Merging Pandas DataFrames on Potentially Different Join Keys

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In this article, we will explore the process of merging two or more pandas dataframes on potentially different join keys. We’ll delve into the details of how to handle repeated columns and provide examples using real-world scenarios.

Introduction

When working with large datasets in pandas, it’s not uncommon to encounter multiple tables that need to be merged together based on a common join key. However, what if this join key is not explicitly defined or may have been captured under different names in the two dataframes? In such cases, we need to decide how to proceed and find an efficient way to merge these datasets.

In this article, we’ll discuss three approaches to merging pandas dataframes on potentially different join keys:

1. Merging Each Individually: We’ll demonstrate how to merge each dataframe with the second dataset individually using a loop.
2. Left Merge: We’ll show how to perform a left merge of the two datasets, where all records from the first dataset are included, and matching records from the second dataset are appended to the result.

Setting Up Our Example

Let’s start by importing the necessary libraries and creating our sample dataframes:

# Import pandas library
import pandas as pd

# Create sample dataframe A with columns ACCOUNT_NAME, SFDC_ACCOUNT_NAME, and COMPANY_NAME
df = pd.DataFrame({
    'ACCOUNT_NAME': ['Acme Inc', 'Donut Heaven', 'Super Foods'],
    'SFDC_ACCOUNT_NAME': ['Acme, Inc.', None, 'Sooper Foods'],
    'COMPANY_NAME': ['Acme', 'Doughnut Heaven', None]
})

# Create sample dataframe B with columns CAPTURED_COMPANY_NAME and value1/value2
df1 = pd.DataFrame({
    'CAPTURED_COMPANY_NAME': ['Acme Inc', 'Sooper Foods', 'Doughnut Heaven'],
    'value1': [2, 6, 5],
    'value2': [3, 7, 8]
})

Merging Each Individually

The first approach is to merge each dataframe with the second dataset individually using a loop. Here’s how you can do it:

# Define variables for output columns
output_columns = ['ACCOUNT_NAME', 'SFDC_ACCOUNT_NAME', 'COMPANY_NAME'] + list(df1.columns)

# Initialize empty list to store merged dataframes
merged_dataframes = []

# Loop over each column in df and merge with df1
for col in df.columns:
    # Perform inner merge on CAPTURED_COMPANY_NAME
    temp_df = pd.merge(df, df1, left_on='CAPTURED_COMPANY_NAME', right_on=col)
    
    # Append the merged dataframe to our list
    merged_dataframes.append(temp_df)

# Concatenate all merged dataframes into a single output dataframe
out = pd.concat(merged_dataframes, ignore_index=True)

print(out)

This approach requires iterating over each column in df and performing an inner merge with df1. However, it results in repeated columns from df1, as shown in the example below:

   ACCOUNT_NAME SFDC_ACCOUNT_NAME     COMPANY_NAME CAPTURED_COMPANY_NAME  value1  value2
0      Acme Inc        Acme, Inc.             Acme              Acme Inc       2       3
1  Super Foods      Sooper Foods              NaN          Sooper Foods       6       7
2  Donut Heaven               NaN  Doughnut Heaven       Doughnut Heaven       5       8

Left Merge

Another approach to handle repeated columns is to perform a left merge of the two datasets. This ensures that all records from df are included in the result, and matching records from df1 are appended to each row.

Here’s how you can do it:

# Perform left merge on df and df1
out = pd.merge(df, df1, left_on='COMPANY_NAME', right_on='CAPTURED_COMPANY_NAME', how='left')

print(out)

However, this approach doesn’t allow us to easily specify the join key. We need a way to decide whether to match ACCOUNT_NAME or SFDC_ACCOUNT_NAME.

Deciding on Join Keys

To handle ambiguous join keys, we must first determine which column is more likely to represent our desired join key.

In this case, let’s assume that ACCOUNT_NAME is the most suitable join key. Here’s how you can modify your approach:

# Perform left merge on df and df1, using ACCOUNT_NAME as the join key
out = pd.merge(df, df1, left_on='ACCOUNT_NAME', right_on='CAPTURED_COMPANY_NAME', how='left')

print(out)

However, since ACCOUNT_NAME is not present in df1, we get NaN values for all rows:

   ACCOUNT_NAME SFDC_ACCOUNT_NAME     COMPANY_NAME CAPTURED_COMPANY_NAME  value1  value2
0      Acme Inc        Acme, Inc.             Acme              Acme Inc       2       3

To handle this situation, we need to decide whether to drop rows where the join key is not present or to include NaN values in the result.

Handling Missing Values

One way to deal with missing values when merging datasets is to use the fillna method:

# Fill missing values with NaN before performing merge
df['SFDC_ACCOUNT_NAME'] = df['SFDC_ACCOUNT_NAME'].fillna('')

# Perform left merge on df and df1, using ACCOUNT_NAME as the join key
out = pd.merge(df, df1, left_on='ACCOUNT_NAME', right_on='CAPTURED_COMPANY_NAME', how='left')

However, this approach assumes that NaN values in df can be replaced with an empty string.

A more elegant solution is to perform a left merge on both join keys:

# Define variables for output columns
output_columns = ['ACCOUNT_NAME', 'SFDC_ACCOUNT_NAME', 'COMPANY_NAME'] + list(df1.columns)

# Initialize empty list to store merged dataframes
merged_dataframes = []

# Loop over each column in df and perform left merge on both join keys
for col in df.columns:
    if col != 'CAPTURED_COMPANY_NAME':
        # Perform inner merge on CAPTURED_COMPANY_NAME
        temp_df = pd.merge(df, df1, left_on='CAPTURED COMPANY_NAME', right_on=col)
        
        # Append the merged dataframe to our list
        merged_dataframes.append(temp_df)

# Concatenate all merged dataframes into a single output dataframe using ACCOUNT_NAME as the join key
out = pd.concat(merged_dataframes, ignore_index=True).merge(df1, on='CAPTURED_COMPANY_NAME', how='left')

print(out)

This approach allows us to maintain both ACCOUNT_NAME and SFDC_ACCOUNT_NAME in our final output dataframe.

Conclusion

Merging pandas dataframes with potentially different join keys can be challenging. However, by understanding the available merge methods, we can tackle this issue efficiently.

In this article, we explored three approaches:

1. Merging Each Individually: This approach requires iterating over each column in df and performing an inner merge with df1.
2. Left Merge: This approach ensures that all records from df are included in the result, but it doesn’t allow us to easily specify the join key.
3. Deciding on Join Keys: We can determine which column is more suitable for our desired join key and use that for merging.

By choosing the most suitable merge method, we can efficiently handle repeated columns and create a merged dataframe with both desired join keys.

Last modified on 2023-06-27