How to Combine Pandas DataFrames Based on a Shared Column: A Guide to `join()` and `merge()`

Combining Pandas Data Frames on a Shared Column: A Comprehensive Guide

Introduction

Combining data from multiple data frames is a common task in data analysis. Pandas offers several methods to achieve this, including the join() and merge() functions. This article demonstrates how to use these functions to combine two data frames that share a common column.

Using the join() Function

The join() function performs an inner join by default, meaning it only retains rows that have matching values in the join column. In the provided example, the join() function cannot be used because the restaurant_ids_dataframe and restaurant_review_frame have overlapping column names (stars and type), as indicated by the error message:

Exception: columns overlap: Index([business_id, stars, type], dtype=object)

Using the merge() Function

The merge() function offers greater flexibility for combining data frames. To perform an outer join, which retains all rows from both data frames, use the how='outer' parameter:

<code class="python">import pandas as pd
pd.merge(restaurant_ids_dataframe, restaurant_review_frame, on='business_id', how='outer')</code>

By default, merge() uses the suffixes ('_x', '_y') to distinguish between columns with duplicate names. To customize the suffixes, pass a value to the suffixes parameter, as shown below:

<code class="python">pd.merge(restaurant_ids_dataframe, restaurant_review_frame, on='business_id', how='outer', suffixes=('_restaurant_id', '_restaurant_review'))</code>

Conclusion

Both the join() and merge() functions can be used to combine data frames on a common column. Understanding the differences between these functions is crucial for achieving the desired join behavior. The merge() function offers more flexibility, including the ability to perform outer joins and customize column suffixes. By mastering these techniques, you can effectively combine data frames to extract meaningful insights from your datasets.

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