{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import rand, randint\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab \n",
    "import scipy.stats as stats\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#Données NBA : nombre de points marqués par les 9 meilleurs joueurs 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names_data = ['Carmelo<br>Anthony', 'Dwyane<br>Wade',\n",
    "          'Deron<br>Williams', 'Brook<br>Lopez',\n",
    "          'Damian<br>Lillard', 'David<br>West',\n",
    "          'Blake<br>Griffin', 'David<br>Lee',\n",
    "          'Demar<br>Derozan']\n",
    "\n",
    "def g(name):\n",
    "    return name.split('<br>')[1]\n",
    "\n",
    "names_data = [g(ele) for ele in names_data]\n",
    "    \n",
    "\n",
    "scores_data = [\n",
    "    ['30', '27', '21', '31', '25', '9', '20', '26', '29', '23', '37', '29','35', '29', '20', '34', '23', '34', '45', '30', '31', '29', '33', '34', '45', '23', '40', '20', '39', '27', '26', '29', '28', '25', '42', '20', '25', '9', '27', '31', '36', '42', '12', '15', '32', '29', '35', '30', '32', '6', '14', '9', '21', '37', '28', '29', '22', '32', '24', '50', '40', '41', '36', '36', '36', '31', '25', '36', '34', '26', '36', '22', '21', '27', '32', '21', '24', '28', '39'],\n",
    "    ['29', '15', '14', '22', '22', '8', '19', '6', '28', '18', '19', '34', '24', '13', '26', '26', '14', '13', '24', '19', '21', '21', '29', '24', '21', '27', '22', '14', '30', '18', '11', '11', '15', '27', '35', '29', '17', '21', '17', '23', '20', '31', '20', '30', '24', '13', '20', '17', '33', '24', '39', '22', '20', '32', '24', '22', '23', '23', '21', '20', '24', '16', '11', '19', '18', '17', '11', '22', '21', '16', '21', '4', '14', '15', '10', '6', '18', '19', '14', '18', '16', '10', '10', '21', '17', '10', '16', '32', '25', '14', '23'],\n",
    "    ['19', '18', '14', '12', '17', '26', '24', '14', '22', '9', '11', '15', '16', '8', '11', '10', '33', '23', '18', '18', '12', '17', '24', '14', '16', '16', '10', '19', '15', '8', '19', '24', '15', '22', '15', '22', '21', '12', '24', '14', '18', '12', '27', '20', '9', '11', '15', '12', '20', '15', '19', '23', '15', '24', '33', '24', '14', '20', '42', '17', '27', '21', '18', '31', '31', '18', '20', '6', '19', '21', '24', '30', '32', '11', '29', '33', '30', '8', '22', '8', '18', '32', '23', '17', '24'],\n",
    "    ['27', '13', '8', '14', '20', '23', '24', '13', '23', '22', '26', '15', '22', '9', '9', '18', '16', '16', '14', '15', '21', '26', '35', '11', '25', '27', '18', '13', '13', '15', '22', '22', '20', '14', '22', '18', '21', '16', '21', '20', '30', '17', '13', '18', '25', '23', '19', '9', '27', '9', '20', '19', '22', '16', '11', '18', '19', '26', '17', '18', '38', '18', '21', '28', '11', '27', '14', '28', '19', '29', '21', '24', '16', '20', '21', '21', '22', '26', '28', '17', '21'],\n",
    "    ['23', '21', '20', '13', '16', '20', '12', '22', '27', '16', '24', '28', '13', '12', '20', '8', '24', '24', '23', '12', '9', '29', '16', '12', '25', '15', '17', '11', '20', '21', '18', '11', '20', '18', '10', '37', '9', '16', '13', '26', '18', '20', '20', '16', '10', '26', '23', '21', '19', '18', '4', '33', '12', '24', '19', '12', '26', '24', '20', '20', '35', '20', '27', '26', '14', '27', '8', '24', '22', '19', '15', '24', '16', '17', '17', '11', '15', '38', '10', '30', '12', '21'],\n",
    "    ['25', '14', '18', '10', '20', '19', '11', '11', '7', '15', '14', '30', '16', '22', '16', '31', '23', '10', '16', '18', '21', '18', '14', '23', '12', '13', '15', '25', '14', '29', '13', '8', '10', '10', '14', '6', '14', '27', '15', '14', '20', '14', '21', '24', '17', '14', '30', '29', '15', '15', '30', '7', '18', '18', '16', '28', '22', '15', '31', '11', '9', '24', '13', '8', '18', '7', '14', '16', '17', '12', '15', '26', '17', '13', '7', '18', '15', '24', '21', '20', '13', '11', '10', '17', '17', '26', '13', '21', '14', '17', '11', '14'],\n",
    "    ['11', '15', '19', '20', '22', '7', '16', '20', '26', '16', '23', '14', '22', '4', '18', '14', '30', '19', '24', '19', '22', '19', '18', '15', '18', '21', '23', '13', '15', '22', '7', '12', '10', '24', '20', '15', '30', '10', '19', '20', '17', '26', '31', '12', '24', '23', '26', '17', '20', '13', '17', '20', '20', '22', '17', '18', '24', '18', '16', '20', '23', '17', '22', '22', '12', '26', '7', '11', '14', '19', '18', '12', '17', '9', '24', '19', '20', '12', '16', '7', '10', '21', '16', '19', '4', '9'],\n",
    "    ['6', '16', '8', '20', '22', '10', '18', '18', '18', '19', '17', '20', '21', '17', '31', '13', '22', '20', '30', '24', '25', '22', '24', '20', '26', '29', '23', '20', '18', '25', '20', '24', '10', '14', '24', '23', '12', '22', '12', '22', '23', '12', '21', '20', '15', '16', '18', '12', '26', '10', '12', '18', '19', '25', '22', '12', '10', '13', '29', '17', '16', '21', '20', '15', '10', '20', '10', '11', '23', '20', '13', '23', '22', '21', '15', '13', '19', '12', '20', '10', '0', '5', '0', '6', '9'],\n",
    "    ['10', '25', '22', '8', '24', '19', '37', '15', '10', '20', '24', '14', '5', '29', '12', '16', '23', '18', '14', '17', '24', '20', '11', '14', '19', '16', '23', '17', '8', '30', '21', '24', '14', '11', '19', '6', '23', '12', '18', '8', '8', '12', '22', '15', '21', '23', '19', '27', '13', '22', '7', '22', '20', '24', '7', '18', '25', '34', '9', '18', '15', '15', '28', '13', '17', '9', '12', '19', '18', '17', '14', '21', '15', '15', '25', '25', '12', '20', '19', '36', '30', '24']\n",
    "]\n",
    "\n",
    "def f(list0):\n",
    "    list1 = [float(x) for x in list0]\n",
    "    return list1\n",
    "\n",
    "scores_data = [f(ele) for ele in scores_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1159a5dd0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dWnvyeP83JXlxkv+T4Vqo705ygxOup8tcLJ/vyFAwf/EY30Mz/C0vNoK5uF69MsO6ctND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\nPRTlISbddAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f30cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,1, figsize = (15,10))\n",
    "axes.boxplot(scores_data, labels = names_data, whis=1.5) #whis = 1.5 determines the reach of the whiskers past the first and third quartiles (e.g., Q3 + whis*IQR, IQR = interquartile range, Q3-Q1)\n",
    "axes.set_title('Points Scored by the Top 9 Scoring NBA Players in 2012')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# qqplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HsqMSEamVpE31mNlSYANQCmx1917JiiUmX3wR3IzVpg3Mng377JPs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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106b523d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#measurements = np.random.normal(loc = 20, scale = 5, size=100) #normal data\n",
    "#measurements = np.random.uniform(0,1, size=100) # uniform data\n",
    "#measurements = np.random.gamma(shape = 2, scale = 1, size=100) # gamma data\n",
    "nb_player=3\n",
    "measurements, text_title  =  scores_data[nb_player], 'Player ' + names_data[nb_player]  \n",
    "stats.probplot(measurements, dist=\"norm\", plot=plt) # compare with the theoretical Gaussian quantile\n",
    "plt.title(text_title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}