How to Efficiently Merge Multiple Pandas DataFrames Based on a Common Column?

Merging Multiple DataFrames on Columns in Pandas with Three-Way Joins

Data merging, a fundamental task in data analysis, allows you to combine data from multiple sources. In Pandas, the join() function is a powerful tool for merging dataframes. However, when joining multiple dataframes, you may encounter challenges related to hierarchical indexing schemes.

Three-Way Joins Using a Common Column

Consider the scenario where you have three CSV files, each containing information about the same set of people. The first column in each file is the name of the person, while the subsequent columns represent their attributes. Your goal is to combine these files into a single CSV, with each row containing all attributes for each unique person.

Hierarchical Indexing and Multi-Index

In Pandas, multi-index refers to an indexing scheme where each index level represents a different column. When joining dataframes, a multi-index is used to align the data based on shared values. In your case, the "join" function may specify that you need a multi-index because you are joining on a single column (name), which is the index in each dataframe.

Merging Dataframes without Hierarchical Indexing

However, some scenarios may not require hierarchical indexing. If the dataframes have a common column, you can use the lambda function and functools package to simplify the merging process. Here's an example:

import pandas as pd
import functools as ft

dfs = [df1, df2, df3, ..., dfN]

df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name'), dfs)

In this code:

• dfs is a list containing the dataframes to be merged.
• ft.reduce applies the lambda function to each pair of dataframes, merging them based on the "name" column.
• df_final is the resulting dataframe, containing all attributes for each unique person.

This approach is convenient for merging multiple dataframes without having to specify complex hierarchical indexing schemes.

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