How to Merge Multiple DataFrames Based on a Common Column and Preserve Shared Rows?

Merging Multiple Dataframes Based on a Common Column

You have multiple dataframes with a common column, 'date', and you need to merge them while preserving rows where the date is common to all dataframes. A recursion function approach might be complex and prone to errors. Here's a simpler solution using pandas' powerful groupby and merge functions:

import pandas as pd

# Create a list of dataframes
dfs = [df1, df2, df3]

# Group all dataframes by the 'date' column and ensure that only the rows
# where the date exists in all dataframes are kept
merged_data = dfs[0].merge(dfs[1:], on='date', how='inner')

print(merged_data)

This solution provides a more effective way to merge multiple dataframes with a common column, maintaining only the rows where the date is common. It's concise, scalable, and easy to implement.

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