Pandas Merge with Right-On Conditional ‘OR’

Overview of Pandas Merging

Pandas is a powerful Python library for data manipulation and analysis. Its merging functionality allows us to combine data from two or more DataFrames based on common columns. This tutorial will explore how to use the merge method to merge DataFrames, focusing on the right-on conditional ‘OR’ approach.

Introduction to the Problem

The problem presented involves merging a left DataFrame with a right DataFrame based on multiple possible matching conditions. The task is to write a single column from the right DataFrame into the merged result when a match does not exist in the specified columns of the left DataFrame. In other words, we want to implement a conditional ‘OR’ statement using pandas.

Background Information

Before diving into the solution, it’s essential to understand some fundamental concepts:

• DataFrames: DataFrames are two-dimensional data structures similar to Excel spreadsheets or SQL tables.
• Merge: The merge method combines rows from two DataFrames based on a common column. There are four types of merges: inner join (default), left join, right join, and outer join.
• Right-On Conditional ‘OR’: This technique involves combining multiple columns from the right DataFrame into a single new column, applying an OR condition to match rows between the DataFrames.

Solution Overview

The solution presented below outlines the steps needed to merge two DataFrames based on a conditional ‘OR’ statement. We will use pandas version 0.25 or later for the explode method, which is essential for this approach.

Step-by-Step Explanation

Combine Values from Multiple Columns into a Single Row

# Select multiple columns and convert them to lists
s = df2.loc[:, 'ID1':'ID4'].apply(list, axis=1)

This step uses the loc method to select all four columns (‘ID1’, ‘ID2’, ‘ID3’, and ‘ID4’) from the right DataFrame (df2). The apply(list, axis=1) function is applied to each row of the selected DataFrames. This produces a new Series where each value is a list containing one element from each of the original columns.

Stack the Values into a New Column

# Explode the values and rename 'RightID'
right = df2.join(s.explode().rename('RightID'))

The explode function converts the lists in the new Series into separate rows, allowing us to stack them into a single column. The rename('RightID') method renames this new column to ‘RightID’.

Reset Index and Merge with Left DataFrame

# Reset index for later matching
# merge with the left frame
# drop duplicates based on 'index'
df1.reset_index() \
    .merge(right, left_on='ID', right_on='RightID') \
    .drop_duplicates('index')

After creating the stacked column, we need to reset the index of both DataFrames so that they can be matched correctly. We merge them using their common ‘ID’ columns and drop any duplicate rows based on their original index values.

Example Use Case

Suppose we have two DataFrames:

df1 = 
  ID  Value
0  A      0
1  B      1
2  C      2
3  D      3
4  E      4
5  F      5

df2 = 
  ID1 ID2 ID3 ID4  AnotherValue
0   L   G   N   Y             1
1   H   U   B   F             4
2   O   Z   Q   V             1
3   H   A   T   P             6
4   V   K   A   G             3
5   E   C   N   U             1

By following the steps outlined in this tutorial, we can merge df1 with df2, combining values from multiple columns of df2 into a single column based on an OR condition. The result will be:

   index ID  Value ID1 ID2 ID3 ID4  AnotherValue RightID
0      0  A      0   H   A   T   P             6       A
2      1  B      1   H   U   B   F             4       B
3      2  C      2   E   C   N   U             1       C
4      4  E      4   E   C   N   U             1       E
5      5  F      5   H   U   B   F             4       F

This result shows that rows from df2 with matching values in any of the four specified columns (‘ID1’, ‘ID2’, ‘ID3’, and ‘ID4’) are merged into df1, based on an OR condition.

Last modified on 2024-05-23