Update olympic_app.py

olympic_app.py

@@ -1,214 +1,219 @@
-import streamlit as st
-import pandas as pd
-import functions
-import important
-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 = functions.preprocess1(df, region_df)
-
-
-#st.title("Olympic Data Analysis.")
-
-st.sidebar.title("Olympic Data Analysis.")
-st.sidebar.image("Olympic_image.jpg")
-user_menu = st.sidebar.radio("Select an option", ("Overview", "Overall Analysis", "Medal tally", "Country-wise Analysis", "Athlete-wise Analysis"))
-
-if user_menu == "Overview":
-    st.image("Analysis_image.png")
-    
-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("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)
-
-    st.subheader("Countries have hosted the olympics.")
-    n_df = df.drop_duplicates(subset="Year")[["Year","City"]]
-    fig = px.bar(n_df, x='City', y='Year',text_auto = True,)
-    fig.update_xaxes(tickangle=45)
-    st.plotly_chart(fig)
-
-    countries_over_time = important.data_over_time(df,"region")
-    fig = px.line(countries_over_time, x="Year", y="region", title = 'Participating Countries over the years')
-    st.plotly_chart(fig)
-
-    events_over_time = important.data_over_time(df, "Event")
-    fig = px.line(events_over_time, x="Year", y="Event", title = 'Events over the years')
-    st.plotly_chart(fig)
-
-    athlete_over_time = important.data_over_time(df, "Name")
-    fig = px.line(athlete_over_time, x="Year", y="Name", 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)
-
-    sport_list = df["Sport"].unique().tolist()
-    sport_list.sort()
-    sport_list.insert(0,"Overall")
-    
-    selected_sport = st.selectbox("Select a Sport",sport_list)
-    st.title("Most successful Athletes")
-    x = important.most_successful(df,selected_sport)
-    st.table(x)
-
-    st.title("Most popular sports of Olympics")
-    sport_df = df["Sport"].value_counts().reset_index()
-    fig = px.pie(sport_df, values='count', names='Sport')
-    fig.update_layout(autosize=False, width=850,height=700)
-    st.plotly_chart(fig)
-
-    st.title("Locations of Stadium of countries where olympics held.")
-    data1 = pd.read_csv("lat_long.csv")
-    st.map(data1)
-
-
-if user_menu == "Medal tally":
-
-    st.sidebar.header("Medal tally")
-    years, country = important.country_year_list(df)
-
-    selected_year = st.sidebar.selectbox("Select year", years)
-    selected_country = st.sidebar.selectbox("Select country", country)
-
-    medal_tally = important.fetch_medal_tally(df,selected_year,selected_country)
-
-    if selected_year == "Overall" and selected_country == "Overall":
-        st.title("Overall Medal 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 in Olympics" )
-    if selected_year != "Overall" and selected_country != "Overall":
-        st.title(selected_country + "'s performance in " + str(selected_year) + " Olympics")
-    st.table(medal_tally)
-    
-
-if user_menu == "Country-wise Analysis":
-
-    st.title("Country-wise Analysis")
-
-    country = df["region"].dropna().unique().tolist()
-    country.sort()
-
-    selected_country = st.sidebar.selectbox("Select a country", country)
-    new_region = important.country_wise_medal_tally(df, selected_country)
-    
-    fig = px.line(new_region, x = "Year", y="Medal")
-    st.subheader(selected_country + "'s Medal Tally over the years")
-    st.plotly_chart(fig)
-
-    pt = important.country_event_heatmap(df,selected_country)
-    st.subheader(selected_country+" excels in the following sports")
-    fig, ax = plt.subplots(figsize=(20,20))
-    ax = sns.heatmap(pt, annot=True)
-    st.pyplot(fig)
-
-    athlete = important.most_successful_athletes(df, selected_country)
-    st.subheader("Top 10 athletes of "+ selected_country)
-    st.table(athlete)
-
-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()
-
-    st.title("Distribution of Age.")
-    fig = ff.create_distplot([x1,x2,x3,x4], ["Age Distribution","Gold Medalist","Silver Medalist","Bronze Medalist"], show_hist=False, show_rug=False)
-    fig.update_layout(autosize=False, width=850,height=530)
-    st.plotly_chart(fig)
-
-    st.title("Distribution of Age wrt sports(Gold Medalist)")
-    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', 'Modern Pentathlon', 'Golf', 'Softball', 'Archery', 'Volleyball','Synchronized Swimming',
-                 'Table Tennis', 'Baseball','Rhythmic Gymnastics','Rugby Sevens', 'Beach Volleyball', 'Triathlon', 'Rugby', 'Lacrosse', 'Polo', 'Cricket',
-                'Ice Hockey','Motorboating']
-    x = []
-    name = []
-    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)
-
-    fig1 = ff.create_distplot(x,name,show_hist=False, show_rug=False)
-    fig1.update_layout(autosize=False, width=850,height=530)
-    st.plotly_chart(fig1)
-
-
-    st.title("Height vs Weight")
-    sport_list = df["Sport"].unique().tolist()
-    sport_list.sort()
-    sport_list.insert(0,"Overall")
-    
-    selected_sport = st.selectbox("Select a Sport",sport_list)
-    new_df = important.weight_v_height(df,selected_sport)
-
-    fig, ax = plt.subplots(figsize=(10,10))
-    ax = sns.scatterplot(new_df, x ="Weight",y = "Height", hue=new_df["Medal"],style=new_df["Sex"],s=100)
-    st.pyplot(fig)
-
-    st.title("Men vs Women participation over the years")
-    final_df = important.men_vs_women(df)
-    fig = px.line(final_df, x="Year", y=["Male","Female"])
-    st.plotly_chart(fig)
-
-    
-    
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+import streamlit as st
+import pandas as pd
+import functions
+import important
+import plotly.express as px
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.figure_factory as ff
+from streamlit_option_menu import option_menu
+
+
+df = pd.read_csv("athlete_events.csv")
+region_df = pd.read_csv("noc_regions.csv")
+
+df = functions.preprocess1(df, region_df)
+
+
+#st.title("Olympic Data Analysis.")
+
+st.sidebar.title("Olympic Data Analysis.")
+st.sidebar.image("Olympic_image.jpg")
+
+with st.sidebar:
+    user_menu = option_menu("Select an option", ["Overview", "Overall Analysis", "Medal tally", "Country-wise Analysis", "Athlete-wise Analysis"],
+                           icons = ["slack", "bar-chart-line-fill", "award-fill", "graph-up-arrow", "person-walking"],
+                           menu_icon="bag-fill", default_index=0)
+
+if user_menu == "Overview":
+    st.image("Analysis_image.png")
+    
+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("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)
+
+    st.subheader("Countries have hosted the olympics.")
+    n_df = df.drop_duplicates(subset="Year")[["Year","City"]]
+    fig = px.bar(n_df, x='City', y='Year',text_auto = True,)
+    fig.update_xaxes(tickangle=45)
+    st.plotly_chart(fig)
+
+    countries_over_time = important.data_over_time(df,"region")
+    fig = px.line(countries_over_time, x="Year", y="region", title = 'Participating Countries over the years')
+    st.plotly_chart(fig)
+
+    events_over_time = important.data_over_time(df, "Event")
+    fig = px.line(events_over_time, x="Year", y="Event", title = 'Events over the years')
+    st.plotly_chart(fig)
+
+    athlete_over_time = important.data_over_time(df, "Name")
+    fig = px.line(athlete_over_time, x="Year", y="Name", 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)
+
+    sport_list = df["Sport"].unique().tolist()
+    sport_list.sort()
+    sport_list.insert(0,"Overall")
+    
+    selected_sport = st.selectbox("Select a Sport",sport_list)
+    st.title("Most successful Athletes")
+    x = important.most_successful(df,selected_sport)
+    st.table(x)
+
+    st.title("Most popular sports of Olympics")
+    sport_df = df["Sport"].value_counts().reset_index()
+    fig = px.pie(sport_df, values='count', names='Sport')
+    fig.update_layout(autosize=False, width=850,height=700)
+    st.plotly_chart(fig)
+
+    st.title("Locations of Stadium of countries where olympics held.")
+    data1 = pd.read_csv("lat_long.csv")
+    st.map(data1)
+
+
+if user_menu == "Medal tally":
+
+    st.sidebar.header("Medal tally")
+    years, country = important.country_year_list(df)
+
+    selected_year = st.sidebar.selectbox("Select year", years)
+    selected_country = st.sidebar.selectbox("Select country", country)
+
+    medal_tally = important.fetch_medal_tally(df,selected_year,selected_country)
+
+    if selected_year == "Overall" and selected_country == "Overall":
+        st.title("Overall Medal 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 in Olympics" )
+    if selected_year != "Overall" and selected_country != "Overall":
+        st.title(selected_country + "'s performance in " + str(selected_year) + " Olympics")
+    st.table(medal_tally)
+    
+
+if user_menu == "Country-wise Analysis":
+
+    st.title("Country-wise Analysis")
+
+    country = df["region"].dropna().unique().tolist()
+    country.sort()
+
+    selected_country = st.sidebar.selectbox("Select a country", country)
+    new_region = important.country_wise_medal_tally(df, selected_country)
+    
+    fig = px.line(new_region, x = "Year", y="Medal")
+    st.subheader(selected_country + "'s Medal Tally over the years")
+    st.plotly_chart(fig)
+
+    pt = important.country_event_heatmap(df,selected_country)
+    st.subheader(selected_country+" excels in the following sports")
+    fig, ax = plt.subplots(figsize=(20,20))
+    ax = sns.heatmap(pt, annot=True)
+    st.pyplot(fig)
+
+    athlete = important.most_successful_athletes(df, selected_country)
+    st.subheader("Top 10 athletes of "+ selected_country)
+    st.table(athlete)
+
+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()
+
+    st.title("Distribution of Age.")
+    fig = ff.create_distplot([x1,x2,x3,x4], ["Age Distribution","Gold Medalist","Silver Medalist","Bronze Medalist"], show_hist=False, show_rug=False)
+    fig.update_layout(autosize=False, width=850,height=530)
+    st.plotly_chart(fig)
+
+    st.title("Distribution of Age wrt sports(Gold Medalist)")
+    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', 'Modern Pentathlon', 'Golf', 'Softball', 'Archery', 'Volleyball','Synchronized Swimming',
+                 'Table Tennis', 'Baseball','Rhythmic Gymnastics','Rugby Sevens', 'Beach Volleyball', 'Triathlon', 'Rugby', 'Lacrosse', 'Polo', 'Cricket',
+                'Ice Hockey','Motorboating']
+    x = []
+    name = []
+    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)
+
+    fig1 = ff.create_distplot(x,name,show_hist=False, show_rug=False)
+    fig1.update_layout(autosize=False, width=850,height=530)
+    st.plotly_chart(fig1)
+
+
+    st.title("Height vs Weight")
+    sport_list = df["Sport"].unique().tolist()
+    sport_list.sort()
+    sport_list.insert(0,"Overall")
+    
+    selected_sport = st.selectbox("Select a Sport",sport_list)
+    new_df = important.weight_v_height(df,selected_sport)
+
+    fig, ax = plt.subplots(figsize=(10,10))
+    ax = sns.scatterplot(new_df, x ="Weight",y = "Height", hue=new_df["Medal"],style=new_df["Sex"],s=100)
+    st.pyplot(fig)
+
+    st.title("Men vs Women participation over the years")
+    final_df = important.men_vs_women(df)
+    fig = px.line(final_df, x="Year", y=["Male","Female"])
+    st.plotly_chart(fig)
+
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