Descriptive_analysis.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np


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# Loading Dataset


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ipl_auction_df= pd.read_csv('/Users/arijeetbhadra/Downloads/Machine Learning (Codes and Data Files)/Data/IPL IMB381IPL2013.csv')


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ipl_auction_df


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type(ipl_auction_df)


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ipl_auction_df.head(10)


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ipl_auction_df.tail(10)


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list(ipl_auction_df)


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ipl_auction_df.head(10).transpose()


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ipl_auction_df.shape


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ipl_auction_df.info()


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ipl_auction_df[0:5]


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ipl_auction_df[-5:]


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ipl_auction_df['PLAYER NAME'][0:5].reset_index()


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ipl_auction_df[['PLAYER NAME','COUNTRY']][0:10]


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ipl_auction_df[['PLAYER NAME','COUNTRY','PLAYING ROLE']][0:10]


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ipl_auction_df.iloc[4:9,1:4]


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ipl_auction_df.COUNTRY.value_counts()


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ipl_auction_df.TEAM.value_counts()


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ipl_auction_df.COUNTRY.value_counts(normalize=True)*100


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pd.crosstab(ipl_auction_df['AGE'],ipl_auction_df['PLAYING ROLE'])


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ipl_auction_df[['PLAYER NAME','SOLD PRICE']]


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ipl_auction_df[['PLAYER NAME','SOLD PRICE']].sort_values('SOLD PRICE')[0:10]


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ipl_auction_df[['PLAYER NAME','SOLD PRICE']].sort_values('SOLD PRICE',ascending=False)[0:10]


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ipl_auction_df['PREMIUM']=ipl_auction_df['SOLD PRICE']-ipl_auction_df['BASE PRICE']


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ipl_auction_df[['PLAYER NAME','SOLD PRICE','BASE PRICE','PREMIUM']]


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ipl_auction_df[['PLAYER NAME','SOLD PRICE','BASE PRICE','PREMIUM']].sort_values('PREMIUM',ascending=False)


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ipl_auction_df[['PLAYER NAME','SOLD PRICE','BASE PRICE','PREMIUM']].sort_values('PREMIUM',ascending=False)[0:10]


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soldprice_by_age=ipl_auction_df.groupby('AGE')['SOLD PRICE'].mean().reset_index()
soldprice_by_age


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soldprice_by_age_role=ipl_auction_df.groupby(['AGE','PLAYING ROLE'])['SOLD PRICE'].mean().reset_index()
soldprice_by_age_role


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soldprice_comparision=soldprice_by_age_role.merge(soldprice_by_age,on='AGE',how='outer')
soldprice_comparision


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soldprice_comparision.rename(columns={'SOLD PRICE_x':'SOLD_PRICE_AGE_ROLE','SOLD PRICE_y':'SOLD_PRICE_AGE'},inplace=True)
soldprice_comparision.head(10)


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ipl_auction_df[ipl_auction_df['SIXERS']>80][['PLAYER NAME','SIXERS']]


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ipl_auction_df[ipl_auction_df['AVE-BL']<50][['PLAYER NAME','AVE-BL']].sort_values('AVE-BL',ascending=False)[0:10]


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ipl_auction_df.drop('Sl.NO.',inplace=True,axis=1)


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ipl_auction_df


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ipl_auction_df


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#Handling missing Values

#SSV File - Space separted Values


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autos=pd.read_csv('/Users/arijeetbhadra/Downloads/Machine Learning (Codes and Data Files)/Data/auto-mpg.data',sep='\s+',header=None)


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autos.head(10)


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type(autos)


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autos.columns=['mpg','cylinders','displacement','horsepower','weight','acceleration','year','origin','name']
autos.head(5)


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autos.info()


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autos['horsepower']=pd.to_numeric(autos['horsepower'],errors='coerce')
autos.info()


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autos[autos.horsepower.isnull()]


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autos1=autos.dropna(subset=['horsepower'])


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autos1[autos.horsepower.isnull()]


# # Data Visualization

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import matplotlib.pyplot as plt
import seaborn as sn
get_ipython().run_line_magic('matplotlib', 'inline')


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# Bar chart


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soldprice_by_age


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sn.barplot(x='AGE',y='SOLD PRICE',data=soldprice_by_age)


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soldprice_comparision


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sn.barplot(x='AGE',y='SOLD_PRICE_AGE_ROLE',hue='PLAYING ROLE',data=soldprice_comparision)


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# Histogram


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plt.hist([ipl_auction_df['SOLD PRICE']],bins=20)


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# Density or distribution Plot


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sn.distplot(ipl_auction_df['SOLD PRICE'])


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# Scatter Plot


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ipl_batsman_df=ipl_auction_df[ipl_auction_df['PLAYING ROLE']=='Batsman']
plt.scatter(x=ipl_batsman_df['SIXERS'],y=ipl_batsman_df['SOLD PRICE'])


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ipl_allrounder_df=ipl_auction_df[ipl_auction_df['PLAYING ROLE']=='Allrounder']
plt.scatter(x=ipl_allrounder_df['SIXERS'],y=ipl_allrounder_df['SOLD PRICE'])


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ipl_bowler_df=ipl_auction_df[ipl_auction_df['PLAYING ROLE']=='Bowler']
plt.scatter(x=ipl_bowler_df['SIXERS'],y=ipl_bowler_df['SOLD PRICE'])


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# To draw direction of relationship


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sn.regplot(x=ipl_batsman_df['SIXERS'],y=ipl_batsman_df['SOLD PRICE'])


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sn.regplot(x=ipl_allrounder_df['SIXERS'],y=ipl_allrounder_df['SOLD PRICE'])


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sn.regplot(x=ipl_bowler_df['SIXERS'],y=ipl_bowler_df['SOLD PRICE'])


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# Pair Plot


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influential_features=['SR-B','AVE','SIXERS','SOLD PRICE']
sn.pairplot(ipl_auction_df[influential_features],size=2)


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autos.info()


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influential_features1=['mpg','cylinders','displacement','horsepower']
sn.pairplot(autos[influential_features1],size=2)


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# Heatmap


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ipl_auction_df[influential_features].corr()


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sn.heatmap(ipl_auction_df[influential_features].corr(),annot=True)


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