File size: 6,773 Bytes
ad8ddbc f3393a7 ad8ddbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | 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)
|