{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-notebook\")\n",
    "import seaborn as sns\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wspeed = pd.DataFrame(np.random.normal(7,7, 365)).abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.078493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.286526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.003320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.400571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.234191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.713693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "count  365.000000\n",
       "mean     8.078493\n",
       "std      5.286526\n",
       "min      0.010161\n",
       "25%      4.003320\n",
       "50%      7.400571\n",
       "75%     11.234191\n",
       "max     26.713693"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wspeed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.countplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cate(ws):\n",
    "    if  ws[0]  < 5:\n",
    "        return \"Pas assez de vent\"\n",
    "    elif ws[0] < 10:\n",
    "        return \"Peu de vent\"\n",
    "    elif ws[0] < 15:\n",
    "        return \"Vent léger\"\n",
    "    elif ws[0] < 20:\n",
    "        return \"Bon vent\"\n",
    "    elif ws[0] < 25:\n",
    "        return \"Vent fort\"\n",
    "    else:\n",
    "        return \"Trop de vent\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wspeed[\"cat\"] = wspeed.apply(cate, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.YTick at 0x7f17a6895908>,\n",
       " <matplotlib.axis.YTick at 0x7f17a68f3b00>,\n",
       " <matplotlib.axis.YTick at 0x7f17a690c710>,\n",
       " <matplotlib.axis.YTick at 0x7f17a687a7b8>,\n",
       " <matplotlib.axis.YTick at 0x7f17a6882128>,\n",
       " <matplotlib.axis.YTick at 0x7f17a686e8d0>,\n",
       " <matplotlib.axis.YTick at 0x7f17a6882b38>,\n",
       " <matplotlib.axis.YTick at 0x7f17a6885630>,\n",
       " <matplotlib.axis.YTick at 0x7f17a688e128>,\n",
       " <matplotlib.axis.YTick at 0x7f17a688ebe0>,\n",
       " <matplotlib.axis.YTick at 0x7f17a68946d8>,\n",
       " <matplotlib.axis.YTick at 0x7f17a681a1d0>,\n",
       " <matplotlib.axis.YTick at 0x7f17a681ac88>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
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L5/v27YObmxs6d+6MrKwshIaGAgBCQ0ORmZnZLH0QERE9Cgxe+tXa2hqVlZX6QC8sLISV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17a72b9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=\"cat\", data=wspeed, color=\"grey\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Nombre de jours\")\n",
    "ax.set_yticks(np.arange(0, 130, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    }
   ],
   "source": [
    "ax.figure.savefig(\"./fig/cat_vent.svg\")"
   ]
  },
  {
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}