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helper.py
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helper.py
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import numpy as np
import pandas as pd
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'] == 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 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').rename(columns={'Year': 'Edition', 'index': col})
nations_over_time.rename(columns={'index': 'Edition', 'Year': 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]
# Count the number of medals per athlete
x = temp_df['Name'].value_counts().reset_index()
x.columns = ['Name', 'Medals'] # Renaming columns to avoid confusion
# Merge with the original DataFrame to get additional information
x = x.head(15).merge(df, on='Name', how='left')[['Name', 'Medals', 'Sport', 'region']].drop_duplicates('Name')
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]
# Count the number of medals per athlete
x = temp_df['Name'].value_counts().reset_index()
x.columns = ['Name', 'Medals'] # Rename columns for clarity
# Get the top 10 athletes
top_athletes = x.head(10)
# Merge with the original DataFrame to get additional information
result = top_athletes.merge(df, on='Name', how='left')[['Name', 'Medals', 'Sport', 'region']].drop_duplicates('Name')
return result
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