# data[].attributes.body
Eg: S.drop(3)

# data[].attributes.body
s1=pd.Series({'Jan':31,'Feb':28,'Mar':31,})

s1.name=‘Days’

s1.index.name=‘Month’

# data[].attributes.body
series[3]=12
series[[1,2]]=[90,89]
s1.index=['a','b','c']

# data[].attributes.body
Series.index
Series.values
Series.dtype
Series.shape

Series.nbytes

Series.ndim

Series.size

Series.hasnans

Series.empty

# data[].attributes.body
1. Using [ ] -
print(s1['d'])
2. Accessing multiple values
print(s1[['a','c','e']])
3. Slicing
print(s1[:3])
print(s1[-3:])
4. iloc
print(s1.iloc[1:4])
5. loc
print(s1.loc['a':'c'])
6. head()
7. tail()

# data[].attributes.body
s1<2
s1[s1<2]

# data[].attributes.body
print(s1+s2)
In all the operations, if both the series are of the same index, based on the operator, operation will be performed.
If the index are different, NaN will be added.

# data[].attributes.body
import pandas as pd

import numpy as np

s=pd.Series([1,3,np.nan,5,7])

print(s)

# data[].attributes.body
import pandas as pd

import numpy as np

d=np.array([1,2,3,4])

ind=['a','b','c','d']

s1=pd.Series(d*2,index=ind)

print(s1)

# data[].attributes.body
import pandas as pd

import numpy as np

ind=['Aparna','Akash','Akshay','Sonia','Akshitha']

s=pd.Series('Welcome to 2020-21',index=ind)

print(s)

# data[].attributes.body
import pandas as pd

import numpy as np

l=[‘a’,’b’,’c’,’d’]

data = np.array(l)

s = pd.Series(data)

print(s)

# data[].attributes.body
import pandas as pd

d={'Jan':31,'Feb':28,'Mar':31,'Apr':30}

s1=pd.Series(d)

print(s1)

# data[].attributes.body
import pandas as pd

l=[10,20,30,40] #Define list

s1=pd.Series(l)#Create Series

print(s1)

# data[].attributes.body
import pandas as pd

s1=pd.Series()

print(s1)