Assignment4

Introduction-to-Data-Science/Pandas_assignment/Prateek/Assignment_pandas.py

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+
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import numpy as np
+
+
+# In[2]:
+
+
+player_match=pd.read_csv("/home/prateek/Documents/Python/Data_science/Data_science_smp/Ipl/DIM_PLAYER_MATCH.csv",encoding="ISO-8859-1",skiprows=[1])
+
+
+# In[3]:
+
+
+player_match
+
+
+# In[4]:
+
+
+#Q1:
+for i in range(2008,2018):
+    yp=player_match.loc[(player_match.Age_As_on_match<25) & (player_match.Season_year==i)]
+    lyp=len(yp)
+    l=len(player_match)
+    per=(lyp/l)*100
+    team=yp.Player_team.value_counts().index[0]
+    print("% of young players and team having max players  in {:d} season={:f} and {}".format(i,per,team))
+yp=player_match.loc[(player_match.Age_As_on_match<25) & (player_match.is_manofThematch==1.0)]
+lyp=len(yp)
+l=len(player_match)
+per=(lyp/l)*100
+print("Percentage of man of the match award won by young player={}".format(per))
+
+
+# In[11]:
+
+
+#Q2:
+def bowl(s):
+    if(s=="Legbreak" or s=="Legbreak googly"):
+        s="right-handed leg spin bowler"
+    return s
+player_match.Bowling_skill=player_match.Bowling_skill.map(bowl,na_action="ignore")
+
+def null(srs):
+    if(pd.isnull(srs.Bowling_skill)==True):
+        srs.Bowling_skill=srs.Batting_hand
+    return srs
+player_match=player_match.apply(null,axis="columns")
+right=len(player_match.loc[player_match.Bowling_skill.str.contains("right",case=False) & player_match.is_manofThematch==1])
+left=len(player_match.loc[player_match.Bowling_skill.str.contains("left",case=False) & player_match.is_manofThematch==1])
+print("Number of times man of the match recieved by right handed player={} , and left handed player={}".format(right,left))
+
+
+# In[8]:
+
+
+#Q3:
+right_list=list((player_match.loc[player_match.Batting_hand.str.contains("right",case=False) & player_match.is_manofThematch==1]).Player_Name.value_counts().index[0:2])
+
+left_list=list((player_match.loc[player_match.Batting_hand.str.contains("left",case=False) & player_match.is_manofThematch==1]).Player_Name.value_counts().index[0:2])
+
+keeper=((player_match.loc[((player_match.Role_Desc=="Keeper") | (player_match.Role_Desc=="CaptainKeeper")) & player_match.is_manofThematch==1]).Player_Name.value_counts().index[0])
+
+
+pacers=list(player_match.loc[((player_match.Bowling_skill.str.contains("fast")) | (player_match.Bowling_skill.str.contains("medium"))) & player_match.is_manofThematch==1].Player_Name.value_counts().index[10:13])
+
+A_rounder=(player_match.loc[((player_match.Bowling_skill.str.contains("fast")) | (player_match.Bowling_skill.str.contains("medium"))) & player_match.is_manofThematch==1].Player_Name.value_counts().index[8])
+
+spin=list(player_match.loc[((player_match.Bowling_skill.str.contains("break")) | (player_match.Bowling_skill.str.contains("slow")) | (player_match.Bowling_skill.str.contains("spin")) ) & player_match.is_manofThematch==1].Player_Name.value_counts().index[[7,12]])
+#player_match.Bowling_skill.unique()
+
+type(right_list)
+team=right_list+left_list+pacers+spin
+#right_list.append(keeper)
+team.append(keeper)
+team.append(A_rounder)
+print("best team is:",team)
+
+
+# In[10]:
+
+
+#Q4:
+match=pd.read_excel("/home/prateek/Documents/Python/Data_science/Data_science_smp/Ipl/DIM_MATCH.xlsx",encoding="ISO-8859-1")
+match.dropna(axis="index",subset=["match_winner"],inplace=True)
+match
+
+def win_lose(d):
+    if(d.match_winner!=d.Team1):
+        d.match_winner=d.match_winner+"-"+d.Team1
+    else:
+        d.match_winner=d.match_winner+"-"+d.Team2
+    return d
+(match.apply(win_lose,axis="columns")).match_winner.value_counts().index[0]
+