Creating a Pandas Series

Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index. Labels need not be unique but must be a hashable type. The object supports both integer and label-based indexing and provides a host of methods for performing operations involving the index. 

To create Series with any of the methods make sure to import pandas library.

Creating an empty Series: Series() function of Pandas is used to create a series. A basic series, which can be created is an Empty Series. 

Python3

# import pandas as pd
import pandas as pd

# Creating empty series
ser = pd.Series()

print(ser)

Output :

Series([], dtype: float64)

By default, the data type of Series is float. 

Creating a series from array: In order to create a series from NumPy array, we have to import numpy module and have to use array() function. 

Python3

# 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:

By default, the index of the series starts from 0 till the length of series -1.

Creating a series from array with an index: In order to create a series by explicitly proving index instead of the default, we have to provide a list of elements to the index parameter with the same number of elements as it is an array. 

Python3

# 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:

Creating a series from Lists: In order to create a series from list, we have to first create a list after that we can create a series from list. 

Python3

import pandas as pd

# a simple list
list = ['g', 'e', 'e', 'k', 's']

# create series form a list
ser = pd.Series(list)
print(ser)

Output :

Creating a series from Dictionary: In order to create a series from the dictionary, we have to first create a dictionary after that we can make a series using dictionary. Dictionary keys are used to construct indexes of Series.  

Python3

import pandas as pd

# a simple dictionary
dict = {'Geeks': 10,
        'for': 20,
        'geeks': 30}

# create series from dictionary
ser = pd.Series(dict)

print(ser)

Output:

Creating a series from Scalar value: In order to create a series from scalar value, an index must be provided. The scalar value will be repeated to match the length of the index. 

Python3

import pandas as pd

import numpy as np

# giving a scalar value with index
ser = pd.Series(10, index=[0, 1, 2, 3, 4, 5])

print(ser)

Output:

Creating a series using NumPy functions : In order to create a series using numpy function, we can use different function of numpy like numpy.linspace(), numpy.random.radn(). 

Python3

# import pandas and numpy
import pandas as pd
import numpy as np

# series with numpy linspace()
ser1 = pd.Series(np.linspace(3, 33, 3))
print(ser1)

# series with numpy linspace()
ser2 = pd.Series(np.linspace(1, 100, 10))
print(& quot
       \n"       , ser2)

Output:

Creating a Series using range function:

Python3

# code
import pandas as pd
ser=pd.Series(range(10))
print(ser)

Output:
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
dtype: int64

Creating a Series using for loop and list comprehension:

Python3

import pandas as pd
ser=pd.Series(range(1,20,3), index=[x for x in 'abcdefg'])
print(ser)

Output:

a     1
b     4
c     7
d    10
e    13
f    16
g    19
dtype: int64

Creating a Series using mathematical expressions:

Python3

import pandas as pd
import numpy as np
ser=np.arange(10,15)
serobj=pd.Series(data=ser*5,index=ser)
print(serobj)

Output:

10    50
11    55
12    60
13    65
14    70
dtype: int32