17.6 C
London
Tuesday, July 22, 2025
HomePandas in PythonDataFrame Functions in PythonHow to Convert Pandas Series to DataFrame (With Examples)

How to Convert Pandas Series to DataFrame (With Examples)

Related stories

Learn About Opening an Automobile Repair Shop in India

Starting a car repair shop is quite a good...

Unlocking the Power: Embracing the Benefits of Tax-Free Investing

  Unlocking the Power: Embracing the Benefits of Tax-Free Investing For...

Income Splitting in Canada for 2023

  Income Splitting in Canada for 2023 The federal government’s expanded...

Can I Deduct Home Office Expenses on my Tax Return 2023?

Can I Deduct Home Office Expenses on my Tax...

Canadian Tax – Personal Tax Deadline 2022

  Canadian Tax – Personal Tax Deadline 2022 Resources and Tools...

You can use the following basic syntax to convert a pandas Series to a pandas DataFrame:

my_df = my_series.to_frame(name='column_name')

The following examples show how to use this syntax in practice.

Example 1: Convert One Series to Pandas DataFrame

Suppose we have the following pandas Series:

import pandas as pd

#create pandas Series
my_series = pd.Series([3, 4, 4, 8, 14, 17, 20])

#view pandas Series
print(my_series)

0     3
1     4
2     4
3     8
4    14
5    17
6    20
dtype: int64

#view object type
print(type(my_series))


We can use the to_frame() function to quickly convert this pandas Series to a pandas DataFrame:

#convert Series to DataFrame and specify column name to be 'values'
my_df = my_series.to_frame(name='values')

#view pandas DataFrame 
print(my_df)

   values
0       3
1       4
2       4
3       8
4      14
5      17
6      20

#view object type 
print(type(my_df))


Example 2: Convert Multiple Series to Pandas DataFrame

Suppose we have three different pandas Series:

import pandas as pd

#define three Series
name = pd.Series(['A', 'B', 'C', 'D', 'E'])
points = pd.Series([34, 20, 21, 57, 68])
assists = pd.Series([8, 12, 14, 9, 11])

We can use the following syntax to convert each Series into a DataFrame and concatenate the three DataFrames into one final DataFrame:

#convert each Series to a DataFrame
name_df = name.to_frame(name='name')
points_df = points.to_frame(name='points')
assists_df = assists.to_frame(name='assists')

#concatenate three Series into one DataFrame
df = pd.concat([name_df, points_df, assists_df], axis=1)

#view final DataFrame
print(df)

  name  points  assists
0    A      34        8
1    B      20       12
2    C      21       14
3    D      57        9
4    E      68       11

The final result is a pandas DataFrame where each Series represents a column.

Additional Resources

The following tutorials explain how to perform other common data object conversions in pandas:

How to Convert Pandas Series to NumPy Array
How to Convert Pandas DataFrame to NumPy Array
How to Convert Pandas DataFrame to Dictionary
How to Convert Pandas DataFrame to List

Subscribe

- Never miss a story with notifications

- Gain full access to our premium content

- Browse free from up to 5 devices at once

Latest stories