Lab W4_NkechiIregbulem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p class=\"pull-left\">\n",
    "COMP 061 - Programming for Data Science\n",
    "</p>\n",
    "<div class=\"pull-right\">\n",
    "<ul class=\"list-inline\">\n",
    "Copyright © Dana Nehoran 2017\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <div style=\"background-color: #FFF8C6; border:1px solid #000; margin-left: 2px; margin-right: 2px; \n",
    "\n",
    "padding-bottom: 2px; padding-left: 2px; padding-right: 1px; padding-top: 10px;\">\n",
    "<center><b><font size=\"6\">Lab Week 4</font></b></center>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparation for pandas and NumPy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "\n",
    "# Set some pandas options\n",
    "pd.set_option('display.notebook_repr_html', False)\n",
    "pd.set_option('display.max_rows', 60)\n",
    "pd.set_option('display.max_columns', 60)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "# Preparation for matplotlib\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class color:\n",
    "   PURPLE = '\\033[95m'\n",
    "   CYAN = '\\033[96m'\n",
    "   DARKCYAN = '\\033[36m'\n",
    "   BLUE = '\\033[94m'\n",
    "   GREEN = '\\033[92m'\n",
    "   YELLOW = '\\033[93m'\n",
    "   RED = '\\033[91m'\n",
    "   BOLD = '\\033[1m'\n",
    "   UNDERLINE = '\\033[4m'\n",
    "   END = '\\033[0m'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data: `Rio 2016 Olympic Athletes.xlsx`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<blockquote style=\"border: 2px solid #666; padding: 10px; background-color: #99e6ff\"><b> Question 1:</b> Data Exploration\n",
    "<div class=\"pull-right\">\n",
    "Practice for HW question 1\n",
    "</div>\n",
    "</blockquote>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><font color=\"blue\", size = 4>a)</font></b> Explore your data in 10 steps and draw some conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"background-color: #FFFF00\">Your answer here</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11538, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes = pd.read_excel('Rio 2016 Olympic Athletes.xlsx')\n",
    "athletes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 11538 Athletes with 14 fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'name', 'first', 'last', 'nationality', 'gender', 'dob', 'age', 'height', 'weight', 'sport', 'gold', 'silver', 'bronze'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['athletics', 'fencing', 'taekwondo', 'cycling', 'triathlon',\n",
       "       'volleyball', 'aquatics', 'rugby sevens', 'wrestling', 'football',\n",
       "       'shooting', 'boxing', 'equestrian', 'rowing', 'judo', 'handball',\n",
       "       'badminton', 'hockey', 'modern pentathlon', 'table tennis',\n",
       "       'canoe', 'basketball', 'golf', 'archery', 'weightlifting',\n",
       "       'sailing', 'tennis', 'gymnastics'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes.sport.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes.sport.unique().shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 28 sports being played in the 2016 Rio Olympic Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ESP', 'KOR', 'CAN', 'MDA', 'NZL', 'AUS', 'USA', 'ETH', 'BRN',\n",
       "       'IOA', 'GBR', 'UZB', 'RSA', 'EGY', 'MAR', 'QAT', 'SUD', 'ALG',\n",
       "       'DEN', 'NED', 'DJI', 'SEN', 'CMR', 'ITA', 'NIG', 'SWE', 'GHA',\n",
       "       'AFG', 'AZE', 'KSA', 'BAN', 'NGR', 'RUS', 'IND', 'HUN', 'KAZ',\n",
       "       'BDI', 'ERI', 'POL', 'BRA', 'GEO', 'CZE', 'SEY', 'GAM', 'LTU',\n",
       "       'IRI', 'ROU', 'CUB', 'SLO', 'BAH', 'ARG', 'PUR', 'FRA', 'RWA',\n",
       "       'TOG', 'MDV', 'TUN', 'ISR', 'LAT', 'JOR', 'MAS', 'LIB', 'LBA',\n",
       "       'PLE', 'IRQ', 'TUR', 'VEN', 'JPN', 'TPE', 'KGZ', 'CHN', 'MEX',\n",
       "       'GRE', 'IRL', 'JAM', 'SUI', 'BAR', 'HON', 'ANG', 'GER', 'COL',\n",
       "       'URU', 'MNE', 'SRB', 'BUL', 'FIN', 'UKR', 'BLR', 'SMR', 'COK',\n",
       "       'SAM', 'AUT', 'BEL', 'KEN', 'SVK', 'POR', 'ECU', 'UAE', 'NAM',\n",
       "       'GUY', 'EST', 'SKN', 'ARU', 'PAN', 'PER', 'TAN', 'FIJ', 'GUI',\n",
       "       'NOR', 'ARM', 'THA', 'SIN', 'TKM', 'CRO', 'BIH', 'TGA', 'MAW',\n",
       "       'DOM', 'GUA', 'MKD', 'TJK', 'CYP', 'CHI', 'MLT', 'ZIM', 'TTO',\n",
       "       'CRC', 'BOL', 'BUR', 'ISL', 'HAI', 'ROT', 'MGL', 'MAD', 'GAB',\n",
       "       'SRI', 'LAO', 'MRI', 'PAR', 'IVB', 'GBS', 'TLS', 'MLI', 'SYR',\n",
       "       'BOT', 'CHA', 'OMA', 'COD', 'CIV', 'GEQ', 'UGA', 'GUM', 'ASA',\n",
       "       'ZAM', 'VAN', 'GRN', 'BIZ', 'VIN', 'MON', 'ALB', 'STP', 'SLE',\n",
       "       'BER', 'HKG', 'ISV', 'CGO', 'ANT', 'BEN', 'LUX', 'PHI', 'CAF',\n",
       "       'PRK', 'LIE', 'MHL', 'NCA', 'KIR', 'CPV', 'INA', 'FSM', 'COM',\n",
       "       'PLW', 'MYA', 'NRU', 'LBR', 'ESA', 'AND', 'TUV', 'SUR', 'CAY',\n",
       "       'NEP', 'PAK', 'SSD', 'MTN', 'VIE', 'MOZ', 'LES', 'LCA', 'BRU',\n",
       "       'SOL', 'BHU', 'CAM', 'KOS', 'SOM', 'PNG', 'YEM', 'SWZ', 'DMA'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes.nationality.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(207,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes.nationality.unique().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 207 countries/nationalities participating in the 2016 Rio Olympic Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           first        last  age         sport\n",
       "6          Aaron     Russell   25    volleyball\n",
       "13         Abbey  D'Agostino   26     athletics\n",
       "14         Abbey    Weitzeil   22      aquatics\n",
       "64       Abigail    Johnston   29      aquatics\n",
       "104      Adeline        Gray   27     wrestling\n",
       "136     Adrienne    Martelli   31        rowing\n",
       "229         Ajee      Wilson   24     athletics\n",
       "232      Akalani  Baravilala   27  rugby sevens\n",
       "334       Alesha     Widdall   28        hockey\n",
       "348         Alev      Kelter   27  rugby sevens\n",
       "352         Alex       Bowen   25      aquatics\n",
       "361         Alex      Morgan   29      football\n",
       "362         Alex       Obert   27      aquatics\n",
       "364         Alex      Roelse   23      aquatics\n",
       "385    Alexander    Karwoski   28        rowing\n",
       "391    Alexander   Massialas   24       fencing\n",
       "394    Alexander     Naddour   27    gymnastics\n",
       "418    Alexandra     Raisman   24    gymnastics\n",
       "466          Ali     Krieger   34      football\n",
       "514        Alisa        Kano   24    gymnastics\n",
       "516        Alise        Post   27       cycling\n",
       "517       Alisha       Glass   30    volleyball\n",
       "535        Allie        Long   31      football\n",
       "537      Allison       Brock   39    equestrian\n",
       "539      Allison     Schmitt   28      aquatics\n",
       "542      Allyson       Felix   33     athletics\n",
       "568       Alyssa      Manley   24        hockey\n",
       "569       Alyssa      Naeher   30      football\n",
       "584       Amanda      Elmore   27        rowing\n",
       "589       Amanda        Polk   32        rowing\n",
       "...          ...         ...  ...           ...\n",
       "10470     Tianna  Bartoletta   33     athletics\n",
       "10516    Timothy        Wang   27  table tennis\n",
       "10521       Tina     Charles   30    basketball\n",
       "10554      Tobin       Heath   30      football\n",
       "10572        Tom     Shields   27      aquatics\n",
       "10602       Tony     Azevedo   37      aquatics\n",
       "10605       Tony      McQuay   28     athletics\n",
       "10611       Tori       Bowie   28     athletics\n",
       "10617    Townley        Haas   22      aquatics\n",
       "10619      Tracy      Eisser   29        rowing\n",
       "10622     Travis     Stevens   32          judo\n",
       "10623    Trayvon     Bromell   23     athletics\n",
       "10662      Tyler        Nase   28        rowing\n",
       "10667      Tyson         Gay   36     athletics\n",
       "10761     Vashti  Cunningham   20     athletics\n",
       "10780      Venus    Williams   38        tennis\n",
       "10819   Victoria     Folayan   33  rugby sevens\n",
       "10868    Vincent     Hancock   29      shooting\n",
       "10884   Virginia    Thrasher   21      shooting\n",
       "10949     Walton       Eller   36      shooting\n",
       "10993    Whitney      Ashley   29     athletics\n",
       "10994    Whitney       Engen   31      football\n",
       "11000       Will       Brown   27      shooting\n",
       "11001       Will       Claye   27     athletics\n",
       "11018    William      Priddy   41    volleyball\n",
       "11242      Yijun        Feng   21  table tennis\n",
       "11339        Yue          Wu   28  table tennis\n",
       "11436       Zach     Garrett   23       archery\n",
       "11437       Zach      Ziemek   25     athletics\n",
       "11439       Zack        Test   29  rugby sevens\n",
       "\n",
       "[567 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USA_athletes = athletes[athletes.nationality == 'USA']\n",
    "USA_athletes[[\"first\", \"last\", \"age\", \"sport\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 567 Americans participating in the 2016 Rio Olympic Games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nationality\n",
       "USA    567\n",
       "BRA    485\n",
       "GER    441\n",
       "AUS    431\n",
       "FRA    410\n",
       "CHN    404\n",
       "GBR    374\n",
       "JPN    346\n",
       "CAN    321\n",
       "ESP    313\n",
       "ITA    312\n",
       "RUS    286\n",
       "NED    249\n",
       "POL    242\n",
       "ARG    223\n",
       "KOR    213\n",
       "NZL    208\n",
       "UKR    205\n",
       "SWE    164\n",
       "HUN    154\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes.groupby(athletes.nationality).size().sort_values(ascending = False)[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "USA & Brazil have the most participating athletes in the 2016 Rio Olympic Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age  sport            \n",
       "16   aquatics               8\n",
       "17   aquatics               6\n",
       "     table tennis           1\n",
       "18   aquatics              23\n",
       "     athletics              3\n",
       "     fencing                1\n",
       "     football               1\n",
       "     gymnastics            11\n",
       "     shooting               1\n",
       "     table tennis           4\n",
       "     weightlifting          1\n",
       "19   aquatics              38\n",
       "     archery                2\n",
       "     athletics              8\n",
       "     fencing                1\n",
       "     football               2\n",
       "     gymnastics            21\n",
       "     modern pentathlon      1\n",
       "     sailing                2\n",
       "     shooting               4\n",
       "     taekwondo              1\n",
       "     weightlifting          2\n",
       "20   aquatics              52\n",
       "     archery                4\n",
       "     athletics             24\n",
       "     boxing                 2\n",
       "     canoe                  2\n",
       "     cycling                1\n",
       "     equestrian             1\n",
       "     fencing                1\n",
       "                         ... \n",
       "23   weightlifting         17\n",
       "     wrestling             13\n",
       "24   aquatics             147\n",
       "     archery               12\n",
       "     athletics            181\n",
       "     badminton             13\n",
       "     basketball            12\n",
       "     boxing                18\n",
       "     canoe                 16\n",
       "     cycling               37\n",
       "     equestrian             2\n",
       "     fencing               12\n",
       "     football             104\n",
       "     golf                   5\n",
       "     gymnastics            31\n",
       "     handball              16\n",
       "     hockey                34\n",
       "     judo                  28\n",
       "     modern pentathlon      1\n",
       "     rowing                28\n",
       "     rugby sevens          18\n",
       "     sailing               13\n",
       "     shooting              11\n",
       "     table tennis          15\n",
       "     taekwondo             11\n",
       "     tennis                12\n",
       "     triathlon              6\n",
       "     volleyball            16\n",
       "     weightlifting         18\n",
       "     wrestling             25\n",
       "Length: 152, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes[athletes.age < 25].groupby([\"age\", \"sport\"]).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The youngest athletes is 16 playing the sport aquatics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age  sport        \n",
       "118  sailing            1\n",
       "42   badminton          1\n",
       "     canoe              1\n",
       "     fencing            1\n",
       "     golf               1\n",
       "     handball           1\n",
       "     tennis             1\n",
       "43   aquatics           1\n",
       "     cycling            1\n",
       "     football           1\n",
       "     gymnastics         1\n",
       "     rowing             1\n",
       "23   triathlon          1\n",
       "43   weightlifting      1\n",
       "44   fencing            1\n",
       "     rowing             1\n",
       "     table tennis       1\n",
       "23   equestrian         1\n",
       "45   sailing            1\n",
       "     tennis             1\n",
       "46   archery            1\n",
       "22   triathlon          1\n",
       "46   handball           1\n",
       "     rowing             1\n",
       "     sailing            1\n",
       "     tennis             1\n",
       "47   cycling            1\n",
       "     golf               1\n",
       "     sailing            1\n",
       "48   golf               1\n",
       "                     ... \n",
       "36   athletics         60\n",
       "28   rowing            60\n",
       "31   aquatics          63\n",
       "35   athletics         64\n",
       "23   football          72\n",
       "30   aquatics          72\n",
       "29   aquatics          77\n",
       "34   athletics         91\n",
       "22   athletics         95\n",
       "21   aquatics          96\n",
       "24   football         104\n",
       "28   aquatics         105\n",
       "23   athletics        108\n",
       "33   athletics        111\n",
       "27   aquatics         111\n",
       "26   aquatics         113\n",
       "22   aquatics         118\n",
       "23   aquatics         126\n",
       "25   football         128\n",
       "32   athletics        133\n",
       "31   athletics        135\n",
       "25   aquatics         141\n",
       "24   aquatics         147\n",
       "30   athletics        177\n",
       "24   athletics        181\n",
       "29   athletics        183\n",
       "25   athletics        186\n",
       "26   athletics        195\n",
       "28   athletics        196\n",
       "27   athletics        199\n",
       "Length: 690, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athletes[athletes.age >16].groupby([\"age\", \"sport\"]).size().sort_values(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The oldest is oddly 118 playing the sport \"sailing\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nationality  sport     weight\n",
       "ALG          football  60.0      3\n",
       "                       62.0      1\n",
       "                       65.0      5\n",
       "                       68.0      1\n",
       "                       70.0      3\n",
       "                       72.0      1\n",
       "                       75.0      6\n",
       "                       79.0      1\n",
       "                       80.0      1\n",
       "ARG          football  65.0      1\n",
       "                       66.0      1\n",
       "                       69.0      1\n",
       "                       70.0      4\n",
       "                       71.0      2\n",
       "                       72.0      1\n",
       "                       73.0      1\n",
       "                       74.0      1\n",
       "                       75.0      1\n",
       "                       76.0      2\n",
       "                       77.0      1\n",
       "                       78.0      1\n",
       "                       79.0      1\n",
       "                       80.0      2\n",
       "                       82.0      1\n",
       "AUS          football  54.0      1\n",
       "                       55.0      2\n",
       "                       56.0      1\n",
       "                       58.0      2\n",
       "                       60.0      3\n",
       "                       61.0      1\n",
       "                                ..\n",
       "SWE          football  74.0      5\n",
       "                       75.0      2\n",
       "                       77.0      3\n",
       "                       78.0      3\n",
       "                       79.0      1\n",
       "                       80.0      3\n",
       "                       85.0      1\n",
       "                       88.0      1\n",
       "USA          football  54.0      4\n",
       "                       56.0      3\n",
       "                       59.0      4\n",
       "                       60.0      2\n",
       "                       61.0      4\n",
       "                       63.0      1\n",
       "                       64.0      1\n",
       "                       67.0      1\n",
       "                       74.0      1\n",
       "                       76.0      1\n",
       "ZIM          football  50.0      1\n",
       "                       53.0      6\n",
       "                       54.0      1\n",
       "                       56.0      1\n",
       "                       57.0      1\n",
       "                       59.0      4\n",
       "                       60.0      2\n",
       "                       61.0      2\n",
       "                       65.0      1\n",
       "                       68.0      1\n",
       "                       69.0      1\n",
       "                       70.0      1\n",
       "Length: 331, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf = athletes[athletes.sport == \"football\"]\n",
    "tf.groupby([\"nationality\", \"sport\", \"weight\"]).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the countries that are participating in the sport, Football."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hockey_df = athletes[athletes.sport == \"Hockey\"]\n",
    "for row in Hockey_df.groupby([\"nationality\", \"sport\"]).size().iteritems():\n",
    "    print (row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, my_item in Hockey_df.iterrows():\n",
    "    print (\"\\n--------------------------------\")\n",
    "    print (color.RED + color.BOLD + \"Name: \" + color.END, my_item.last, \", \", my_item.first)\n",
    "    print (color.RED + color.BOLD + \"Position: \"+ color.END, my_item.weight)\n",
    "    print (color.RED + color.BOLD + \"Height (inches): \"+ color.END, my_item.height_in)\n",
    "    print (my_item.nationality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<blockquote style=\"border: 2px solid #666; padding: 10px; background-color: #99e6ff\"><b> Question 2:</b> Plot\n",
    "<div class=\"pull-right\">\n",
    "Practice for HW question 1\n",
    "</div>\n",
    "</blockquote>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><font color=\"blue\", size = 4>b)</font></b> Create a plot and explain it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"background-color: #FFFF00\">Your answer here</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Sport')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot = athletes.groupby([\"sport\"]).size().plot(kind=\"barh\", fontsize = 18, figsize = (20, 10))\n",
    "my_plot.set_title(\"Athletes per Sport\", fontsize = 40)\n",
    "my_plot.set_xlabel(\"Number of Athletes\", fontsize = 30, color = \"blue\")\n",
    "my_plot.set_ylabel(\"Sport\", fontsize = 30, color = \"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}