pandas pre-filter an exploded list

Introduction

In this article, we’ll explore a common problem when working with pandas DataFrames and lists. Suppose you have a DataFrame with a list column that needs to be exploded and filtered based on another list. You’re not alone in facing this challenge. In fact, it’s a common issue many data analysts and scientists encounter when dealing with large datasets.

The Problem

Let’s consider an example to illustrate the problem. We have a DataFrame df_have with two columns: ‘user’ and ‘group_ids’. The ‘group_ids’ column is a list of integers that represent different groups within each user.

df_have = pd.DataFrame({'user': ['emp_1', 'emp_2', 'emp_3', 'admin'],
                        'group_ids': [[5, 3], [4, 2, 3], [1, 4], [1, 2, 3, 4, 5]]})

In this example, the ‘group_ids’ column is a list of integers that represent different groups within each user. The goal is to explode this list and filter based on another list of selected IDs.

Current Solution

One way to solve this problem is to use the explode function in pandas, which splits a list into separate rows. However, this approach can be slow when dealing with large lists.

df_have = df_have.explode('group_ids')
selected_ids = [2, 3]
df_want = df_have[df_have['group_ids'].isin(selected_ids)]

This code explodes the ‘group_ids’ list and then filters based on the selected IDs. However, this approach can be slow when dealing with large lists.

Optimized Solution

A more efficient way to solve this problem is by using a list comprehension to pre-filter the values within the groups that have the selected IDs.

selected_ids = [2, 3]
S = set(selected_ids)

out = (df.assign(group_ids=[[x for x in l if x in S] for l in df['group_ids']])
          .explode('group_ids')
          .dropna(subset=['group_ids'])
      )

This code creates a set S of the selected IDs and then uses a list comprehension to filter the values within each group. The resulting DataFrame is then exploded and filtered based on the selected IDs.

How it Works

The optimized solution works by using the following steps:

Pre-filtering: Create a set S of the selected IDs.
Assigning new values: Use a list comprehension to filter the values within each group. The list comprehension iterates over each value in the ‘group_ids’ column and checks if it is in the set S. If it is, the value is included in the new ‘group_ids’ column.
Exploding: Use the explode function to split the list into separate rows.
Dropping NaN values: Drop any rows that have NaN values in the ‘group_ids’ column.

Benefits

The optimized solution has several benefits:

Faster performance: By pre-filtering the values within each group, we can avoid the slow filtering step when using the explode function.
Improved readability: The list comprehension makes it easier to understand what’s happening in the code.
Reduced memory usage: By dropping any rows that have NaN values, we can reduce memory usage and improve performance.

Conclusion

In this article, we explored a common problem when working with pandas DataFrames and lists. We introduced an optimized solution using list comprehension to pre-filter the values within each group before exploding and filtering based on selected IDs. This approach provides faster performance, improved readability, and reduced memory usage compared to the traditional method.

Example Use Cases

Here are some example use cases where this optimized solution can be applied:

Data analysis: When working with large datasets, pre-filtering the values within each group can significantly improve performance.
Machine learning: In machine learning applications, pre-filtering data can help reduce noise and improve model accuracy.
Data visualization: When creating visualizations, pre-filtering data can help ensure that only relevant information is displayed.