How Can I Efficiently Merge Multiple Pandas DataFrames with Overlapping Columns?

Combining Multiple Dataframes Using Three-Way Joins in Pandas

Given multiple CSV files with overlapping person names as the first column, the task is to merge these files into a single CSV with each row containing all attributes for a unique person.

The traditional join() function in Pandas requires hierarchical indexing. However, an alternative approach is available to simplify the joining process.

Reduce Function for DataFrame Merging

One efficient way to merge dataframes is to use the functools.reduce function along with the pd.merge function. Here's how the code would look like:

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

This approach allows the merging of an arbitrary number of dataframes with a common 'name' column.

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