series.py

import pandas as pd
import numpy as np

#blank series
s = pd.Series()
print(s)

#series with numbers
s = pd.Series([10,20,30,40,50])
print(s)

#series with numbers and index

s= pd.Series([10,20,30,40,50],index=[1,2,3,4,5])
print(s)

#series with numbers and char index
s = pd.Series([10,20,30,40,50],index=['a','b','c','d','e'])
print(s)

#series with constant values
s = pd.Series(55,index=[1,2,3,4,5,6])
print(s)

#series with constant and python function
s = pd.Series(34,index= range(100))
print(s)

# series with python function
s = pd.Series(range(2,89))
print(s)

# series with float values
s = pd.Series([10,20,30,40.5,50])
print(s)

# series with string type values
s = pd.Series('Welcome to DAV Chander Nagar',index=[1,2,3,4,5,6])
print(s)

# series with string and index also in string

s= pd.Series('Welcome to DAV Chander Nagar',index=['rakesh','arushi','mannat','vinay','pratham'])
print(s)

# series with range and for loop
s = pd.Series(range(5),index = [x  for x in 'abcde'])
print(s)

# series with two different lists
names =['rakesh','vishank','nikunj','unnati','vipul']
city = [ 'GZB','Delhi','Meerut','Pune','Panji' ]
s = pd.Series(names,index=city)
print(s)


#series with Nan values of numpy

s = pd.Series([10,20,30,np.NaN,-34.5,6])
print(s)

#series from a python Dictionary
dict1={'name':'rakesh','roll':20,'city':'Gzb','age':40,'profession':'Teaching'}
s= pd.Series(dict1)
print(s)


# series using a mathematical expression
data =np.arange(10,15)
s= pd.Series(data**2,index= data)
print(s)