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| import requests | |
| import streamlit as st | |
| from streamlit_lottie import st_lottie | |
| st.set_page_config(page_title='Asia cup Analysis',layout='wide') | |
| # st.title("Asia Cup Data") | |
| # st.text(" ") | |
| # st.image("/home/tejas/Downloads/Asia_cup.jpg") | |
| def load_lottieurl(url): | |
| r=requests.get(url) | |
| if r.status_code != 200: | |
| return None | |
| return r.json() | |
| lottie_coding=load_lottieurl("https://assets6.lottiefiles.com/packages/lf20_1fXD2hXInk.json") | |
| with st.container(): | |
| # right_column=st.columns(2) | |
| # with right_column: | |
| st_lottie(lottie_coding, height=300, key='coding') | |
| # st.markdown("""---""") | |
| # st.beta_columns | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle #to load a saved modelimport base64 #to open .gif files in streamlit app | |
| import pandas as pd | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| df=pd.read_csv('/home/tejas/Asia_cup/asiacup.csv') | |
| col1=['Opponent','Format','Selection','Avg Bat Strike Rate','Highest Score','Wicket Taken','Given Extras','Highest Individual wicket','Run Rate','Extras'] | |
| df1=df.drop(col1,axis=1) | |
| Df=df1.head(10) | |
| # with st.sidebar: | |
| # st.table(Df) | |
| df2=df1.dropna() | |
| df2.head(10) | |
| # option = st.selectbox( | |
| # 'How would you like to see?', | |
| # (' Number of times Team won the toss.', 'Number of times Team won the result.', 'Number of matches done on different ground.',"Top 5 player of Match."," Number of times the Team get all out.")) | |
| st.markdown("# CHOOSE THE OPTION") | |
| tab1, tab2, tab3, tab4, tab5 = st.tabs(["Number of times Team won the toss.", "Number of times Team won the result.", "Number of matches done on different ground.","Top 5 player of Match."," Number of times the Team get all out."]) | |
| with tab1: | |
| st.markdown("Q1.} Number of times Team won the toss.") | |
| df3=df2[df2['Toss']=='Win'] | |
| df3.head(10) | |
| df4=df3['Team'].value_counts() | |
| df4 | |
| chart = df4.plot.bar(y='Team', figsize=(10, 5),xlabel='Teams',ylabel='Toss_winning') | |
| st.line_chart(df4) | |
| with tab2: | |
| st.markdown("Q2.}Number of times Team won the result.") | |
| df5=df2[df2['Result']=='Win'] | |
| df5.head(10) | |
| df6=df5['Team'].value_counts() | |
| df6 | |
| st.bar_chart(df6) | |
| with tab3: | |
| st.markdown("Q3.}Number of matches done on different ground") | |
| df7=df1['Ground'].value_counts() | |
| df7 | |
| st.bar_chart(df7) | |
| with tab4: | |
| st.markdown("Q4.}Top 5 player of Match") | |
| df8=df1['Player Of The Match'].value_counts() | |
| df9=df8.head(5) | |
| df9 | |
| st.bar_chart(df9) | |
| with tab5: | |
| st.markdown("Q5.} Number of times the Team get all out.") | |
| df10=df1[df1['Wicket Lost']==10.0] | |
| df11=df10['Team'].value_counts() | |
| df11 | |
| st.line_chart(df11) | |
| st.markdown("""---""") | |
| # st.radio('Which is your favourite Team?',['India','Sri Lanka','Pakisthan','Bangladesh','Afghanistan','Hong Kong','UAE']) | |
| # st.markdown("""---""") | |
| st.markdown("# #Number of times a Team won and Loss the Match.") | |
| df12=df[['Team','Result']] | |
| # df12 | |
| df13=df12[['Team', 'Result']].value_counts().reset_index(name='count') | |
| df14=df13.sort_values(by=['Result']) | |
| # df14 | |
| df15=df14.drop([13,16,12,15,14]) | |
| # df15 | |
| st.bar_chart(df15,x='Team',y='count',height=500) | |
| st.markdown("""---""") | |
| st.markdown("# #Run scored by different Teams in different Year") | |
| df16=df[['Team','Run Scored','Year']] | |
| # df16 | |
| df17=df16.sort_values(by=['Team']) | |
| # df17 | |
| df18=df17.drop([56,57]) | |
| df18 | |
| df19=df18[df18['Team']=='Afghanistan'] | |
| df20=df19.mean() | |
| # df20 | |
| # st.markdown("""---""") | |
| df21=df18[df18['Team']=='Bangladesh'] | |
| df22=df21.mean() | |
| # df22 | |
| # st.markdown("""---""") | |
| df23=df18[df18['Team']=='Hong Kong'] | |
| df24=df23.mean() | |
| # df24 | |
| # st.markdown("""---""") | |
| df25=df18[df18['Team']=='India'] | |
| df26=df25.mean() | |
| # df26 | |
| df27=df18[df18['Team']=='Pakistan'] | |
| df28=df27.mean() | |
| # df28 | |
| # st.markdown("""---""") | |
| df29=df18[df18['Team']=='Sri Lanka'] | |
| df30=df29.mean() | |
| # df30 | |
| # # st.line_chart(df19, y='Run Scored',x='Year') | |
| # df20=df18[df18['Team']=='Sri Lanka'] | |
| # # st.line_chart(df19, y='Run Scored',x='Year') | |
| # df21=df18[df18['Team']=='Pakisthan'] | |
| # # st.line_chart(df19, y='Run Scored',x='Year') | |
| # # st.line_chart(df19, y='Run Scored',x='Year') | |
| st.markdown("""---""") | |
| st.markdown("# #Average run scored by the Team in Asia Cup") | |
| data=[['Afghanistan',187.42],['Bangladesh',185.06],['Hong Kong',135.75],['India',213.68],['Pakistan',217.55],['Sri Lanka',212.55]] | |
| df31 = pd.DataFrame(data, columns=['Team', 'Average_score']) | |
| df31 | |
| # st.bar_chart(df31, y='Average_score',x='Team') | |
| st.markdown("""---""") | |
| import streamlit as st | |
| import extra_streamlit_components as stx | |
| st.markdown("# #Details of match of team India differentiated by runs.") | |
| # chosen_id1= stx.tab_bar(Team=[ | |
| # stx.TabBarItemData(id="Tab1", title='India'), | |
| # stx.TabBarItemData(id="Tab2", title="Sri Lanka"), | |
| chosen_id= stx.tab_bar(data=[ | |
| stx.TabBarItemData(id="tab1", title="Below 100", description="Match Details of Team India getting less than 100 runs"), | |
| # st.text(""), | |
| stx.TabBarItemData(id="tab2", title="100-200", description="Match Details of Team India getting runs between 100 and 200"), | |
| # st.text(""), | |
| stx.TabBarItemData(id="tab3", title="200-300", description="Match Details of Team India getting runs between 200 and 300"), | |
| # st.text(""), | |
| stx.TabBarItemData(id="tab4", title="Above 300", description=" Match Details of Team India getting more than 300 runs")]) | |
| placeholder = st.container() | |
| if chosen_id == "tab1": | |
| placeholder.markdown(f"## Welcome to `{chosen_id}`") | |
| placeholder.info(f"Since we are in {chosen_id}, So details of matches of team India when they scored below 100 is:") | |
| df32=df[df['Team']=='India'] | |
| df33=df32[df32['Run Scored']<100.0000] | |
| # with st.sidebar: | |
| st.table(df33) | |
| # placeholder.image("https://placekitten.com/g/400/200",caption=f"Meowhy from {chosen_id}") | |
| # placeholder.slider("A slider",0,10,5,1) | |
| # placeholder.checkbox("A checkbox",True) | |
| # placeholder.button("A button") | |
| elif chosen_id == "tab2": | |
| placeholder.markdown(f"## Welcome to `{chosen_id}`") | |
| placeholder.info(f"Since we are in {chosen_id} , So details of matches of team India when then scored between 100 and 200 is:") | |
| df34=df[df['Team']=='India'] | |
| df35=df34[(df34['Run Scored']>100.0000)&(df34['Run Scored']<200.0000)] | |
| # with st.sidebar: | |
| st.table(df35) | |
| elif chosen_id == "tab3": | |
| placeholder.markdown(f"## Welcome to `{chosen_id}`") | |
| placeholder.info(f"Since we are in {chosen_id}, So details of matches of team India when they scored between 200 and 300 is:") | |
| df36=df[df['Team']=='India'] | |
| df37=df36[(df36['Run Scored']>200.0000)&(df36['Run Scored']<300.0000)] | |
| # with st.sidebar:/ | |
| st.table(df37) | |
| elif chosen_id == "tab4": | |
| placeholder.markdown(f"## Welcome to `{chosen_id}`") | |
| placeholder.info(f"Since we are in {chosen_id}, So details of matches of team India when they scored above 300 is:") | |
| df38=df[df['Team']=='India'] | |
| df39=df38[df38['Run Scored']>300.0000] | |
| # with st.sidebar: | |
| st.table(df39) | |
| # import streamlit as st | |
| # from streamlit_javascript import st_javascript | |
| # url = st_javascript("await fetch('').then(r => window.parent.location.href)") | |
| # st.write(url) | |
| # st.markdown(""" | |
| # **** | |
| # ### Don't forget to `pip install extra_streamlit_components` | |
| # # """) | |
| # df23=[['df19','df20']] | |
| # df23 | |
| # df23 = pd.DataFrame(columns=['df19','df20']) | |
| # st.line_chart(df23) | |
| # columns=['df19','df20'] | |
| # result = df16.loc[df16['India'] == 1, 'Run Scored'].sum() | |
| # result | |
| # st.selectbox('Which is your favourite Team',['India','Sri Lanka','Pakisthan','Bangladesh','Afghniastan','Hong Kong','UAE']) | |
| # # st.write('You selected:', option) | |
| # st.markdown("""---""") | |
| # st.markdown("Q1.} Number of times Team won the toss.") | |
| # df3=df2[df2['Toss']=='Win'] | |
| # df3.head(10) | |
| # df4=df3['Team'].value_counts() | |
| # df4 | |
| # chart = df4.plot.bar(y='Team', figsize=(10, 5),xlabel='Teams',ylabel='Toss_winning') | |
| # st.line_chart(df4) | |
| # st.markdown("""---""") | |
| # st.markdown("Q2.}Number of times Team won the result.") | |
| # df5=df2[df2['Result']=='Win'] | |
| # df5.head(10) | |
| # df6=df5['Team'].value_counts() | |
| # df6 | |
| # st.bar_chart(df6) | |
| # st.markdown("""---""") | |
| # st.markdown("Q3.}Number of matches done on different ground") | |
| # df7=df1['Ground'].value_counts() | |
| # df7 | |
| # st.bar_chart(df7) | |
| # st.markdown("""---""") | |
| # st.markdown("Q4.}Top 5 player of Match") | |
| # df8=df1['Player Of The Match'].value_counts() | |
| # # df8 | |
| # df9=df8.head(5) | |
| # df9 | |
| # st.bar_chart(df9) | |
| # st.markdown("""---""") | |
| # st.markdown("Q5.} Number of times the Team get all out.") | |
| # df10=df1[df1['Wicket Lost']==10.0] | |
| # # df10 | |
| # df11=df10['Team'].value_counts() | |
| # df11 | |
| # st.line_chart(df11) | |