Pandas – Merge DataFrames on Multiple Columns

In this tutorial, we’ll look at how to merge pandas dataframes on multiple columns.

How to join pandas dataframes on multiple columns?

The pandas merge() function is used to do database-style joins on dataframes. To merge dataframes on multiple columns, pass the columns to merge on as a list to the on parameter of the merge() function. The following is the syntax:

df_merged = pd.merge(df_left, df_right, on=['Col1', 'Col2', ...], how='inner')

Note that, the list of columns passed must be present in both the dataframes. If the column names are different in the two dataframes, use the left_on and right_on parameters to pass your column lists to merge on.

Examples

Let’s look at an example of using the merge() function to join dataframes on multiple columns. First, let’s create two dataframes that we’ll be joining together.

import pandas as pd

# monthly users
df_users = pd.DataFrame({
    'Year': [2019,2019,2019,2019,2019,2019,2019,2019,2019,2019,2019,2019,2020,2020,2020],
    'Quarter': ['Q1','Q1','Q1','Q2','Q2','Q2','Q3','Q3','Q3','Q4','Q4','Q4','Q1','Q1','Q1'],
    'Month': ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec','Jan','Feb','Mar'],
    'Users': [150,170,160,200,190,196,210,225,260,210,212,219,630,598,321]
})

# advertising partners
df_ad_partners = pd.DataFrame({
    'Year': [2019,2019,2019,2019,2020],
    'Quarter': ['Q1','Q2','Q3','Q4','Q1']
})

The dataframe df_users shows the monthly user count of an online store whereas the table df_ad_partners shows which ad partner was handling the store’s advertising.

Merge dataframes on two columns

If we want to include the advertising partner info alongside the users dataframe, we’ll have to merge the dataframes using a left join on columns “Year” and “Quarter” since the advertising partner information is unique at the “Year” and “Quarter” level.

df_merged = pd.merge(df_users, df_ad_partners, on=['Year', 'Quarter'], how='left')

This is the dataframe we get on merging –

Year	Quarter	Month	Users	Ad Partner
2019	Q1	Jan	150	A
2019	Q1	Feb	170	A
2019	Q1	Mar	160	A
2019	Q2	Apr	200	A
2019	Q2	May	190	A
2019	Q2	Jun	196	A
2019	Q3	Jul	210	A
2019	Q3	Aug	225	A
2019	Q3	Sep	260	A
2019	Q4	Oct	210	A
2019	Q4	Nov	212	A
2019	Q4	Dec	219	A
2020	Q1	Jan	630	B
2020	Q1	Feb	598	B
2020	Q1	Mar	321	B

You can see the Ad Partner info alongside the users count. An interesting observation post the merge is that there has been an increase in users since the switch from A to B as the advertising partner.

In the above example, we saw how to merge two pandas dataframes on multiple columns. The columns to merge on had the same names across both the dataframes. It can happen that sometimes the merge columns across dataframes do not share the same names. In that case, you can use the left_on and right_on parameters to pass the list of columns to merge on from the left and right dataframe respectively.

For a complete list of pandas merge() function parameters, refer to its documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

• Pandas – Join vs Merge
• Pandas – Merge DataFrames on Multiple Columns
• Understanding Joins in Pandas
• Append Rows to a Pandas DataFrame
• Concat DataFrames in Pandas