Merging and Grouping DataFrames Based on Substring Matching

This article will delve into the process of merging two dataframes, df1 and df2, based on a specific column (Id) in df2 that is present as a substring in another column (A) in df1. We’ll use pandas, a popular Python library for data manipulation and analysis, to achieve this.

Introduction

In many real-world applications, data from different sources may need to be integrated or merged. In this scenario, we’re dealing with two dataframes: df1 and df2. While both contain important information, they don’t share a common column that can be used for merging. This is where the concept of substring matching comes into play.

Substring matching involves searching for a specified pattern within another string. In this case, we’re looking for Id values present as substrings in the A column of df1. If such an Id is found, it indicates that the row from df1 should be merged with the corresponding row from df2.

Prerequisites

For this tutorial, you’ll need:

Python 3.x
pandas library (pip install pandas)
numpy library (pip install numpy)

You can verify your pandas installation by running import pandas as pd; pd.__version__

Step 1: Extracting Ids from Column A

To start, we’ll create a new column in df1 that extracts the Id values present as substrings within the A column. We’ll use the str.extract method provided by pandas to achieve this.

import pandas as pd

# Creating sample DataFrames
df1 = pd.DataFrame({'A': ['2018 43 NFO BID_-324 dfs.DataNode X', '2018 5  NFO dfs.FSNames BID_123  XX', '2019 13 NFO BID_-398 dfs.DataNode  X X', '2019 45 NFO dfs.DataNode BID_-324', '2019 14 NFO BID_123 dfs.DataNode X'], 'label':[0,0,0,0,0]})
df2 = pd.DataFrame({'Id':['BID_-324', 'BID_123', 'BID_-398'], 'label':[1,1,2]})

# Extracting Ids from column A
df1['Id'] = df1['A'].str.extract('(BID[\_\-0-9]*)')

print(df1)

Step 2: Merging DataFrames

Next, we’ll use the pandas.merge function to merge df1 with df2. The merging is based on the common column (Id) between both dataframes. We’ll drop the Id column from df1, as it’s no longer needed after extraction.

# Merging DataFrames
df = pd.merge(df1, df2, on=['Id']).drop(columns=['Id', 'label_x']).rename(columns={'label_y': 'label', 'index': 'gid'})

print(df)

Step 3: Assigning Group Identifier

Finally, we’ll add a new column (gid) to each row in the merged dataframe. The value of this column will serve as a unique identifier for each group.

# Adding group identifier
df['gid'] = df['gid'] + 1

print(df)

Example Use Cases

The provided solution can be applied to various data integration scenarios, such as:

Combining customer information with order details.
Integrating sensor readings from different devices.
Merging product information with sales data.

In each of these cases, the process involves identifying common attributes between the two data sources and merging them accordingly.

Conclusion

This tutorial demonstrated how to merge two dataframes based on substring matching. By extracting Id values present as substrings in column A, we were able to match rows from both dataframes and create a new dataframe with merged information. This technique is useful in a variety of applications where integrating data from different sources is essential.