How to Perform a Three-Way Join of Pandas DataFrames Based on a Single Column?

Perform Three-Way Joins on Pandas Dataframes Based on Columns

When working with disparate datasets, merging them together to obtain a comprehensive view can be crucial. In Python's pandas library, the join() function offers a powerful way to combine multiple dataframes based on a common index.

Question:

You possess three CSV files, each containing person names as the first column and various attributes as the remaining columns. Your goal is to "join" these files into a single CSV, with each row representing a unique person and all their attributes.

Initially, the join() function implies the need for a multiindex. However, the confusion arises when attempting to join based on a single index.

Answer:

To achieve the desired three-way join, you can employ the functools.reduce function, which facilitates a sequential reduction operation on the dataframes. Here's how you can do it:

import functools as ft
dfs = [df0, df1, df2, ..., dfN]  # List of dataframes
df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name'), dfs)

This approach allows you to merge an arbitrary number of dataframes based on a common column, such as 'name' in your case. The reduce() function iteratively applies the pandas merge() function to the dataframes in the list, resulting in a single dataframe called df_final that contains all the merged attribute data.

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