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helper.py
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import numpy as np
def fetch_medal_tally(df,year,country):
medal_df=df.drop_duplicates(subset=['Team','NOC','Games','Year','City','Sport','Event','Medal'])
flag=0
if year =='Overall' and country=='Overall':
temp_df=medal_df
if year =='Overall' and country!= 'Overall':
flag=1
temp_df=medal_df[medal_df['region'] == country]
if year!= "Overall" and country=='Overall':
temp_df=medal_df[medal_df['Year'] == int(year)]
if year!= "Overall" and country!='Overall':
temp_df=medal_df[(medal_df['Year'] == int(year)) & (medal_df['region']==country)]
if flag==1:
x =temp_df.groupby('Year').sum()[['Gold','Silver','Bronze']].sort_values('Year').reset_index()
else:
x =temp_df.groupby('region').sum()[['Gold','Silver','Bronze']].sort_values('Gold',ascending=False).reset_index()
x['total']= x['Gold'] + x['Silver'] +x['Bronze']
x['Gold'] = x['Gold'].astype('int')
x['Silver'] = x['Silver'].astype('int')
x['Bronze'] = x['Bronze'].astype('int')
x['total'] = x['total'].astype('int')
return x
def medal_tally(df):
medal_tally = df.drop_duplicates(subset=['Team', 'NOC', 'Games', 'Year', 'City', 'Sport', 'Event', 'Medal'])
medal_tally = (medal_tally.groupby('region').sum()[['Gold', 'Silver', 'Bronze']].sort_values('Gold', ascending=False).reset_index())
medal_tally['total'] = medal_tally['Gold'] + medal_tally['Silver'] + medal_tally['Bronze']
medal_tally['Gold'] = medal_tally['Gold'].astype('int')
medal_tally['Silver'] = medal_tally['Silver'].astype('int')
medal_tally['Bronze'] = medal_tally['Bronze'].astype('int')
medal_tally['total'] = medal_tally['total'].astype('int')
return medal_tally
def country_year_list(df):
years = df['Year'].unique().tolist()
years.sort()
years.insert(0, 'Overall')
country = np.unique(df['region'].dropna().values).tolist()
country.sort()
country.insert(0, 'Overall')
return years,country
def data_over_time(df,col):
nations_over_time=df.drop_duplicates(['Year',col])['Year'].value_counts().reset_index().sort_values('Year')
nations_over_time.rename(columns={'Year': 'Edition','count': col},inplace=True)
return nations_over_time
def most_successful(df,sport):
temp_df=df.dropna(subset=['Medal'])
if sport!= 'Overall':
temp_df=temp_df[temp_df['Sport']==sport]
x= temp_df['Name'].value_counts().reset_index().head(15).merge(df)[['Name','count','Sport','region']].drop_duplicates('Name')
x.rename(columns={'count':'Medals'},inplace=True)
return x
def yearwise_medal_tally(df,country):
temp_df=df.dropna(subset=['Medal'])
temp_df.drop_duplicates(subset=['Team','NOC','Games','Year','City','Sport','Event','Medal'],inplace=True)
new_df=temp_df[temp_df['region']==country]
final_df=new_df.groupby('Year').count()['Medal'].reset_index()
return final_df
def country_event_heatmap(df,country):
temp_df=df.dropna(subset=['Medal'])
temp_df.drop_duplicates(subset=['Team','NOC','Games','Year','City','Sport','Event','Medal'],inplace=True)
new_df=temp_df[temp_df['region']==country]
pt=new_df.pivot_table(index='Sport',columns='Year',values='Medal',aggfunc='count').fillna(0)
return pt
def most_successful_countrywise(df,country):
temp_df=df.dropna(subset=['Medal'])
temp_df=temp_df[temp_df['region']==country]
x= temp_df['Name'].value_counts().reset_index().head(10).merge(df)[['Name','count','Sport']].drop_duplicates('Name')
x.rename(columns={'count':'Medals'},inplace=True)
return x
def weight_v_height(df,sport):
athlete_df=df.drop_duplicates(subset=['Name','region'])
athlete_df['Medal'].fillna('No Medal',inplace=True)
if sport!=' Overall ':
temp_df=athlete_df[athlete_df['Sport'] == sport]
return temp_df
else:
return athlete_df
def men_vs_women(df):
athlete_df=df.drop_duplicates(subset=['Name','region'])
men=athlete_df[athlete_df['Sex']=='M'].groupby('Year').count()['Name'].reset_index()
women=athlete_df[athlete_df['Sex']=='F'].groupby('Year').count()['Name'].reset_index()
final=men.merge(women,on='Year',how='left')
final.rename(columns={'Name_x':'Male','Name_y':'Female'},inplace=True)
final.fillna(0,inplace=True)
return final