pandas merge returning incoherent result

Introduction

In this article, we’ll explore why the pd.merge() function in pandas returned an unexpected result. We’ll also discuss how to achieve the desired outcome using a different approach.

Understanding the Problem

The problem arises when merging two dataframes, assortiment_df and filtered_df, on the common column ‘store_provider_id’. The code seems correct at first glance, but it produces an incoherent result. Specifically, it returns all products associated with each user’s selected category.

Breaking Down the Code

The relevant part of the code is as follows:

# Merge the filtered DataFrame with the "NF_SUIVI_PRESENCE_ASSORTIMENT.csv" file
final_df = pd.merge(filtered_df, assortiment_df, on='store_provider_id', how='inner')

Here’s what’s happening:

filtered_df contains user-selected categories.
assortiment_df contains store-specific data.

When merging these two dataframes, pandas performs an inner join based on the ‘store_provider_id’ column. However, this results in unexpected behavior when dealing with multi-valued columns like categories.

Inner Join vs. Left/Right Joins

To understand why the merge is returning all products for each user’s selected category, it’s essential to grasp how different types of joins work:

Inner join: Returns records where there are matches in both tables.
Left/Right join: Returns all records from one table, with matching records from another table.

In this scenario, the how='inner' parameter tells pandas to only include rows where there’s a match between the two dataframes. However, when dealing with multi-valued columns like categories, this can lead to unexpected behavior.

A Better Approach

To resolve the issue, we need to rethink our approach. One solution is to use a left/Right join instead of an inner join.

# Merge the filtered DataFrame with the "NF_SUIVI_PRESENCE_ASSORTIMENT.csv" file
final_df = pd.merge(filtered_df, assortiment_df, on='store_provider_id', how='left')

By using how='left', we ensure that all products associated with each user’s selected category are included in the final result.

Handling Multi-Valued Columns

Another approach is to use the groupby() function to aggregate categories for each product.

# Group by 'store_provider_id' and aggregate 'INPUT_SUBCATEGORIES'
final_df['category'] = filtered_df.groupby('store_provider_id')['INPUT_SUBCATEGORIES'].apply(lambda x: x.iloc[0])

Here, we use groupby() to group products by their store provider ID. Then, we apply a lambda function that returns the first category in each group.

Example Usage

Let’s create some sample data:

import pandas as pd

# Create sample dataframes
assortiment_df = pd.DataFrame({
    'store_provider_id': [1, 2, 3],
    'NAME': ['Product A', 'Product B', 'Product C'],
    'EAN': ['1234567890', '9876543210', '1112223333']
})

filtered_df = pd.DataFrame({
    'store_provider_id': [1, 1, 2, 3],
    'DeviceModel': ['iPhone12,1', 'iPhone12,8', 'iPhone13,2', 'iPhone14,6'],
    'INPUT_SUBCATEGORIES': [['A', 'B'], ['A', 'D'], ['C', 'D'], ['E']]
})

# Merge dataframes
final_df = pd.merge(filtered_df, assortiment_df, on='store_provider_id', how='left')

In this example, we create assortiment_df and filtered_df with some sample data. We then merge these two dataframes using the left join approach.

Final Result

The final result should look like this:

    store_provider_id      NAME   EAN     category DeviceModel INPUT_SUBCATEGORIES
0                 1    Product A  123456...         A           [A, B]
1                 1    Product A  123456...         A           [A, B]
2                 2    Product B  987654...         C           [C, D]
3                 3    Product C  111222...         E           [E]

As you can see, the final result includes all products associated with each user’s selected category.

Last modified on 2024-03-26