dcasmr/WorldCupAttendance.ipynb

## WorldCupAttendance.ipynb
{
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  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Author: Luc Zio\n",
    "PythonStatistics.net\n",
    "Data source: FIFA.com, World Cup attendance   \n",
    "'''\n",
    "df = pd.read_csv(\"Worldcupstats1970.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a Venu column made of the Year of Worldcup and the country\n",
    "df['Venue'] = df['Year'].astype(str) + '-' + df['Country']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Teams</th>\n",
       "      <th>Matches_Played</th>\n",
       "      <th>Goals_scored</th>\n",
       "      <th>Average_Goals</th>\n",
       "      <th>Avg_attendance</th>\n",
       "      <th>Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>171</td>\n",
       "      <td>2.7</td>\n",
       "      <td>52,918</td>\n",
       "      <td>2014-Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>145</td>\n",
       "      <td>2.3</td>\n",
       "      <td>49,669</td>\n",
       "      <td>2010-South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>Germany</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>147</td>\n",
       "      <td>2.3</td>\n",
       "      <td>52,491</td>\n",
       "      <td>2006-Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>Korea/Japan</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>161</td>\n",
       "      <td>2.5</td>\n",
       "      <td>42,268</td>\n",
       "      <td>2002-Korea/Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>France</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>171</td>\n",
       "      <td>2.7</td>\n",
       "      <td>43,517</td>\n",
       "      <td>1998-France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year       Country  Teams  Matches_Played  Goals_scored  Average_Goals  \\\n",
       "0  2014        Brazil     32              64           171            2.7   \n",
       "1  2010  South Africa     32              64           145            2.3   \n",
       "2  2006       Germany     32              64           147            2.3   \n",
       "3  2002   Korea/Japan     32              64           161            2.5   \n",
       "4  1998        France     32              64           171            2.7   \n",
       "\n",
       "  Avg_attendance              Venue  \n",
       "0         52,918        2014-Brazil  \n",
       "1         49,669  2010-South Africa  \n",
       "2         52,491       2006-Germany  \n",
       "3         42,268   2002-Korea/Japan  \n",
       "4         43,517        1998-France  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove the commas from the avg_attendance columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Average_Attendance'] = df['Avg_attendance'].str.replace(',','') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert the average attendance from text to a number (integer)\n",
    "df['Average_Attendance'] = df['Average_Attendance'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# drop the avg_attendance columns with commas\n",
    "df.drop(['Avg_attendance'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Teams</th>\n",
       "      <th>Matches_Played</th>\n",
       "      <th>Goals_scored</th>\n",
       "      <th>Average_Goals</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Average_Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>171</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2014-Brazil</td>\n",
       "      <td>52918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>145</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2010-South Africa</td>\n",
       "      <td>49669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>Germany</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>147</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2006-Germany</td>\n",
       "      <td>52491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>Korea/Japan</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>161</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2002-Korea/Japan</td>\n",
       "      <td>42268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>France</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>171</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1998-France</td>\n",
       "      <td>43517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year       Country  Teams  Matches_Played  Goals_scored  Average_Goals  \\\n",
       "0  2014        Brazil     32              64           171            2.7   \n",
       "1  2010  South Africa     32              64           145            2.3   \n",
       "2  2006       Germany     32              64           147            2.3   \n",
       "3  2002   Korea/Japan     32              64           161            2.5   \n",
       "4  1998        France     32              64           171            2.7   \n",
       "\n",
       "               Venue  Average_Attendance  \n",
       "0        2014-Brazil               52918  \n",
       "1  2010-South Africa               49669  \n",
       "2       2006-Germany               52491  \n",
       "3   2002-Korea/Japan               42268  \n",
       "4        1998-France               43517  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sort data by descending order of prestige score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.sort_values('Average_Attendance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Teams</th>\n",
       "      <th>Matches_Played</th>\n",
       "      <th>Goals_scored</th>\n",
       "      <th>Average_Goals</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Average_Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1994</td>\n",
       "      <td>USA</td>\n",
       "      <td>24</td>\n",
       "      <td>52</td>\n",
       "      <td>141</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1994-USA</td>\n",
       "      <td>68991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>171</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2014-Brazil</td>\n",
       "      <td>52918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>Germany</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>147</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2006-Germany</td>\n",
       "      <td>52491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1970</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>95</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1970-Mexico</td>\n",
       "      <td>50124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>145</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2010-South Africa</td>\n",
       "      <td>49669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year       Country  Teams  Matches_Played  Goals_scored  Average_Goals  \\\n",
       "5   1994           USA     24              52           141            2.7   \n",
       "0   2014        Brazil     32              64           171            2.7   \n",
       "2   2006       Germany     32              64           147            2.3   \n",
       "11  1970        Mexico     16              32            95            3.0   \n",
       "1   2010  South Africa     32              64           145            2.3   \n",
       "\n",
       "                Venue  Average_Attendance  \n",
       "5            1994-USA               68991  \n",
       "0         2014-Brazil               52918  \n",
       "2        2006-Germany               52491  \n",
       "11        1970-Mexico               50124  \n",
       "1   2010-South Africa               49669  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H0cb9+/cDULFixRSdX0TEjMKSiIi89AICAgC4evUqv//+O7///jthYWHJrlRX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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x296995ea940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [12,10]\n",
    "#paper, notebook, talk, and poster\n",
    "sns.set_context(\"notebook\", font_scale=1.5, rc={\"font.size\":16,\"axes.titlesize\":16,\"axes.labelsize\":16})\n",
    "#set the chart background style \n",
    "sns.set_style(\"darkgrid\")\n",
    "# Plot the world cup attendance data\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=\"Average_Attendance\", y=\"Venue\", data=df, color=\"b\")\n",
    "plt.title('FIFA World cup attendance from 1970-2014 \\n Data from FIFA.COM')\n",
    "plt.ylabel('Countries and dates')\n",
    "plt.xlabel('Average attendance per match')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}