Overlaying Two Pandas DataFrames When One is Partial

When working with two pandas DataFrames, it’s often necessary to overlay one DataFrame onto the other. In this case, we’re dealing with a situation where only certain columns match between the two DataFrames, and we want to merge them based on those matching columns.

Problem Statement

The problem statement provides us with two example DataFrames: background_df and data_df. The task is to overlay data_df onto background_df, overwriting any rows in background_df that have matching values for certain columns (Name1, Name2, Id1, and Id2). We’re looking for a way to achieve this without relying on indices, as the use of indices can lead to issues when dealing with missing data or duplicate values.

Solution Overview

The solution involves creating unique IDs for each row in both DataFrames based on the matching columns. These IDs are then used to merge the two DataFrames together. To ensure that only rows with matching values are merged, we’ll use a combination of string manipulation and pandas’ built-in merging functionality.

Creating Unique IDs

First, let’s create unique IDs for each row in both background_df and data_df. We can do this by splitting the values in the matching columns into individual elements and then joining them back together with underscores (_) as separators.

check_cols = 'Name1 Name2 Id1 Id2'.split()
val_cols = 'Attr1 Attr2'.split()

background_df['full_id'] = background_df[check_cols].apply(lambda row: '_'.join([str(_).strip() for _ in row]), axis=1)
data_df['full_id'] = data_df[check_cols].apply(lambda row: '_'.join([str(_).strip() for _ in row]), axis=1)

Merging DataFrames

Now that we have unique IDs for each row, we can merge the two DataFrames together using these IDs. We’ll use the merge function with a left join and specify suffixes to handle any potential conflicts between columns.

background_df[check_cols+['full_id',]].merge(data_df[val_cols+['full_id',]], how='left', on='full_id', suffixes=('_x', '_y'))

Handling Extra Columns

In the provided solution, the author mentions that there may be extra columns in background_df (specifically, Attr1 and Attr2) that we’d like to keep instead of what might be present in data_df. To handle this, we can modify the merge statement to include these extra columns as suffixes.

background_df[check_cols+['full_id',]].merge(data_df[val_cols+['full_id',]], how='left', on='full_id', suffixes=('_x', '_y'))

This approach allows us to preserve the original values in background_df while still merging with data_df.

Conclusion

In this solution, we’ve demonstrated a way to overlay two pandas DataFrames when only certain columns match between them. By creating unique IDs for each row based on these matching columns and using pandas’ built-in merging functionality, we can achieve this goal without relying on indices.

The key takeaways from this example are:

• Use string manipulation to create unique IDs for each row in both DataFrames.
• Utilize pandas’ merge function with a left join and specify suffixes to handle potential conflicts between columns.
• Be mindful of extra columns that may be present in one or both DataFrames, and modify the merge statement accordingly.

Last modified on 2023-10-23