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app.py
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import streamlit as st
import preprocessor, helper
import plotly.express as px
import plotly.figure_factory as ff
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
df = preprocessor.preprocess()
st.sidebar.title('Olympics Analysis')
st.sidebar.image('https://webstockreview.net/images/game-clipart-olympic-7.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 a year", years)
selected_country = st.sidebar.selectbox("Select a country", country)
medal_tally = helper.fetch_year_country(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':
edition = 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.header('Top Statistics')
col1, col2, col3 = st.columns(3)
with col1:
st.header('Editions')
st.title(edition)
with col2:
st.header('Host Cities')
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('Athletes')
st.title(athletes)
with col3:
st.header('Nations')
st.title(nations)
nations_over_time = helper.data_over_time(df, 'region')
fig = px.line(nations_over_time, x='Edition', y='region')
st.title("Participating nations overtime")
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 progression overtime")
st.plotly_chart(fig)
athletes_over_time = helper.data_over_time(df, 'Name')
fig = px.line(athletes_over_time, x='Edition', y='Name')
st.title("Athletes participation overtime")
st.plotly_chart(fig)
st.title("No. of Events over time(Every Sport)")
fig, ax = plt.subplots(figsize=(20, 20))
x = df.drop_duplicates(subset=['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.header('Most Successful Athletes')
sport_list = df['Sport'].unique().tolist()
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.header('Country-Wise Medal Tally')
country_list = df['region'].dropna().unique().tolist()
country_list.sort()
selected_country = st.sidebar.selectbox('Select a Country', country_list)
country_df = helper.country_wise_medal_tally(df, selected_country)
st.header(selected_country+' Medal Tally Over Years')
fig = px.line(country_df, x='Year', y='Medal')
st.plotly_chart(fig)
st.header(selected_country+' excels in following sports')
fig, ax = plt.subplots(figsize=(20, 20))
pt = helper.country_event_heatmap(df, selected_country)
ax = sns.heatmap(pt, annot=True)
st.pyplot(fig)
st.header('TOP-10 Athletes of '+selected_country)
top10 = helper.successful_athlete(df, selected_country)
st.table(top10)
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']
medals = ['Gold', 'Silver', 'Bronze']
selected_medal = st.selectbox('select medal', medals)
st.text('Selecting Bronze may throw an error')
st.text('because majority list or tuple are empty in this case')
for sport in famous_sports:
temp_df = athlete_df[athlete_df['Sport'] == sport]
x.append(temp_df[temp_df['Medal'] == selected_medal]['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"+'('+selected_medal+')')
st.plotly_chart(fig)
st.title('Height Vs Weight')
sport_list = df['Sport'].unique().tolist()
sport_list.insert(0, 'Overall')
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 v/s women participation over the years')
final = helper.men_vs_women(df)
fig = px.line(final, x='Year', y=['Men', 'Women'])
fig.update_layout(autosize=False, width=1000, height=600)
st.plotly_chart(fig)