Join 2 Dataframes with Same Column but Different Values (Python)

Are you tired of dealing with multiple dataframes that have the same column names but different values? Do you want to combine them into a single dataframe that’s easy to work with? Look no further! In this article, we’ll show you how to join 2 dataframes with the same column but different values in Python.

What is a DataFrame?

A DataFrame is a two-dimensional table of data with columns of potentially different types. It’s similar to an Excel spreadsheet or a table in a relational database. DataFrames are widely used in data science and machine learning to store and manipulate data.

Why Join DataFrames?

There are several reasons why you might want to join two dataframes with the same column but different values:

• Consolidate data from different sources: You might have data from different sources, such as CSV files, databases, or APIs, that you want to combine into a single dataframe.

• Perform data analysis: Joining dataframes allows you to perform data analysis and visualization on a larger dataset.

• Improve data quality: By combining dataframes, you can remove duplicates, fill in missing values, and improve the overall quality of your data.

Types of Joins

There are several types of joins in Python, including:

1. Inner Join: Returns only the rows that have a match in both dataframes.

2. Left Join: Returns all the rows from the left dataframe and the matching rows from the right dataframe.

3. Right Join: Returns all the rows from the right dataframe and the matching rows from the left dataframe.

4. Outer Join: Returns all the rows from both dataframes, with null values where there are no matches.

Join 2 DataFrames with Same Column but Different Values

Now that we’ve covered the basics, let’s dive into the nitty-gritty of joining 2 dataframes with the same column but different values.

Example DataFrames

Let’s create two example dataframes:

import pandas as pd

df1 = pd.DataFrame({
    'Name': ['John', 'Jane', 'Bob'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Chicago', 'Los Angeles']
})

df2 = pd.DataFrame({
    'Name': ['John', 'Jane', 'Alice'],
    'Age': [25, 29, 40],
    'City': ['New York', 'Chicago', 'San Francisco']
})

The two dataframes have the same column names (Name, Age, and City) but different values.

Inner Join

To perform an inner join on the two dataframes, we can use the merge function:

merged_df = pd.merge(df1, df2, on='Name')
print(merged_df)

The resulting dataframe will contain only the rows where the Name column matches in both dataframes:

Name	Age_x	City_x	Age_y	City_y
John	25	New York	25	New York
Jane	30	Chicago	29	Chicago

Left Join

To perform a left join on the two dataframes, we can use the merge function with the how parameter set to ‘left’:

merged_df = pd.merge(df1, df2, on='Name', how='left')
print(merged_df)

The resulting dataframe will contain all the rows from the left dataframe (df1) and the matching rows from the right dataframe (df2):

Name	Age_x	City_x	Age_y	City_y
John	25	New York	25.0	New York
Jane	30	Chicago	29.0	Chicago
Bob	35	Los Angeles	NaN	NaN

Right Join

To perform a right join on the two dataframes, we can use the merge function with the how parameter set to ‘right’:

merged_df = pd.merge(df1, df2, on='Name', how='right')
print(merged_df)

The resulting dataframe will contain all the rows from the right dataframe (df2) and the matching rows from the left dataframe (df1):

Name	Age_x	City_x	Age_y	City_y
John	25.0	New York	25	New York
Jane	30.0	Chicago	29	Chicago
Alice	NaN	NaN	40	San Francisco

Outer Join

To perform an outer join on the two dataframes, we can use the merge function with the how parameter set to ‘outer’:

merged_df = pd.merge(df1, df2, on='Name', how='outer')
print(merged_df)

The resulting dataframe will contain all the rows from both dataframes, with null values where there are no matches:

Name	Age_x	City_x	Age_y	City_y
John	25.0	New York	25.0	New York
Jane	30.0	Chicago	29.0	Chicago
Bob	35.0	Los Angeles	NaN	NaN
Alice	NaN	NaN	40.0	San Francisco

Conclusion

In this article, we’ve shown you how to join 2 dataframes with the same column but different values in Python using the merge function. We’ve covered the different types of joins, including inner, left, right, and outer joins, and provided examples of each. By following these instructions, you can combine your dataframes and perform data analysis and visualization on a larger dataset.

Remember to choose the right type of join based on your data and analysis goals. Happy joining!

Frequently Asked Questions

Get ready to merge like a pro! Here are the top questions and answers about joining two dataframes with the same column but different values in Python.