Merging DataFrames with Duplicate Rows

In this article, we will explore how to merge two data frames, tbl_1 and tbl_2, where tbl_2 has duplicate rows compared to tbl_1. Specifically, we will use the pandas library in Python to perform an inner merge between the two DataFrames.

Introduction

When working with data from various sources or datasets that have overlapping records, it is common to encounter duplicate rows. In such cases, you may need to append these duplicates to a main DataFrame while maintaining data integrity and accuracy.

Pandas provides several tools for handling duplicate rows in DataFrames, including the duplicated() function, which can be used to identify duplicate rows based on certain conditions. However, when it comes to merging two DataFrames with duplicate rows, you may need to use more advanced techniques involving merging and concatenation.

In this article, we will demonstrate how to add duplicate rows from one DataFrame to another based on two columns using pandas’ merge() function and data concatenation.

Background

Pandas Library Overview

Pandas is a powerful Python library designed for data manipulation and analysis. It provides various tools for working with structured data, including DataFrames and Series. The main features of pandas include:

DataFrames: Two-dimensional data structures that can store and manipulate large datasets.
Series: One-dimensional data structures that represent individual columns in a DataFrame.

Merging DataFrames

Merging DataFrames involves combining two or more DataFrames based on common columns, which are known as the join key. The main types of joins include:

Inner Join: Returns only rows where there is a match between the two DataFrames.
Left Join (or Left Merge): Returns all rows from the left DataFrame and matching rows from the right DataFrame.
Right Join (or Right Merge): Returns all rows from the right DataFrame and matching rows from the left DataFrame.
Full Outer Join: Returns all rows from both DataFrames, with null values where there is no match.

Duplicate Rows in DataFrames

When working with duplicate rows, you may need to decide how to handle them. Some common strategies include:

Drop duplicates: Remove duplicate rows from a DataFrame.
Keep the first occurrence: Keep only the first occurrence of each duplicate row.
Append duplicates: Append all occurrences of each duplicate row.

In this article, we will focus on appending duplicate rows using pandas’ merge() function and data concatenation.

Appending Duplicate Rows

To append duplicate rows from tbl_2 to tbl_1, you can use the following steps:

Step 1: Merge `tbl_1` with its own duplicate columns using an inner merge

# Import necessary libraries
import pandas as pd

# Define the DataFrames
tbl_1 = pd.DataFrame({
    'a': [1, 3, 5],
    'b': [2, 4, 6],
    'c': ['x', 'y', 'z']
})

tbl_2 = pd.DataFrame({
    'a': [1, 3, 5],
    'b': [1, 4, 6],
    'c': ['a', 'b', 'c']
})

# Merge tbl_1 with its own duplicate columns using an inner merge
merged_df = pd.concat([tbl_1, 
                       tbl_1[['a','b']].merge(tbl_2, how='inner')])

Step 2: Drop rows from `merged_df` that do not exist in `tbl_1`

# Drop rows from merged_df that do not exist in tbl_1
final_df = merged_df[merged_df['a'] == tbl_1['a']].copy()

Step 3: Append duplicates from `tbl_2` to `tbl_1`

# Append duplicates from tbl_2 to tbl_1
final_df = pd.concat([final_df, 
                      tbl_2[~tbl_2['a'].isin(final_df['a'])]])

Conclusion

In this article, we demonstrated how to add duplicate rows from one DataFrame to another based on two columns using pandas’ merge() function and data concatenation. By following the steps outlined above, you can efficiently merge DataFrames with duplicate rows while maintaining data integrity and accuracy.

Example Use Case: Data Analysis

Suppose you have a dataset containing information about employees in different departments within an organization. You want to analyze employee turnover rates based on departmental changes. In this case, you would need to append duplicate records from the second dataset to the first dataset, assuming that there is an inner relationship between the two datasets.

# Example usage: Data analysis
import pandas as pd

# Define the data
employees = pd.DataFrame({
    'Employee ID': [1, 2, 3],
    'Department': ['Sales', 'Marketing', 'IT']
})

turnover = pd.DataFrame({
    'Employee ID': [1, 3],
    'New Department': ['HR', 'Finance']
})

# Append duplicate records from turnover to employees
final_df = pd.concat([employees, 
                      turnover[~turnover['Employee ID'].isin(employees['Employee ID'])]])

By following this approach, you can analyze employee turnover rates based on departmental changes while accounting for duplicate records.

Next Steps

This article has demonstrated how to merge DataFrames with duplicate rows using pandas’ merge() function and data concatenation. However, there are many other ways to handle duplicates in DataFrames, including using the duplicated() function or the drop_duplicates() method.

Last modified on 2024-04-05