dcasmr/Worldcup2014Brazil.ipynb Secret

## Worldcup2014Brazil.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Passes</th>\n",
       "      <th>PassCompleted</th>\n",
       "      <th>Games</th>\n",
       "      <th>Avgpass</th>\n",
       "      <th>Avgpasscompleted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>950</td>\n",
       "      <td>604</td>\n",
       "      <td>3</td>\n",
       "      <td>317</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1181</td>\n",
       "      <td>799</td>\n",
       "      <td>3</td>\n",
       "      <td>394</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1208</td>\n",
       "      <td>878</td>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1375</td>\n",
       "      <td>1046</td>\n",
       "      <td>3</td>\n",
       "      <td>458</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1382</td>\n",
       "      <td>1021</td>\n",
       "      <td>3</td>\n",
       "      <td>461</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Passes  PassCompleted  Games  Avgpass  Avgpasscompleted\n",
       "0     950            604      3      317               201\n",
       "1    1181            799      3      394               266\n",
       "2    1208            878      3      403               293\n",
       "3    1375           1046      3      458               349\n",
       "4    1382           1021      3      461               340"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Author: Luc Zio\n",
    "   PythonStatistics.net\n",
    "   Data source: FIFA website, World Cup 2014, Brazil   \n",
    "'''\n",
    "df = pd.read_csv(\"AveragePass.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the average number of passes attempted in a game\n",
    "> To be used as a vertical line in the histogram of passes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "509.25"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OverallMean = df['Avgpass'].mean()\n",
    "OverallMean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iQ4cOSut17ty52LaqVauGatWqiY/t7Oywc+dOWFhY4ObNm4iKikJoaCjkcnmxnjoHBwfx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\n/vY3p8HMzmvLli2VmZmpBQsWaMKECRo/frwqVaqkCRMmqEKFCvmOF6T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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a44a59d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Set title and axes font sizes\n",
    "sns.set_context(\"notebook\", font_scale=1.5, rc={\"font.size\":16,\"axes.titlesize\":16,\"axes.labelsize\":16})\n",
    "## Set the overall figure size\n",
    "plt.rcParams[\"figure.figsize\"] = [12,8]\n",
    "## Set the background style\n",
    "sns.set_style(\"darkgrid\")\n",
    "## Plot the histogram withou a kde line as an overlay\n",
    "plt.title('Histogram distribution of Males life expectancy')\n",
    "sns.distplot(AveragePass, kde=False)\n",
    "plt.xlabel('Average number of passes attempted')\n",
    "## Draw a vertical line to illustrate the overall mean number of passes attempted in a game\n",
    "plt.axvline(OverallMean, color=\"k\", linestyle=\"--\") \n",
    "## Draw a blue arrow to illustrate the mean\n",
    "plt.annotate('Mean', xy=(OverallMean, 9), xytext=(600, 9),arrowprops=dict(facecolor='blue', shrink=0.05))\n",
    "plt.title('Histogram distribution of the average number of passes attemped\\n World cup 2014 Brazil')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}