Understanding the Problem: Restoring Exploded Data after Merging

In this blog post, we’ll explore how to restore exploded data in pandas after a merge operation. The explode() function is often used to split a column into separate rows, but when merging two datasets with exploded columns, things can get complicated.

Background and Context

Before diving into the solution, let’s take a step back and understand what’s happening here. We have two datasets, df and df_2, which are merged on specific columns using an outer join. The df dataset has three burst columns: color, Name, and Name_2. These columns are exploded into separate rows when we apply the explode() function.

Now, let’s say we need to merge this exploded data with another dataset, df_3. However, df_3 doesn’t have the same structure as df. We want to restore the original form of the exploded columns in df.

The Challenge

The main challenge here is that the explode() function splits each row into separate rows. When we merge these rows with df_2, we need to decide how to handle the duplicates. If we simply concatenate the values, we’ll end up with a lot of duplicate data.

To restore the original form, we need to find a way to combine the exploded columns back together while preserving the original data. This is where the solution comes in.

Solution Overview

There are two approaches to solving this problem:

1. Using a custom function to explode and merge the data
2. Using an existing library or package (e.g., dask) that provides a built-in way to handle exploded data during merging

In this response, we’ll focus on using a custom function to solve the problem.

Approach 1: Custom Function to Explode and Merge Data

We can create a custom function that takes in the exploded columns and merges them with df_2. The function will need to decide how to handle duplicates and combine the values correctly.

Here’s an example implementation:

tmp = df.copy()
cols = ["color", "Name", "Name_2"]
tmp[cols] = tmp[cols].apply(lambda x: x.str.split("\s+"))

def xpl(df, col):
    return df.explode(col)

matches = (
    xpl(tmp, "color")
        .pipe(lambda x: xpl(x, "Name"))
        .pipe(lambda x: xpl(x, "Name_2"))
        .reset_index().merge(df_2)["index"]
)

# matches gives [0, 1, 2]
df_solution = df.loc[matches]

This implementation uses a custom function xpl() to explode each column and then merges the resulting data with df_2. The matches variable contains the indices of the rows that need to be restored.

Approach 2: Using an Existing Library or Package

If you’re working with large datasets, it might be more efficient to use a library like dask that provides built-in support for exploded data during merging. Dask allows you to perform parallel computations on large datasets and can handle exploded columns more efficiently.

Here’s an example implementation using dask:

import dask.dataframe as dd

tmp = df.copy()
cols = ["color", "Name", "Name_2"]
tmp[cols] = tmp[cols].apply(lambda x: x.str.split("\s+"))

df_exploded = (
    tmp.explode("color")
        .explode("Name")
        .explode("Name_2")
)

# merge with df_2
df_solution = dd.merge(df_exploded, df_2, on=["color", "Name", "Name_2"], how="outer").compute()

This implementation uses dask to create an exploded dataframe and then merges it with df_2. The resulting dataframe is computed using the compute() method.

Conclusion

Restoring exploded data after merging can be a challenging problem, but there are ways to solve it. By using a custom function or leveraging existing libraries like dask, you can recover the original form of your data and preserve its integrity.

In this blog post, we explored two approaches to solving this problem: using a custom function and utilizing an existing library (dask). We also discussed some best practices for working with exploded columns in pandas, such as choosing the right approach based on dataset size and complexity.

Last modified on 2023-08-12