Creation of a series of pandas

NumPy | Python Methods and Functions

Tags do not have to be unique, but must be hashable. The object supports both integer and label-based indexing and provides many methods for performing index operations.

Create empty series:
Basic series, which you can create, — this is an Empty Series.

# import pandas as pd

import pandas as pd

   
  # Create empty series  
   ser   =   pd.Series () 
   
   print   (ser)

Exit:

 Series ([], dtype: float64)

Creating a series from an array:
To create a series from an array, we should import the numpy module and use the array () function.

# import pandas as pd

import pandas as pd

# import numpy as np

import numpy as np

# simple array

data = np.array ([ `g` , ` e` , `e` , `k` , ` s` ])

ser = pd.Series (data)

print (ser)

Output:

Creating a series from an array with an index:
To create a series from an array with an index, we must provide an index with the same element number as in the array.

# import pandas as pd

import pandas as pd

# import numpy as np

import numpy as np

# simple array

data = np.array ([ `g` , `e` , ` e` , `k` , ` s` ])

   
  # providing an index  
   ser   =   pd.Series (data, index   =   [  10  ,   11  ,   12  ,   13  ,   14  ]) 
   print   (ser)

Output:

import pandas as pd

# simple list

list = [ `g` , ` e` , `e` , ` k` , `s` ]


# create series from the list

ser = pd.Series ( list )
print (ser)

Output:

import pandas as pd

# simple dictionary

dict = { `Geeks` : 10 ,

`for` : 20 ,

` geeks` : 30 }

# create series from dictionary < / code>
ser = pd.Series ( dict ) print (ser)

Output:

Create a series from a scalar value:
To create a series from a scalar value, you must specify an index. The scalar value will iterate to match the length of the index.

import pandas as pd

import numpy as np

# giving a scalar value with an index

ser = pd.Series ( 10 , index = [ 0 , 1 , 2 , 3 , 4 , 5 ])
   print (ser)

Output:

Creating a series using numpy functions :
To create a series using numpy function, we can use various numpy functions such as numpy.linspace () , randn-python/ target=_blank> numpy.random.radn ( ) .

# import of pandas and nudies

import pandas as pd

import numpy as np


# NumPy series Linspace ()

ser1 = pd.Series (np. linspace ( 3 , 33 , 3 ))

print (ser1)

# series with NumPy Linspace ()

ser2 = pd.Series (np.linspace ( 1 , 100 , 10 ))
print ( "" , ser2)

Output:

Creation of a series of pandas

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