| 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 |
|
|
| df = pd.read_csv('athlete_events.csv') |
| region_df = pd.read_csv('noc_regions.csv') |
|
|
| df = preprocessor.preprocess(df,region_df) |
|
|
| 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') |
| user_menu = st.sidebar.radio( |
| 'Select an Option', |
| ('Medal Tally','Overall Analysis','Country-wise Analysis','Athlete wise Analysis') |
| ) |
|
|
| 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("Editions") |
| 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 in 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 of " + 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.title("Distribution of Age") |
| 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=temp_df['Weight'], y=temp_df['Height'], 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) |
|
|
|
|
|
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