Combining Pandas Data Frames: Join on a Common Column

Joinder is an essential operation for merging data frames based on common attributes. This question examines the issue of combining two pandas data frames: restaurant_ids_dataframe and restaurant_review_frame.

The user attempts to utilize the DataFrame.join() method to perform a left join using the column business_id. However, an error occurs due to overlapping columns (business_id, stars, and type). To resolve this issue, we can employ the merge function instead:

import pandas as pd

pd.merge(restaurant_ids_dataframe, restaurant_review_frame, on='business_id', how='outer')

The on parameter specifies the field name used for joining, while the how parameter defines the join type (outer, inner, left, or right). In this case, outer is selected for a union of keys from both data frames.

Note that both data frames contain a column named stars. By default, the merge operation appends suffixes to the column names (star_x and star_y). To customize these suffixes, we can use the suffixes keyword argument:

pd.merge(restaurant_ids_dataframe, restaurant_review_frame, on='business_id', how='outer', suffixes=('_restaurant_id', '_restaurant_review'))

With this modification, the star columns will be renamed to star_restaurant_id and star_restaurant_review. By leveraging the merge function and appropriately configuring the join type and column suffixes, we can successfully combine the two data frames based on their shared business_id column.