SERIES CONCEPT OF PANDAS

import pandas as pd

Ser_Weight= pd.Series(data=[42, 75, 56, 45, 55],

 index =['Stud1', 'Stud2', 'Stud3', 'Stud4', 'Stud5'])

Ser_Age = pd.Series(data=[15, 17, 16, 15, 18],

index =['Stud1', 'Stud2', 'Stud3', 'Stud4', 'Stud5'])

Ser_Name= pd.Series(data=['Arnav', 'Kailash', 'Mohan' , 'Manavi' , 'Kavya'],

index =['Stud1', 'Stud2', 'Stud3', 'Stud4', 'Stud5'])

dtf1 =pd.DataFrame({'Weight' : Ser_Weight, 'Age' :Ser_Age, 'Name': Ser_Name})

print(dtf1)

#print(dtf1.T)

#print(dtf1.index)

#print(dtf1.columns)

#print(dtf1.axes)

#print(dtf1.dtypes)

#print(dtf1.size)

#print(dtf1.shape)

#print(dtf1.ndim)

#print(dtf1.empty)

#print(dtf1.count())

#print(dtf1.count(1))

#print(dtf1.T)

#print(dtf1[['Weight', 'Name']])

#print(dtf1.loc['Stud1': 'Stud3'])

#print(dtf1.iloc[0:4, 0:3])

#print(dtf1.Name['Stud3'])

#print(dtf1.at['Stud1', 'Age'])

#print(dtf1.iat[0,2])

#dtf1.Name['Stud3']='Akash'

#print(dtf1)

#dtf1['Subject']=['English', 'Maths', 'Chemistry', 'Physics', 'IP' ]

#print(dtf1)

#dtf1.at['Stud3']='Bipul'

#print(dtf1)

#dtf1.loc['Stud3']='Bipul'

#print(dtf1)

#dtf1.at[ : , 'Age']=16

#print(dtf1)

#dtf1.loc[ : , 'Age']=16

#print(dtf1)

#deldtf1['Age']

#print(dtf1)

dtf1.pop('Weight')

print(dtf1)

dtf2=dtf1.drop(['Stud1'])

print(dtf2)

import numpy as np

import pandas as pd

obj1=pd.Series(data=[0, 1, 2, 3, 4])

obj2=pd.Series(data=[2.5, 3.5, 4.5, 9.5], index=['a', 'b', 'c', 'd'])

#print(obj1)

#print(obj2)

#print(obj1.index)

#print(obj2.index)

#print(obj1.values)

#print(obj2.values)

#print(obj1.dtype)

#print(obj2.dtype)

#print(obj1.itemsize)

#print(obj2.itemsize)

#print(obj1.shape)

#print(obj2.shape)

#print(obj1.ndim)

#print(obj2.ndim)

#print(obj1.size)

#print(obj2.size)

#print(obj1.nbytes)

print(obj2.nbytes)