Create Pandas series– In this tutorial, we are going to create pandas series. A pandas series is like a NumPy array with labels that can hold an integer, float, string, and constant data.

Syntax

pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)
where
           data : array-like, Iterable, dict, or scalar value
           index : array-like or Index (1d)
           dtype : str, numpy.dtype, or ExtensionDtype, optional
           name : str, optional
           copy  : bool, default False

Creating a Blank Pandas Series

#blank series
import pandas as pd
s = pd.Series()
print(s)

Output of the code

Series([], dtype: float64)

Note: float64 is the default datatype of the Pandas series.

Series with numbers

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

Output of the above code

0    10
1    20
2    30
3    40
4    50
dtype: int64

series with numbers and index

#series with numbers and index import pandas as pd
s = pd.Series([10, 20, 30, 40, 50], 
	       index=[1, 2, 3, 4, 5])
print(s)

Output

1    10
2    20
3    30
4    40
5    50
dtype: int64

series with numbers and char index

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

output

a    10
b    20
c    30
d    40
e    50
dtype: int64

series with constant values

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

output

1    55
2    55
3    55
4    55
5    55
6    55

series with constant and python function

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

output

0     34
1     34
2     34
3     34
4     34
      ..
98    34
99    34
Length: 100, dtype: int64

series with python function

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

output

0      2
1      3
2      4
3      5
4      6
      ..
82    84
83    85
84    86
85    87
86    88
Length: 87, dtype: int64

series with float values

If any value of the data is float then the system automatically convert the datatype of whole series into float.

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

Output

0    10.0
1    20.0
2    30.0
3    40.5
4    50.0
dtype: float64

series with string type values

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

output

1    Welcome to DAV Chander Nagar
2    Welcome to DAV Chander Nagar
3    Welcome to DAV Chander Nagar
4    Welcome to DAV Chander Nagar
5    Welcome to DAV Chander Nagar
6    Welcome to DAV Chander Nagar
dtype: object

series with string and index also in string

# series with string and index also in string
import pandas as pd
s = pd.Series('Welcome to DAV Chander Nagar', 
	index=['rakesh', 'arushi', 'mannat', 'vinay', 
                'pratham'])
print(s)

output

rakesh     		Welcome to DAV Chander Nagar
arushi     		Welcome to DAV Chander Nagar
mannat     	Welcome to DAV Chander Nagar
vinay      		Welcome to DAV Chander Nagar
pratham    	Welcome to DAV Chander Nagar
dtype: object

NOTE : The data type in this case is object.

series with range and for loop

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

output

0     34
1     34
2     34
3     34
4     34
      ..
98    34
99    34
Length: 100, dtype: int64

series with range and for loop

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

output

a    0
b    1
c    2
d    3
e    4
dtype: int64

NOTE: List comprehension has been used to create a list.

Pandas series with two different lists

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

output

GZB        rakesh
Delhi     vishank
Meerut     nikunj
Pune       unnati
Panji       vipul
dtype: object

Create Pandas series with Nan values of numpy

#series with Nan values of numpy
import pandas as pd
import numpy as np
s = pd.Series([10, 20, 30, np.NaN, -34.5, 6])
print(s)

output

0    10.0
1    20.0
2    30.0
3     NaN
4   -34.5
5     6.0
dtype: float64

series from a python Dictionary

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

output

name            rakesh
roll                20
city               Gzb
age                 40
profession    Teaching
dtype: object

Pandas Series using NumPy arange( ) function

import pandas as pd
import numpy as np
data = np.arange(10, 15)
s = pd.Series(data**2, index=data)
print(s)

output

10    100
11    121
12    144
13    169
14    196
dtype: int32

Hope these examples will help to create Pandas series. In the above examples, the pandas module is imported using as. You can learn more about importing a Python module in the Python Module tutorial.