How to Create a Series in Pandas (with 6 Examples)

Updated: January 5, 2023 By: Wolf Post a comment

Pandas Series is a one-dimensional array-like object that stores data of any type. It can be created from a variety of data sources, such as a Python list, Python dictionary, numpy array, CSV file, JSON file, etc.


The syntax for creating a Pandas Series is:



  • data: The data source
  • index: An optional parameter that can be used to specify a label for the Series. If nothing is specified, the values of the series are labeled with their index number.
  • dtype: This parameter is optional. It determines the data type of the values of the Series. If not specified, this will be inferred from data source.
  • name: Optional. This parameter sets the name of the Series
  • copy: Default is false. Copy input data or not.
  • fastpath: This is an internal parameter. You shouldn’t care about it.

If you are new to Pandas and data science, you may find it a bit confusing. However, the following examples will give you a clear understanding of how to create a Pandas series.


Creating a Series from a Python list

This example creates a series from a list of numbers:

import pandas as pd

numbers = [9, 8, 7, 6, 5] 
number_series = pd.Series(numbers)


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

This code snippet produces a series from a list of strings with customized labels:

import pandas as pd

colors = ['red', 'green', 'blue', 'yellow', 'orange']
labels = ['a', 'b', 'c', 'd', 'e']
color_series = pd.Series(colors, index=labels)


a       red
b     green
c      blue
d    yellow
e    orange
dtype: object

Creating a Series from a Python dictionary

Let’s say we have a dictionary whoose keys are job titles, and values are the corresponding salaries. We’ can create a Pandas Series from this dictionary like this:

import pandas as pd

jobs_and_salaries = {
    'Data Scientist': 120000,
    'Software Engineer': 100000,
    'Data Analyst': 90000,
    'Business Analyst': 80000,
    'Project Manager': 80000

series = pd.Series(jobs_and_salaries)


Data Scientist       120000
Software Engineer    100000
Data Analyst          90000
Business Analyst      80000
Project Manager       80000
dtype: int64

The labels are the names of the occupations and the values are the respective salaries.

Creating a Series from a Numpy array

You can construct a Series from a Numpy array as follows:

data = np.array(['a', 'b', 'c', 'd'])
s = pd.Series(data)


0    a
1    b
2    c
3    d
dtype: object

Creating a Series from scalar value

The code:

s = pd.Series(4.4, index=['a', 'b', 'c', 'd', 'e'])


a    4.4
b    4.4
c    4.4
d    4.4
e    4.4
dtype: float64

Creating a Series from a DataFrame

You can create a Pandas Series from a Pandas DataFrame by using the DataFrame.squeeze() method. It will convert a single column of the source DataFrame into a Series.

In this example, we have a DataFrame (about some products) with a column named Price. We’ll create a Series that contains the values from the Price column:

import pandas as pd
data = {
    'Products': ['Laptop','Tablet', 'Phone', 'Keyboard', 'Mouse'],
    'Brand': ['A', 'B', 'C', 'D', 'E'],
    'Price': [1000, 800, 1300, 150, 100]
df = pd.DataFrame(data, columns=['Products', 'Brand', 'Price'])
price_series = df['Price'].squeeze()


0    1000
1     800
2    1300
3     150
4     100
Name: Price, dtype: int64

Creating a Series from a CSV file

In order to create a Pandas Series from a CSV file, you can use the pandas.read_csv() function. This function takes the file path as an argument and returns a DataFrame. You can then use the DataFrame to generate a Series by selecting a column from the DataFrame.

This example creates a DataFrame from an online CSV file that stores sample data about employees in a fiction company. Then, it will return 2 Series as first_name_series (containing first names of employees) and last_name_series (containing last names of employees):

import pandas as pd

import ssl
ssl._create_default_https_context = ssl._create_unverified_context

file_path = ''
dataframe = pd.read_csv(file_path, storage_options={'User-Agent': 'Mozilla/5.0'})

first_name_series = dataframe['first_name']
last_name_series = dataframe['last_name']



0       Jose
1    Douglas
2     Sherry
3    Charles
4     Sharon
Name: first_name, dtype: object

0     Lopez
1    Carter
2    Foster
3    Fisher
4    Hunter
Name: last_name, dtype: object

f you find something confusing or error in the examples above, please leave comments. Happy coding and have a nice day!