes.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.1) Vérifier que le chemin existe :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert(os.path.isfile(path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.2) créer la dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_csv(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) compter adultes Vs les mineurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexe</th>\n",
       "      <th>sportivite</th>\n",
       "      <th>age</th>\n",
       "      <th>sur_velo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "      <td>True</td>\n",
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       "      <th>105</th>\n",
       "      <td>H</td>\n",
       "      <td>-1.331255</td>\n",
       "      <td>36</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sexe  sportivite  age  sur_velo\n",
       "id                                 \n",
       "101    F   -0.285718   66      True\n",
       "102    H    2.219441   37      True\n",
       "103    F    2.637251   49      True\n",
       "104    F    1.413551   33      True\n",
       "105    H   -1.331255   36      True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adultes = df.age > 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mineurs = df.age < 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_mineurs = df[mineurs][\"age\"].count()\n",
    "nb_adultes = df[adultes][\"age\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 949)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_mineurs, nb_adultes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Ajouter une colonne dans la df en affectant un nom de colonne à une liste de valeur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cyclistes = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyclistes[\"est_adulte\"] =  adultes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexe</th>\n",
       "      <th>sportivite</th>\n",
       "      <th>age</th>\n",
       "      <th>sur_velo</th>\n",
       "      <th>est_adulte</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>F</td>\n",
       "      <td>-0.285718</td>\n",
       "      <td>66</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>H</td>\n",
       "      <td>2.219441</td>\n",
       "      <td>37</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>F</td>\n",
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       "      <td>H</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sexe  sportivite  age  sur_velo  est_adulte\n",
       "id                                             \n",
       "101    F   -0.285718   66      True        True\n",
       "102    H    2.219441   37      True        True\n",
       "103    F    2.637251   49      True        True\n",
       "104    F    1.413551   33      True        True\n",
       "105    H   -1.331255   36      True        True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyclistes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) compter la moyenne des sportivite par sexe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1) solution 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.055555555555557"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyclistes[mineurs][\"age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.04531085353003"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyclistes[adultes][\"age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1.2) solution 1 bis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "majorite: 16.055555555555557\n",
      "majorite: 50.04531085353003\n"
     ]
    }
   ],
   "source": [
    "for majorite in [mineurs, adultes]:\n",
    "    print(\"majorite: %s\"%(cyclistes[majorite][\"age\"].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2) avec un group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "est_adulte\n",
       "False    16.627451\n",
       "True     50.045311\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyclistes.groupby([\"est_adulte\"])[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.3) dessiner le résultat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11172b748>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wti8OLe8OPId7Pi9BO4Ej9HkmyQZgBbAc+ByDO1w+vKqe3mdd0rAki4CvVdVv9V3LQuIf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\nEQa6JDXCQJekRhjoktSI/wOBXSdv/3fYHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111776320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "cyclistes.groupby([\"est_adulte\"])[\"age\"].mean().plot.bar(title=\"age moyen\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Calculer et afficher, le min, max et mean par majorite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grouped  = cyclistes.groupby([\"est_adulte\"])[\"age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111717ba8>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fAi6rqnnA64H3JZkHLAVWVdUcYFWzLknqkWEDvao2V9X3muVfAQ8ARwBnAiuaZiuAs7pV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\n+P+VaFcpnpl0UgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ade6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped.agg([\"min\", \"mean\",  \"max\"]).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_sportivite = cyclistes[[\"age\", \"sportivite\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1116157b8>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xumo8vgVmWiRp6mZUWWBpudx0mNQCIanfblX/IG792HHYMLgLZ7zlYBx1yHTc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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ea2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cyclistes.plot.scatter(x=\"age\", y=\"sportivite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid RGBA argument: 's'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ('s', None)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-dc6c1887d6aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcyclistes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"age\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sportivite\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sexe\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, **kwds)\u001b[0m\n\u001b[1;32m   2796\u001b[0m         \u001b[0maxes\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAxesSubplot\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2797\u001b[0m         \"\"\"\n\u001b[0;32m-> 2798\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'scatter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2800\u001b[0m     def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   2618\u001b[0m                           \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2619\u001b[0m                           \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2620\u001b[0;31m                           sort_columns=sort_columns, **kwds)\n\u001b[0m\u001b[1;32m   2621\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_frame\u001b[0;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   1855\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1856\u001b[0m                  \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1857\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   1858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1859\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   1680\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1682\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1683\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1684\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    830\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m         scatter = ax.scatter(data[x].values, data[y].values, c=c_values,\n\u001b[0;32m--> 832\u001b[0;31m                              label=label, cmap=cmap, **self.kwds)\n\u001b[0m\u001b[1;32m    833\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    834\u001b[0m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4032\u001b[0m                 \u001b[0moffsets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4033\u001b[0m                 \u001b[0mtransOffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'transform'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4034\u001b[0;31m                 \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4035\u001b[0m                 )\n\u001b[1;32m   4036\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentityTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, paths, sizes, **kwargs)\u001b[0m\n\u001b[1;32m    900\u001b[0m         \"\"\"\n\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 902\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    903\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, edgecolors, facecolors, linewidths, linestyles, antialiaseds, offsets, transOffset, norm, cmap, pickradius, hatch, urls, offset_position, zorder, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hatch_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hatch.color'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfacecolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_facecolor\u001b[0;34m(self, c)\u001b[0m\n\u001b[1;32m    679\u001b[0m         \"\"\"\n\u001b[1;32m    680\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_facecolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_facecolors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m_set_facecolor\u001b[0;34m(self, c)\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 666\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_facecolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    667\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/romain/anaconda/envs/python35/lib/python3.5/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid RGBA argument: {!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_c\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m     \u001b[0;31m# tuple color.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: 's'"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112063358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cyclistes.plot.scatter(x=\"age\", y=\"sportivite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}