Here are a few title options that fit the question-and-answer format: * **How to Merge Pandas DataFrames When They Have Overlapping Columns?** * **Overlapping Columns in Pandas Merges: How to Handle

Combining DataFrames Using Join: Handling Overlapping Columns

In pandas, you can merge two dataframes by joining them on a common column. However, you encountered an error when attempting this operation due to overlapping columns.

The error occurs because both restaurant_ids_dataframe and restaurant_review_frame have a column named 'stars'. When performing a left join using restaurant_review_frame.join(), pandas will create two separate columns for these overlapping data: 'stars_x' and 'stars_y'.

To resolve this issue, you can use the merge function instead:

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

The merge function allows you to specify the method of merging (in this case, outer join using how='outer'), as well as the columns to join on (on='business_id').

Alternatively, you can modify the suffixes for the merged columns using the suffixes parameter:

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

This will create two columns named 'stars_restaurant_id' and 'stars_restaurant_review'.

By handling overlapping columns appropriately, you can successfully merge two pandas dataframes and create a combined dataframe that contains all relevant information.

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