DAY 25-PANDAS

import pandas as pd 
df = pd.read_csv("https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv")

DROP and SET INDEX

df.drop('PassengerId',axis=1,inplace=True)//permanently drops passengerID
df.drop(3,inplace=True)//drops 3rd row

df.set_index("Name",inplace=True)
df.reset_index()

`d = {'key1' :[3,4,5,6,7],
'key2':[5,6,7,8,6],
'key3':[4,5,6,7,8]
}
pd.DataFrame(d)
df1 = pd.read_csv('taxonomy.csv')

`

df1.dropna()
df1.dropna(inplace=True)
df1.dropna(axis=1)
df2.fillna("sudh")
df.reset_index(inplace=True)
g = df.groupby('Survived')
g.sum()

g.mean()
g1 = df.groupby('Pclass')
g1.max().transpose()

//concatinate
df5 = df[['Name',   'Survived', 'Pclass']][0:5]
df6 = df[['Name',   'Survived', 'Pclass']][5:10]
pd.concat([df5,df6])

MERGE

data1 = pd.DataFrame({'key1':[1,2,4,5,6],
                      'key2':[4,5,6,7,8],
                      'key3':[3,4,5,6,6]
}
)
data2 = pd.DataFrame({'key1':[1,2,45,6,67],
                      'key4':[56,5,6,7,8],
                      'key5':[3,56,5,6,6]
}
)
pd.merge(data1,data2)
pd.merge(data1,data2,how = 'left')
pd.merge(data1,data2,how = 'right')
pd.merge(data1,data2,how = 'outer',on = 'key1')
pd.merge(data1,data2,how = 'cross')

similarly to merge we have join

data1.join(data2,how='right')
data1.join(data2,how='inner')
data1.join(data2,how='outer')
data1.join(data2,how='cross')

df['Fare_INR'] = df['Fare'].apply(lambda x : x*80)
df['name_len'] = df['Name'].apply(len)