Successfully Added Olympics_analysis_web_App

Data Analysis/olympics_Analysis/README.md

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+<h2>olympics-data-analysis-web-app</h2>
+
+A Streamlit web application for the analysis of olympics dataset
+
+Dataset Link: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results
+
+Live Link: https://olympicdatanalysis.streamlit.app/

Data Analysis/olympics_Analysis/app.py

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+import streamlit as st
+import pandas as pd
+import preprocessor,helper
+import plotly.express as px
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.figure_factory as ff
+
+##def main():
+
+df = pd.read_csv('athlete_events.csv')
+region_df = pd.read_csv('noc_regions.csv')
+
+df = preprocessor.preprocess(df, region_df)
+
+user_menu = st.sidebar.radio(
+    'Select an Option',
+    ('Medal Tally','Overall Analysis','Country-wise Analysis','Athlete wise Analysis')
+)
+
+st.sidebar.title("Olympics Analysis")
+st.sidebar.image('https://e7.pngegg.com/pngimages/1020/402/png-clipart-2024-summer-olympics-brand-circle-area-olympic-rings-olympics-logo-text-sport.png')
+if user_menu == 'Medal Tally':
+    st.sidebar.header("Medal Tally")
+    years, country = helper.country_year_list(df)
+
+    selected_year = st.sidebar.selectbox("Select Year",years)
+    selected_country = st.sidebar.selectbox("Select Country", country)
+
+    medal_tally = helper.fetch_medal_tally(df,selected_year,selected_country)
+    if selected_year == 'Overall' and selected_country =='Overall':
+        st.title("Overall Tally")
+    if selected_year!='Overall' and selected_country=="Overall":
+        st.title("Medal Tally in " + str(selected_year) + " Olympics ")
+    if selected_year=="Overall" and selected_country != "overall":
+        st.title(selected_country + " Overall Performance ")
+    if selected_year!='Overall' and selected_country!= "overall":
+        st.title(selected_country + " Performance in " + str(selected_year) + " Olympics ")
+
+    st.table(medal_tally)
+
+if user_menu =='Overall Analysis':
+    editions=df['Year'].unique().shape[0]-1
+    cities=df['City'].unique().shape[0]
+    sports=df['Sport'].unique().shape[0]
+    events=df['Event'].unique().shape[0]
+    athletes=df['Name'].unique().shape[0]
+    nations=df['region'].unique().shape[0]
+
+    st.title(" Top Statistics")
+    col1,col2,col3=st.columns(3)
+    with col1:
+        st.header(" Edition ")
+        st.title(editions)
+
+    with col2:
+        st.header(" Hosts ")
+        st.title(cities)
+
+    with col3:
+        st.header(" Sports ")
+        st.title(sports)
+
+    col1,col2,col3=st.columns(3)
+    with col1:
+        st.header(" Events ")
+        st.title(events)
+
+    with col2:
+        st.header(" Nations ")
+        st.title(nations)
+
+    with col3:
+        st.header(" Athletes ")
+        st.title(athletes)
+
+    nations_over_time=helper.data_over_time(df,'region')
+    fig=px.line(nations_over_time,x='Edition',y='region')
+    st.title(" Participating Nations over the years ")
+    st.plotly_chart(fig)
+
+
+    events_over_time=helper.data_over_time(df,'Event')
+    fig=px.line(events_over_time,x='Edition',y='Event')
+    st.title(" Events over the years ")
+    st.plotly_chart(fig)
+
+    athlete_over_time=helper.data_over_time(df,'Name')
+    fig=px.line(athlete_over_time,x='Edition',y='Name')
+    st.title(" Athletes over the years ")
+    st.plotly_chart(fig)
+
+    st.title(" No. of Events over time(Every Sport)")
+    fig,ax = plt.subplots(figsize=(20,20))
+    x=df.drop_duplicates(['Year','Sport','Event'])
+    ax=sns.heatmap(x.pivot_table(index='Sport',columns='Year',values='Event',aggfunc='count').fillna(0).astype('int'),annot=True)
+    st.pyplot(fig)
+
+    st.title("Most Successful Athletes")
+    sport_list=df['Sport'].unique().tolist()
+    sport_list.sort()
+    sport_list.insert(0,'Overall')
+
+    selected_sport = st.selectbox("Select a Sport ",sport_list)
+    x=helper.most_successful(df,selected_sport)
+    st.table(x)
+
+if user_menu =='Country-wise Analysis':
+
+    st.sidebar.title('Country-wise Analysis')
+
+    country_list=df['region'].dropna().unique().tolist()
+    country_list.sort()
+
+    selected_country=st.sidebar.selectbox('Select a Country',country_list)
+
+    country_df=helper.yearwise_medal_tally(df,selected_country)
+    fig=px.line(country_df,x='Year',y='Medal')
+    st.title(selected_country + " Medal Tally over the years ")
+    st.plotly_chart(fig)
+
+
+    st.title(selected_country + " excels int the following sports")
+    pt=helper.country_event_heatmap(df,selected_country)
+    fig,ax = plt.subplots(figsize=(20,20))
+    ax=sns.heatmap(pt,annot=True)
+    st.pyplot(fig)
+
+    st.title(" Top 10 athletes " + selected_country)
+    top10_df=helper.most_successful_countrywise(df,selected_country)
+    st.table(top10_df)
+
+if user_menu == 'Athlete wise Analysis':
+    athlete_df=df.drop_duplicates(subset=['Name','region'])
+
+    x1=athlete_df['Age'].dropna()
+    x2=athlete_df[athlete_df['Medal']=='Gold']['Age'].dropna()
+    x3=athlete_df[athlete_df['Medal']=='Silver']['Age'].dropna()
+    x4=athlete_df[athlete_df['Medal']=='Bronze']['Age'].dropna()    
+
+    fig=ff.create_distplot([x1,x2,x3,x4],['Overall Age','Gold Medalist','Silver Medalist','Bronze Medalist'],show_hist=False,show_rug=False)
+
+    fig.update_layout(autosize=False,width=1000,height=600)
+    st.plotly_chart(fig)
+
+    x = []
+    name = []
+    famous_sports = ['Basketball', 'Judo', 'Football', 'Tug-Of-War', 'Athletics',
+                     'Swimming', 'Badminton', 'Sailing', 'Gymnastics',
+                     'Art Competitions', 'Handball', 'Weightlifting', 'Wrestling',
+                     'Water Polo', 'Hockey', 'Rowing', 'Fencing',
+                     'Shooting', 'Boxing', 'Taekwondo', 'Cycling', 'Diving', 'Canoeing',
+                     'Tennis', 'Golf', 'Softball', 'Archery',
+                     'Volleyball', 'Synchronized Swimming', 'Table Tennis', 'Baseball',
+                     'Rhythmic Gymnastics', 'Rugby Sevens',
+                     'Beach Volleyball', 'Triathlon', 'Rugby', 'Polo', 'Ice Hockey']
+    for sport in famous_sports:
+        temp_df = athlete_df[athlete_df['Sport'] == sport]
+        x.append(temp_df[temp_df['Medal'] == 'Gold']['Age'].dropna())
+        name.append(sport)
+
+    fig = ff.create_distplot(x, name, show_hist=False, show_rug=False)
+    fig.update_layout(autosize=False, width=1000, height=600)
+    st.title("Distribution of Age wrt Sports(Gold Medalist)")
+    st.plotly_chart(fig)
+
+
+    sport_list=df['Sport'].unique().tolist()
+    sport_list.sort()
+    sport_list.insert(0,'Overall')
+
+    st.title("Height Vs Weight ")
+    selected_sport=st.selectbox(' Select a Sport ',sport_list)
+    temp_df=helper.weight_v_height(df,selected_sport)
+    fig,ax=plt.subplots()
+    ax=sns.scatterplot(x='Weight',y='Height',data=temp_df,hue=temp_df['Medal'],style=temp_df['Sex'],s=60)
+
+    st.pyplot(fig)
+
+    st.title(' Men VS Women Participation over the years')
+    final=helper.men_vs_women(df)
+    fig=px.line(final,x='Year',y=["Male","Female"])
+    fig.update_layout(autosize=False, width=1000, height=600)
+    st.plotly_chart(fig)
+
+
+
+
+

Data Analysis/olympics_Analysis/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

Data Analysis/olympics_Analysis/preprocessor.py

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+import pandas as pd
+#df=pd.read_csv('athlete_events.csv')
+#egion_df=pd.read_csv('noc_regions.csv')
+
+def preprocess(df,region_df):
+    ##global df,region_df
+
+    # filtering for summer olympics
+
+    df = df[df['Season'] == 'Summer']
+    # merge with region_df
+    df = df.merge(region_df,on = 'NOC', how='left')
+    # dropping duplicates
+    df.drop_duplicates(inplace=True)
+    # one hor encoding medals
+    df = pd.concat([df, pd.get_dummies(df['Medal'])], axis=1)
+    return df

Data Analysis/olympics_Analysis/requirements.txt

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+streamlit
+plotly
+seaborn
+matplotlib

Data Analysis/olympics_Analysis/setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/config.toml