Notes

import pandas as pd
import numpy as np
import scipy.stats as stats
import re

#mlb_df=pd.read_csv("assets/mlb.csv")
#nhl_df=pd.read_csv("assets/nhl.csv")
#nba_df=pd.read_csv("assets/nba.csv")
#nfl_df=pd.read_csv("assets/nfl.csv")
#cities=pd.read_html("assets/wikipedia_data.html")[1]
#cities=cities.iloc[:-1,[0,3,5,6,7,8]]

def nhl_correla():
# YOUR CODE HERE
#raise NotImplementedError()

nhl_df=pd.read_csv("assets/nhl.csv")
cities=pd.read_html("assets/wikipedia_data.html")[1]
cities=cities.iloc[:-1,[0,3,5,6,7,8]]

nhl_df.drop([0,9,18,26],0,inplace=True)
cities.drop([14,15,18,19,20,21,23,24,25,27,28,32,33,38,40,41,42,44,45,46,48,49,50],0,inplace=True)

l= []
for i in cities['NHL']:
i=i.split('[')
l.append(i[0])
cities['NHL'] = l

li = []
for i in nhl_df['team']:
i = re.findall("[^*]+", i)
li.append(i[0])
nhl_df['team'] = li

nhl_df = nhl_df.head(31)

nhl_df['team_ville'] = nhl_df['team']
nhl_df['team_ville'] = nhl_df['team_ville'].map({'Tampa Bay Lightning':'Tampa Bay Area',
'Boston Bruins':'Boston',
'Toronto Maple Leafs':'Toronto',
'Florida Panthers':'Miami–Fort Lauderdale',
'Detroit Red Wings':'Detroit',
'Montreal Canadiens':'Montreal',
'Ottawa Senators':'Ottawa',
'Buffalo Sabres':'Buffalo',
'Washington Capitals':'Washington, D.C.',
'Pittsburgh Penguins':'Pittsburgh',
'Philadelphia Flyers':'Philadelphia',
'Columbus Blue Jackets':'Columbus',
'New Jersey Devils':'New York City',
'Carolina Hurricanes':'Raleigh',
'New York Islanders':'New York City',
'New York Rangers':'New York City',
'Nashville Predators':'Nashville',
'Winnipeg Jets':'Winnipeg',
'Minnesota Wild':'Minneapolis–Saint Paul',
'Colorado Avalanche':'Denver',
'St. Louis Blues':'St. Louis',
'Dallas Stars':'Dallas–Fort Worth',
'Chicago Blackhawks':'Chicago',
'Vegas Golden Knights':'Las Vegas',
'Anaheim Ducks':'Los Angeles',
'San Jose Sharks':'San Francisco Bay Area',
'Los Angeles Kings':'Los Angeles',
'Calgary Flames':'Calgary',
'Edmonton Oilers':'Edmonton',
'Vancouver Canucks':'Vancouver',
'Arizona Coyotes':'Phoenix'})

df = pd.merge(nhl_df,cities, left_on= "team_ville", right_on= "Metropolitan area")

df['W'] = pd.to_numeric(df['W'])
df['L'] = pd.to_numeric(df['L'])
df['Population (2016 est.)[8]'] = pd.to_numeric(df['Population (2016 est.)[8]'])

he = ['team','W','L','Metropolitan area','Population (2016 est.)[8]']

df = df[he]

df['W/L'] = df['W']/(df['L']+df['W'])

df = df.groupby('Metropolitan area').mean().reset_index()

return df

def nba_correla():
# YOUR CODE HERE
#raise NotImplementedError()

nba_df=pd.read_csv("assets/nba.csv")
cities=pd.read_html("assets/wikipedia_data.html")[1]
cities=cities.iloc[:-1,[0,3,5,6,7,8]]

cities.drop([16,17,19,20,21,22,23,26,29,30,31,34,35,36,37,39,40,43,44,47,48,49,50],0,inplace=True)

l1 = []
for i in nba_df['team']:
#i=i.rstrip()
i=i.split('*')
l1.append(i[0])
nba_df['team'] = l1

l2 = []
for i in nba_df['team']:
i=i.split('(')
l2.append(i[0])
nba_df['team'] = l2

l3 = []
for i in nba_df['team']:
i=i.rstrip()
l3.append(i)
nba_df['team'] = l3



nba_df = nba_df.head(30)

nba_df['team_ville'] = nba_df['team']
nba_df['team_ville'] = nba_df['team_ville'].map({'Toronto Raptors':'Toronto',
'Boston Celtics':'Boston',
'Philadelphia 76ers':'Philadelphia',
'Cleveland Cavaliers':'Cleveland',
'Indiana Pacers':'Indianapolis',
'Miami Heat':'Miami–Fort Lauderdale',
'Milwaukee Bucks':'Milwaukee',
'Washington Wizards':'Washington, D.C.',
'Detroit Pistons':'Detroit',
'Charlotte Hornets':'Charlotte',
'New York Knicks':'New York City',
'Brooklyn Nets':'New York City',
'Chicago Bulls':'Chicago',
'Orlando Magic':'Orlando',
'Atlanta Hawks':'Atlanta',
'Houston Rockets':'Houston',
'Golden State Warriors':'San Francisco Bay Area',
'Portland Trail Blazers':'Portland',
'Oklahoma City Thunder':'Oklahoma City',
'Utah Jazz':'Salt Lake City',
'New Orleans Pelicans':'New Orleans',
'San Antonio Spurs':'San Antonio',
'Minnesota Timberwolves':'Minneapolis–Saint Paul',
'Denver Nuggets':'Denver',
'Los Angeles Clippers':'Los Angeles',
'Los Angeles Lakers':'Los Angeles',
'Sacramento Kings':'Sacramento',
'Dallas Mavericks':'Dallas–Fort Worth',
'Memphis Grizzlies':'Memphis',
'Phoenix Suns':'Phoenix'})

df2 = pd.merge(nba_df,cities, left_on= "team_ville", right_on= "Metropolitan area")

df2['W/L%'] = pd.to_numeric(df2['W/L%'])
df2['W'] = pd.to_numeric(df2['W'])
df2['L'] = pd.to_numeric(df2['L'])
df2['Population (2016 est.)[8]'] = pd.to_numeric(df2['Population (2016 est.)[8]'])
he = ['team','W','L','W/L%','Metropolitan area','Population (2016 est.)[8]']
df2 = df2[he]
df2['W/L'] = df2['W']/(df2['L']+df2['W'])
df2 = df2.groupby('Metropolitan area').mean().reset_index()

return df2

def mlb_correla():
# YOUR CODE HERE
#raise NotImplementedError()

mlb_df=pd.read_csv("assets/mlb.csv")
cities=pd.read_html("assets/wikipedia_data.html")[1]
cities=cities.iloc[:-1,[0,3,5,6,7,8]]

cities.drop([24,25,26,28,29,30,31,32,33,34,35,36,37,38,39,41,42,43,44,45,46,47,48,49,50],0,inplace=True)

mlb_df = mlb_df.head(30)

mlb_df['team_ville'] = mlb_df['team']
mlb_df['team_ville'] = mlb_df['team_ville'].map({'Boston Red Sox':'Boston',
'New York Yankees':'New York City',
'Tampa Bay Rays':'Tampa Bay Area',
'Toronto Blue Jays':'Toronto',
'Baltimore Orioles':'Baltimore',
'Cleveland Indians':'Cleveland',
'Minnesota Twins':'Minneapolis–Saint Paul',
'Detroit Tigers':'Detroit',
'Chicago White Sox':'Chicago',
'Kansas City Royals':'Kansas City',
'Houston Astros':'Houston',
'Oakland Athletics':'San Francisco Bay Area',
'Seattle Mariners':'Seattle',
'Los Angeles Angels':'Los Angeles',
'Texas Rangers':'Dallas–Fort Worth',
'Atlanta Braves':'Atlanta',
'Washington Nationals':'Washington, D.C.',
'Philadelphia Phillies':'Philadelphia',
'New York Mets':'New York City',
'Miami Marlins':'Miami–Fort Lauderdale',
'Milwaukee Brewers':'Milwaukee',
'Chicago Cubs':'Chicago',
'St. Louis Cardinals':'St. Louis',
'Pittsburgh Pirates':'Pittsburgh',
'Cincinnati Reds':'Cincinnati',
'Los Angeles Dodgers':'Los Angeles',
'Colorado Rockies':'Denver',
'Arizona Diamondbacks':'Phoenix',
'San Francisco Giants':'San Francisco Bay Area',
'San Diego Padres':'San Diego'})

df3 = pd.merge(mlb_df,cities, left_on= "team_ville", right_on= "Metropolitan area")

#df2['W/L%'] = pd.to_numeric(df2['W/L%'])
df3['W'] = pd.to_numeric(df3['W'])
df3['L'] = pd.to_numeric(df3['L'])
df3['Population (2016 est.)[8]'] = pd.to_numeric(df3['Population (2016 est.)[8]'])
he = ['team','W','L','Metropolitan area','Population (2016 est.)[8]']
df3 = df3[he]
df3['W/L'] = df3['W']/(df3['L']+df3['W'])
df3 = df3.groupby('Metropolitan area').mean().reset_index()

return df3

def nfl_correla():
# YOUR CODE HERE
#raise NotImplementedError()

nfl_df=pd.read_csv("assets/nfl.csv")
cities=pd.read_html("assets/wikipedia_data.html")[1]
cities=cities.iloc[:-1,[0,3,5,6,7,8]]

nfl_df.drop([0,5,10,15,20,25,30,35],0,inplace=True)

cities.drop([13,22,27,30,31,32,33,34,35,36,37,38,39,40,41,42,43,45,46,47,49,50],0,inplace=True)

l1 = []
for i in nfl_df['team']:
#i=i.rstrip()
i=i.split('*')
l1.append(i[0])
nfl_df['team'] = l1

l2 = []
for i in nfl_df['team']:
i=i.split('+')
l2.append(i[0])
nfl_df['team'] = l2

nfl_df = nfl_df.head(32)

nfl_df['team_ville'] = nfl_df['team']
nfl_df['team_ville'] = nfl_df['team_ville'].map({'New England Patriots':'Boston',
'Miami Dolphins':'Miami–Fort Lauderdale',
'Buffalo Bills':'Buffalo',
'New York Jets':'New York City',
'Baltimore Ravens':'Baltimore',
'Pittsburgh Steelers':'Pittsburgh',
'Cleveland Browns':'Cleveland',
'Cincinnati Bengals':'Cincinnati',
'Houston Texans':'Houston',
'Indianapolis Colts':'Indianapolis',
'Tennessee Titans':'Nashville',
'Jacksonville Jaguars':'Jacksonville',
'Kansas City Chiefs':'Kansas City',
'Los Angeles Chargers':'Los Angeles',
'Denver Broncos':'Denver',
'Oakland Raiders':'San Francisco Bay Area',
'Dallas Cowboys':'Dallas–Fort Worth',
'Philadelphia Eagles':'Philadelphia',
'Washington Redskins':'Washington, D.C.',
'New York Giants':'New York City',
'Chicago Bears':'Chicago',
'Minnesota Vikings':'Minneapolis–Saint Paul',
'Green Bay Packers':'Green Bay',
'Detroit Lions':'Detroit',
'New Orleans Saints':'New Orleans',
'Carolina Panthers':'Charlotte',
'Atlanta Falcons':'Atlanta',
'Tampa Bay Buccaneers':'Tampa Bay Area',
'Los Angeles Rams':'Los Angeles',
'Seattle Seahawks':'Seattle',
'San Francisco 49ers':'San Francisco Bay Area',
'Arizona Cardinals':'Phoenix'})

df4 = pd.merge(nfl_df,cities, left_on= "team_ville", right_on= "Metropolitan area")


df4['W'] = pd.to_numeric(df4['W'])
df4['L'] = pd.to_numeric(df4['L'])
df4['Population (2016 est.)[8]'] = pd.to_numeric(df4['Population (2016 est.)[8]'])
he = ['team','W','L','Metropolitan area','Population (2016 est.)[8]']
df4 = df4[he]
df4['W/L'] = df4['W']/(df4['L']+df4['W'])
df4 = df4.groupby('Metropolitan area').mean().reset_index()

return df4





def sports_team_performance():
# YOUR CODE HERE
#raise NotImplementedError()

nfl = nfl_correla()
nba = nba_correla()
mlb = mlb_correla()
nhl = nhl_correla()

nba_nfl = pd.merge(nba,nfl, on='Metropolitan area')
pval_nba_nfl = stats.ttest_rel(nba_nfl['W/L_x'],nba_nfl['W/L_y'])[1]
nba_nhl = pd.merge(nba,nhl, on='Metropolitan area')
pval_nba_nhl = stats.ttest_rel(nba_nhl['W/L_x'],nba_nhl['W/L_y'])[1]
mlb_nfl = pd.merge(mlb,nfl, on='Metropolitan area')
pval_mlb_nfl = stats.ttest_rel(mlb_nfl['W/L_x'],mlb_nfl['W/L_y'])[1]
mlb_nhl = pd.merge(mlb,nhl, on='Metropolitan area')
pval_mlb_nhl = stats.ttest_rel(mlb_nhl['W/L_x'],mlb_nhl['W/L_y'])[1]
mlb_nba = pd.merge(mlb,nba, on='Metropolitan area')
pval_mlb_nba = stats.ttest_rel(mlb_nba['W/L_x'],mlb_nba['W/L_y'])[1]
nhl_nfl = pd.merge(nhl,nfl, on='Metropolitan area')
pval_nhl_nfl = stats.ttest_rel(nhl_nfl['W/L_x'],nhl_nfl['W/L_y'])[1]

pv = {'NFL': {"NFL": np.nan, 'NBA': pval_nba_nfl, 'NHL': pval_nhl_nfl, 'MLB': pval_mlb_nfl},
'NBA': {"NFL": pval_nba_nfl, 'NBA': np.nan, 'NHL': pval_nba_nhl, 'MLB': pval_mlb_nba},
'NHL': {"NFL": pval_nhl_nfl, 'NBA': pval_nba_nhl, 'NHL': np.nan, 'MLB': pval_mlb_nhl},
'MLB': {"NFL": pval_mlb_nfl, 'NBA': pval_mlb_nba, 'NHL': pval_mlb_nhl, 'MLB': np.nan}
}


# Note: p_values is a full dataframe, so df.loc["NFL","NBA"] should be the same as df.loc["NBA","NFL"] and
# df.loc["NFL","NFL"] should return np.nan
#sports = ['NFL', 'NBA', 'NHL', 'MLB']
#p_values = pd.DataFrame({k:np.nan for k in sports}, index=sports)
p_values = pd.DataFrame(pv)

assert abs(p_values.loc["NBA", "NHL"] - 0.02) <= 1e-2, "The NBA-NHL p-value should be around 0.02"
assert abs(p_values.loc["MLB", "NFL"] - 0.80) <= 1e-2, "The MLB-NFL p-value should be around 0.80"
return p_values