Skip to main content
TopMiniSite

Back to all posts

How to Union 3 Dataframes By Pandas?

Published on
3 min read
How to Union 3 Dataframes By Pandas? image

Best Python Data Science Books to Buy in October 2025

1 Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

BUY & SAVE
$43.99 $79.99
Save 45%
Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
2 Data Science from Scratch: First Principles with Python

Data Science from Scratch: First Principles with Python

BUY & SAVE
$42.65 $65.99
Save 35%
Data Science from Scratch: First Principles with Python
3 Python Data Science Handbook: Essential Tools for Working with Data

Python Data Science Handbook: Essential Tools for Working with Data

BUY & SAVE
$44.18 $79.99
Save 45%
Python Data Science Handbook: Essential Tools for Working with Data
4 Python for Data Science: A Hands-On Introduction

Python for Data Science: A Hands-On Introduction

BUY & SAVE
$37.84 $59.99
Save 37%
Python for Data Science: A Hands-On Introduction
5 Python Data Science Handbook: Essential Tools for Working with Data

Python Data Science Handbook: Essential Tools for Working with Data

  • COMPREHENSIVE GUIDE COVERING ESSENTIAL PYTHON LIBRARIES FOR DATA SCIENCE.
  • HANDS-ON EXAMPLES THAT ENHANCE LEARNING AND PRACTICAL APPLICATION SKILLS.
  • EXPERT INSIGHTS AND TIPS TO STREAMLINE YOUR DATA ANALYSIS WORKFLOW.
BUY & SAVE
$52.62 $69.99
Save 25%
Python Data Science Handbook: Essential Tools for Working with Data
6 Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

BUY & SAVE
$37.99 $65.99
Save 42%
Football Analytics with Python & R: Learning Data Science Through the Lens of Sports
7 Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

BUY & SAVE
$45.00
Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
8 Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud

Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud

BUY & SAVE
$89.95 $126.65
Save 29%
Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud
9 Introduction to Machine Learning with Python: A Guide for Data Scientists

Introduction to Machine Learning with Python: A Guide for Data Scientists

BUY & SAVE
$45.00 $59.99
Save 25%
Introduction to Machine Learning with Python: A Guide for Data Scientists
10 Python for Data Science For Dummies (For Dummies (Computer/tech))

Python for Data Science For Dummies (For Dummies (Computer/tech))

BUY & SAVE
$22.86 $34.99
Save 35%
Python for Data Science For Dummies (For Dummies (Computer/tech))
+
ONE MORE?

To union 3 dataframes by pandas, you can use the concat() function. This function allows you to concatenate multiple dataframes along a specified axis (rows or columns). You can pass a list of dataframes as an argument to the function, and pandas will concatenate them together. The syntax for the concat() function is pd.concat([df1, df2, df3]), where df1, df2, and df3 are the dataframes you want to union. Make sure that the dataframes have the same column names and data types before concatenating them to avoid any issues.

How to concatenate dataframes in pandas?

You can concatenate dataframes in pandas using the concat() function. Here is an example of how to concatenate two dataframes:

import pandas as pd

Create two dataframes

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

Concatenate the two dataframes

result = pd.concat([df1, df2])

print(result)

This will concatenate the two dataframes vertically, meaning the rows from the second dataframe will be appended to the end of the first dataframe. You can also concatenate dataframes horizontally by specifying the axis parameter:

result = pd.concat([df1, df2], axis=1)

This will concatenate the two dataframes side by side, meaning the columns from the second dataframe will be appended to the right of the columns in the first dataframe.

How to combine three dataframes using pandas?

To combine three dataframes using pandas, you can use the concat function. Here is an example:

import pandas as pd

Create three sample dataframes

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]}) df3 = pd.DataFrame({'A': [13, 14, 15], 'B': [16, 17, 18]})

Combine the three dataframes

combined_df = pd.concat([df1, df2, df3])

print(combined_df)

This will concatenate the three dataframes row-wise, combining them into a single dataframe.

How to merge dataframes with duplicate column names in pandas?

To merge dataframes with duplicate column names in pandas, you can use the suffixes parameter in the merge function to specify a suffix for duplicate column names.

Here's an example:

import pandas as pd

Create two dataframes with duplicate column names

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

Merge the two dataframes with a suffix for duplicate column names

merged_df = pd.merge(df1, df2, on='A', suffixes=('_df1', '_df2'))

print(merged_df)

In this example, the suffixes=('_df1', '_df2') parameter specifies that the duplicate column names should be suffixed with "_df1" and "_df2" in the resulting merged dataframe. This will prevent any naming conflicts and allow you to merge dataframes with duplicate column names.