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dash_fun.py
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dash_fun.py
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# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly.graph_objs as go
import plotly.express as px
# %%
dl = pd.read_excel('data/deliveries.xlsx')
df=pd.read_excel('data/data.xlsx')
dl.head()
# %%
team_ground={'Sunrisers Hyderabad':['Rajiv Gandhi Intl. Cricket Stadium'], 'Mumbai Indians':['Wankhede Stadium','Brabourne Stadium']
, 'Gujarat Lions':['Saurashtra Cricket Association Stadium','Green Park'],'Rising Pune Supergiant':['Maharashtra Cricket Association Stadium'], 'Royal Challengers Bangalore':['M. Chinnaswamy Stadium'],'Kolkata Knight Riders': ['Eden Gardens'],
'Delhi Daredevils':['Feroz Shah Kotla Ground'], 'Kings XI Punjab':['Punjab Cricket Association Stadium, Mohali','Himachal Pradesh Cricket Association Stadium','Holkar Cricket Stadium'],
'Chennai Super Kings':['MA Chidambaram Stadium, Chepauk'], 'Rajasthan Royals': ['Sawai Mansingh Stadium'],
'Deccan Chargers':['Rajiv Gandhi Intl. Cricket Stadium','Vidarbha Cricket Association Stadium, Jamtha',],
'Kochi Tuskers Kerala':['Nehru Stadium'], 'Pune Warriors':['Dr DY Patil Sports Academy', 'Maharashtra Cricket Association Stadium','Subrata Roy Sahara Stadium'],
'Delhi Capitals':['Feroz Shah Kotla Ground']}
# %%
colors={'Sunrisers Hyderabad':['rgb(252, 111, 3)','black'],'Kolkata Knight Riders':['rgb(34, 19, 77)','rgb(224, 166, 7)'], 'Mumbai Indians':['rgb(13, 19, 140)','rgb(22, 149, 181)'],'Gujarat Lions':['rgb(212, 82, 6)','rgb(9, 44, 148)'], 'Rising Pune Supergiant':['rgb(71, 27, 133)','rgb(210, 38, 252)'], 'Royal Challengers Bangalore':['rgb(199, 12, 24)', 'black'],'Delhi Daredevils':['rgb(5, 20, 79)','rgb(168, 2, 7)'],
'Kings XI Punjab':['rgb(227, 5, 30)','rgb(218, 195, 219)'],
'Chennai Super Kings':['rgb(222, 215, 11)','rgb(222, 127, 11)'],
'Deccan Chargers':['rgb(237, 230, 126)','rgb(18, 35, 102)'],
'Kochi Tuskers Kerala':['rgb(209, 104, 23)','rgb(131, 23, 209)'],
'Pune Warriors':['rgb(19, 80, 112)','rgb(38, 55, 64)'],'Delhi Capitals':['rgb(163, 7, 14)','rgb(25, 7, 115)'], 'Rajasthan Royals':['rgb(224, 61, 107)','rgb(80, 35, 153)']}
# %%
def team_home_performance(selected_team):
#filtered_df = df[df['team1'] ==selected_team]
h=0
a=0
hw=0
aw=0
f=0#0home 1 away
for i,k,j,l in zip(df['team1'],df['team2'],df['venue'],df['winner']):
f=1
if ((i==selected_team or k==selected_team)):
for s in team_ground[selected_team]:
if(j==s):
f=0
if(f==0):
h=h+1
if(l==selected_team):
hw=hw+1
else:
a=a+1
if(l==selected_team):
aw=aw+1
home_rec=[hw,h-hw]
away_rec=[aw,a-aw]
rec=[hw+aw,(h-hw)+(a-aw)]
return (home_rec)
# %%
# %%
def team_away_performance(selected_team):
#filtered_df = df[df['team1'] ==selected_team]
h=0
a=0
hw=0
aw=0
f=0#0home 1 away
for i,k,j,l in zip(df['team1'],df['team2'],df['venue'],df['winner']):
f=1
if ((i==selected_team or k==selected_team)):
for s in team_ground[selected_team]:
if(j==s):
f=0
if(f==0):
h=h+1
if(l==selected_team):
hw=hw+1
else:
a=a+1
if(l==selected_team):
aw=aw+1
home_rec=[hw,h-hw]
away_rec=[aw,a-aw]
rec=[hw+aw,(h-hw)+(a-aw)]
return (away_rec)
# %%
def loss_stat(selected_team):
oponent_home=dict()
oponent_away=dict()
f=0
#oponent initialization
teams=df['team1'].unique()
for i in teams:
if(i!=selected_team):
oponent_home[i]=0
oponent_away[i]=0
for i,j,r,k,l in zip(df['team1'],df['team2'],df['result'],df['winner'],df['venue']):
f=1
if(r=='normal'):
if(i==selected_team or j==selected_team):
if(k!=selected_team and k!='nan'):
#print(k)
for s in team_ground[selected_team]:
#print(s,"-",l)
if(s==l):
f=0
if(f==0):
#pass
oponent_home[k]=int(oponent_home[k])+1
#print(oponent_home[k],"-",k)
if(f==1):
#pass
oponent_away[k]=int(oponent_away[k])+1
#print(oponent_away[k],"-",k)
loss_home=pd.DataFrame(oponent_home.items())
loss_home=pd.DataFrame(oponent_home.items(), columns=['Team', 'Home_Losses'])
loss_away=pd.DataFrame(oponent_away.items())
loss_away=pd.DataFrame(oponent_away.items(), columns=['Team', 'Away_Losses'])
losses=pd.merge(loss_home,loss_away,how='inner',on='Team')
return (losses)
# %%
# %%
def win_stat(selected_team):
oponent_home=dict()
oponent_away=dict()
f=0
op=""
#oponent initialization
teams=df['team1'].unique()
for i in teams:
if(i!=selected_team):
oponent_home[i]=0
oponent_away[i]=0
for i,j,r,k,l in zip(df['team1'],df['team2'],df['result'],df['winner'],df['venue']):
f=1
if(r=='normal'):
if(i==selected_team or j==selected_team):
if(i!=selected_team):
op=i
if(j!=selected_team):
op=j
if(k==selected_team):
for s in team_ground[selected_team]:
#print(s,"-",l)
if(s==l):
f=0
if(f==0):
oponent_home[op]=int(oponent_home[op])+1
if(f==1):
oponent_away[op]=int(oponent_away[op])+1
win_home=pd.DataFrame(oponent_home.items())
win_home=pd.DataFrame(oponent_home.items(), columns=['Team', 'Home_Wins'])
win_away=pd.DataFrame(oponent_away.items())
win_away=pd.DataFrame(oponent_away.items(), columns=['Team', 'Away_Wins'])
wins=pd.merge(win_home,win_away,how='inner',on='Team')
return (wins)
# %%
# %%
def team_batting(selected_team):
bn=[]
tr=[]
sbt=pd.DataFrame(columns=['batsman','batsman_runs'])
batsmen = df[['id','season']].merge(dl, left_on = 'id', right_on = 'match_id', how = 'left').drop('id', axis = 1)
season=batsmen.groupby(['batsman','batting_team'])['batsman_runs'].sum().reset_index()
#print(season)
for i,j,k in zip(season['batsman'],season['batting_team'],season['batsman_runs']):
if(j==selected_team):
bn.append(i)
tr.append(k)
sbt['batsman']=bn
sbt['batsman_runs']=tr
sbt=sbt.sort_values(by='batsman_runs',ascending=False).reset_index(drop=True)
sbt_ret=sbt.head(10)
return (sbt_ret)
# %%
# %%
def team_6s(selected_team):
bn=[]
tr=[]
sbt=pd.DataFrame(columns=['batsman','no_of_6s'])
df_sixes = dl[['batsman','batting_team','batsman_runs']][dl.batsman_runs==6].groupby(['batsman','batting_team'])['batsman_runs'].count() .reset_index()
for i,j,k in zip(df_sixes['batsman'],df_sixes['batting_team'],df_sixes['batsman_runs']):
if(j==selected_team):
bn.append(i)
tr.append(k)
sbt['batsman']=bn
sbt['no_of_6s']=tr
sbt=sbt.sort_values(by='no_of_6s',ascending=False)
sbt_ret=sbt.head(10)
return (sbt_ret)
# %%
# %%
def team_4s(selected_team):
bn=[]
tr=[]
sbt=pd.DataFrame(columns=['batsman','no_of_4s'])
df_fours = dl[['batsman','batting_team','batsman_runs']][dl.batsman_runs==4].groupby(['batsman','batting_team'])['batsman_runs'].count() .reset_index()
for i,j,k in zip(df_fours['batsman'],df_fours['batting_team'],df_fours['batsman_runs']):
if(j==selected_team):
bn.append(i)
tr.append(k)
sbt['batsman']=bn
sbt['no_of_4s']=tr
sbt=sbt.sort_values(by='no_of_4s',ascending=False)
sbt_ret=sbt.head(10)
return (sbt_ret)
# %%
# %%
def team_bowling(selected_team):
bn=[]
tr=[]
sbt=pd.DataFrame(columns=['bowlers','wickets'])
bowlers=dl.groupby('bowler').sum().reset_index()
bowl=dl['bowler'].value_counts().reset_index()
bowlers=bowlers.merge(bowl,left_on='bowler',right_on='index',how='left')
bowlers=bowlers[['bowler_x','total_runs','bowler_y']]
bowlers.rename({'bowler_x':'bowler','total_runs':'runs_given','bowler_y':'balls'},axis=1,inplace=True)
bowlers['overs']=(bowlers['balls']//6)
dismissal_kinds = ["bowled", "caught", "lbw", "stumped", "caught and bowled", "hit wicket"]
ct=dl[dl["dismissal_kind"].isin(dismissal_kinds)]
ct=ct.groupby(['bowler','bowling_team']).size().reset_index()
ct=ct.rename(columns={0:'wickets'}).sort_values(by='wickets')
#print(bowlers)
for i,j,k in zip(ct['bowler'],ct['bowling_team'],ct['wickets']):
if(j==selected_team):
bn.append(i)
tr.append(k)
sbt['bowlers']=bn
sbt['wickets']=tr
sbt=sbt.sort_values(by='wickets',ascending=False).reset_index(drop=True)
sbt_ret=sbt.head(10)
return (sbt_ret)
# %%
# %%
def venue_score(selected_venue,ssn):
sn=[]
mtc=[]
avg1=[]
avg2=[]
std=[]
batsmen = df[['id','season','venue']].merge(dl, left_on = 'id', right_on = 'match_id', how = 'left').drop('id', axis = 1)
avgruns_each_season=df.groupby(['season','venue']).count().id.reset_index()
avgruns_each_season.rename(columns={'id':'matches'},inplace=1)
season=batsmen[batsmen.inning==1].groupby(['season','venue'])['total_runs'].sum().reset_index()
avgruns_each_season['total_runs_1']=season['total_runs']
season=batsmen[batsmen.inning==2].groupby(['season','venue'])['total_runs'].sum().reset_index()
avgruns_each_season['total_runs_2']=season['total_runs']
avgruns_each_season['average_runs_1st_inn']=avgruns_each_season['total_runs_1']/avgruns_each_season['matches']
avgruns_each_season['average_runs_2nd_inn']=avgruns_each_season['total_runs_2']/avgruns_each_season['matches']
lat=['17.4065','18.9389','12.9788','22.5646','30.6909','13.0629','26.8940','28.6379']
lon=['78.5505','72.8258','77.5998','88.3433','76.7375','80.2792','75.8032','77.2432']
sta=['Rajiv Gandhi Intl. Cricket Stadium','Wankhede Stadium','M. Chinnaswamy Stadium','Eden Gardens',
'IS Bindra Stadium','MA Chidambaram Stadium, Chepauk','Sawai Mansingh Stadium','Feroz Shah Kotla']
ven_info=pd.DataFrame(columns=['Stadium','Latitude','Longitude'])
ven_info['Stadium']=sta
ven_info['Latitude']=lat
ven_info['Longitude']=lon
for s,v,m,a1,a2 in zip(avgruns_each_season['season'],avgruns_each_season['venue'],avgruns_each_season['matches'],avgruns_each_season['average_runs_1st_inn'],avgruns_each_season['average_runs_2nd_inn']):
#print(selected_venue)
if(v==selected_venue):
#print(selected_venue)
std.append(v)
sn.append(s)
mtc.append(m)
avg1.append(a1)
avg2.append(a2)
venue_avg=pd.DataFrame(columns=['season','Stadium','matches','first_innings_avg','second_innings_avg'])
venue_avg['season']=sn
venue_avg['Stadium']=std
venue_avg['matches']=mtc
venue_avg['first_innings_avg']=avg1
venue_avg['second_innings_avg']=avg2
va=pd.merge(venue_avg[venue_avg.season==ssn],ven_info[ven_info.Stadium==selected_venue],on='Stadium')
#print(va)
return (va)
# %%
# %%
def batsman_season(selected_batsman):
sn=[]
rn=[]
sbt=pd.DataFrame(columns=['season','batsman_runs'])
batsmen = df[['id','season']].merge(dl, left_on = 'id', right_on = 'match_id', how = 'left').drop('id', axis = 1)
season=batsmen.groupby(['batsman','season'])['batsman_runs'].sum().reset_index()
#print(season)
for i,j,k in zip(season['batsman'],season['season'],season['batsman_runs']):
if(i==selected_batsman):
sn.append(j)
rn.append(k)
sbt['season']=sn
sbt['batsman_runs']=rn
sbt=sbt.sort_values(by='batsman_runs',ascending=False).reset_index(drop=True)
sbt_ret=sbt
return (sbt_ret)
# %%
# %%
def batsman_opponent(selected_batsman):
sn=[]
rn=[]
sbt=pd.DataFrame(columns=['opponent','batsman_runs'])
batsmen = df[['id','season']].merge(dl, left_on = 'id', right_on = 'match_id', how = 'left').drop('id', axis = 1)
season=batsmen.groupby(['batsman','bowling_team'])['batsman_runs'].sum().reset_index()
#print(season)
for i,j,k in zip(season['batsman'],season['bowling_team'],season['batsman_runs']):
if(i==selected_batsman):
sn.append(j)
rn.append(k)
sbt['opponent']=sn
sbt['batsman_runs']=rn
sbt=sbt.sort_values(by='batsman_runs',ascending=False).reset_index(drop=True)
sbt_ret=sbt
return (sbt_ret)
# %%
# %%
def bowler_opp_team(selected_bowler):
ot=[]
wk=[]
sbt=pd.DataFrame(columns=['opponent','wickets'])
bowlers=dl.groupby('bowler').sum().reset_index()
bowl=dl['bowler'].value_counts().reset_index()
bowlers=bowlers.merge(bowl,left_on='bowler',right_on='index',how='left')
bowlers=bowlers[['bowler_x','total_runs','bowler_y']]
bowlers.rename({'bowler_x':'bowler','total_runs':'runs_given','bowler_y':'balls'},axis=1,inplace=True)
bowlers['overs']=(bowlers['balls']//6)
dismissal_kinds = ["bowled", "caught", "lbw", "stumped", "caught and bowled", "hit wicket"]
ct=dl[dl["dismissal_kind"].isin(dismissal_kinds)]
ct=ct.groupby(['bowler','batting_team']).size().reset_index()
ct=ct.rename(columns={0:'wickets'}).sort_values(by='wickets',ascending=False)
for b,o,w in zip(ct['bowler'],ct['batting_team'],ct['wickets']):
if(b==selected_bowler):
ot.append(o)
wk.append(w)
sbt['opponent']=ot
sbt['wickets']=wk
sbt=sbt.sort_values(by='wickets',ascending=False).reset_index(drop=True)
sbt_ret=sbt
return (sbt_ret)
# %%
# %%
def bowler_opp_batsman(selected_bowler):
ot=[]
wk=[]
sbt=pd.DataFrame(columns=['opponent','wickets'])
bowlers=dl.groupby('bowler').sum().reset_index()
bowl=dl['bowler'].value_counts().reset_index()
bowlers=bowlers.merge(bowl,left_on='bowler',right_on='index',how='left')
bowlers=bowlers[['bowler_x','total_runs','bowler_y']]
bowlers.rename({'bowler_x':'bowler','total_runs':'runs_given','bowler_y':'balls'},axis=1,inplace=True)
bowlers['overs']=(bowlers['balls']//6)
dismissal_kinds = ["bowled", "caught", "lbw", "stumped", "caught and bowled", "hit wicket"]
ct=dl[dl["dismissal_kind"].isin(dismissal_kinds)]
ct=ct.groupby(['bowler','batsman']).size().reset_index()
ct=ct.rename(columns={0:'wickets'}).sort_values(by='wickets',ascending=False)
for b,o,w in zip(ct['bowler'],ct['batsman'],ct['wickets']):
if(b==selected_bowler):
ot.append(o)
wk.append(w)
sbt['opponent']=ot
sbt['wickets']=wk
sbt=sbt.sort_values(by='wickets',ascending=False).reset_index(drop=True)
sbt_ret=sbt.head(10)
return (sbt_ret)
# %%
# %%
'''def win_stat(selected_team):
oponent_home=dict()
oponent_away=dict()
f=0
op=""
#oponent initialization
teams=df['team1'].unique()
for i in teams:
if(i!=selected_team):
oponent_home[i]=0
oponent_away[i]=0
for i,j,k,l in zip(df['team1'],df['team2'],df['winner'],df['venue']):
f=1
if(i==selected_team or j==selected_team):
if(i!=selected_team):
op=i
if(j!=selected_team):
op=j
if(k==selected_team):
for s in team_ground[selected_team]:
#print(s,"-",l)
if(s==l):
f=0
if(f==0):
oponent_home[op]=int(oponent_home[op])+1
if(f==1):
oponent_away[op]=int(oponent_away[op])+1
win_home=pd.DataFrame(oponent_home.items())
win_home=pd.DataFrame(oponent_home.items(), columns=['Team', 'Home_Wins'])
win_away=pd.DataFrame(oponent_away.items())
win_away=pd.DataFrame(oponent_away.items(), columns=['Team', 'Away_Wins'])
wins=pd.merge(win_home,win_away,how='inner',on='Team')
print(wins)
c=0
for s in colors[selected_team]:
if(c==0):
hc=s
if(c==1):
ac=s
c=c+1
fig = go.Figure(data=[
go.Bar(name='Home Wins', x=wins['Team'], y=wins['Home_Wins'],marker_color=hc),
go.Bar(name='Away Wins', x=wins['Team'], y=wins['Away_Wins'],marker_color=ac)
])
# Change the bar mode
#fig.update_traces(marker_color=colors[selected_team], marker_line_color='rgb(8,48,107)',
# marker_line_width=1.5, opacity=0.6)
#fig.update_layout(barmode='group')
#fig.show()'''
# %%
'''
bowlers=dl.groupby('bowler').sum().reset_index()
bowl=dl['bowler'].value_counts().reset_index()
bowlers=bowlers.merge(bowl,left_on='bowler',right_on='index',how='left')
bowlers=bowlers[['bowler_x','total_runs','bowler_y']]
bowlers.rename({'bowler_x':'bowler','total_runs':'runs_given','bowler_y':'balls'},axis=1,inplace=True)
bowlers['overs']=(bowlers['balls']//6)
dismissal_kinds = ["bowled", "caught", "lbw", "stumped", "caught and bowled", "hit wicket"]
ct=dl[dl["dismissal_kind"].isin(dismissal_kinds)]
ct=ct.groupby(['bowler','batsman']).size().reset_index()
ct=ct.rename(columns={0:'wickets'}).sort_values(by='wickets',ascending=False)
for b,o,w in zip(ct['bowler'],ct['batsman'],ct['wickets']):
if(b=='SP Narine'):
print(b,"\t",o,"\t",w,"\t")
'''
# %%
'''batsmen = df[['id','season']].merge(dl, left_on = 'id', right_on = 'match_id', how = 'left').drop('id', axis = 1)
season=batsmen.groupby(['batsman','season'])['batsman_runs'].sum().reset_index()
season=df.groupby(['team1','season']).count().id.reset_index()
season.rename(columns={'id':'matches'},inplace=1)
print(season.head(4))
'''
# %%
'''
df_strike_rate = dl.groupby(['batsman']).agg({'ball':'count','batsman_runs':'mean'}).sort_values(by='batsman_runs',ascending=False)
df_strike_rate.rename(columns ={'batsman_runs' : 'strike rate'}, inplace=True)
df_runs_per_match = dl.groupby(['batsman','match_id']).agg({'batsman_runs':'sum'})
df_total_runs = df_runs_per_match.groupby(['batsman']).agg({'sum' ,'mean','count'})
df_total_runs.rename(columns ={'sum' : 'batsman run','count' : 'match count','mean' :'average score'}, inplace=True)
df_total_runs.columns = df_total_runs.columns.droplevel()
df_sixes = dl[['batsman','batting_team','batsman_runs']][dl.batsman_runs==6].groupby(['batsman','batting_team']).agg({'batsman_runs':'count'})
df_four = dl[['batsman','batting_team','batsman_runs']][dl.batsman_runs==4].groupby(['batsman','batting_team']).agg({'batsman_runs':'count'})
df_batsman_stat = pd.merge(pd.merge(pd.merge(df_strike_rate,df_total_runs, left_index=True, right_index=True),
df_sixes, left_index=True, right_index=True),df_four, left_index=True, right_index=True)
print(df_runs_per_match.head(10))
'''
# %%
'''
print(df_batsman_stat.columns)
df_batsman_stat.rename(columns = {'ball' : 'ball', 'strike rate':'strike_rate','batsman run' : 'batsman_run',
'match count' : 'match_count','average score' : 'average_score' ,'batsman_runs_x' :'six',
'batsman_runs_y':'four'},inplace=True)
df_batsman_stat['strike_rate'] = df_batsman_stat['strike_rate']*100
df_batsman_stat.sort_values(by='batsman_run',ascending=False,inplace=True)
#df_batsman_stat.sort_values(by='batsman_run',ascending=False)
#df_batsman_stat.reset_index(inplace=True)
print(df_batsman_stat.head(10))
bowlers=dl.groupby('bowler').sum().reset_index()
bowl=dl['bowler'].value_counts().reset_index()
print(bowl)
bowlers=bowlers.merge(bowl,left_on='bowler',right_on='index',how='left')
bowlers=bowlers[['bowler_x','total_runs','bowler_y']]
bowlers.rename({'bowler_x':'bowler','total_runs':'runs_given','bowler_y':'balls'},axis=1,inplace=True)
bowlers['overs']=(bowlers['balls']//6)
bowlers['economy']=(bowlers['runs_given']/bowlers['overs'])
print(bowl.columns)
'''