Update Data Frame Column Values Based on Conditional Match With Another DataFrame

Introduction to Data Frame Column Value Updates in Pandas

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When working with data frames, it’s not uncommon to encounter scenarios where you need to update values based on a conditional match between two data frames. In this article, we’ll explore how to achieve this using pandas and provide an efficient technique for updating column values from one data frame to another.

Prerequisites

Before diving into the solution, make sure you have the following prerequisites:

Familiarity with pandas and its data structures (data frames)
Knowledge of basic Python programming concepts

If you’re new to pandas, we recommend checking out their official documentation or a beginner’s guide for an introduction.

The Problem

Let’s assume we have two data frames: df_A and df_B. We want to update the values in df_A’s ’last_name’ and ‘first_name’ columns based on a match with the corresponding values in df_B.

Here are examples of what these data frames might look like:

df_A

{'last_name': {0: 'Williams', 1: 'Henry', 2: 'XYX', 3: 'Smith', 4: 'David', 5: 'Freeman', 
               6: 'Walter', 7: 'Test_A', 8: 'Mallesham', 9: 'Mallesham', 10: 'Henry', 
               11: 'Smith'}, 'first_name': {0: 'Henry', 1: 'Williams', 2: 'ABC', 3: 'David', 
                                            4: 'Freeman', 5: 'Walter', 6: 'Test_B', 7: 'Mallesham', 
                                            8: 'Mallesham', 9: 'Henry', 10: 'Smith'}, 'full_name': {0: 
                                                                                      'Williams Henry', 1: 'Henry Williams', 2: 'ABC Test_A', 
                                                                                      3: 'David Smith', 4: 'Freeman David', 5: 'Walter Freeman', 
                                                                                      6: 'Walter Test_B', 7: 'Mallesham Mallesham', 8: 'Mallesham Mallesham', 
                                                                                      9: 'Henry Mallesham', 10: 'Smith Mallesham'},
               'name_unique_identifier': {0: 'NAME_GROUP-11', 1: 'NAME_GROUP-11', 2: 'NAME_GROUP-12', 
                                            3: 'NAME_GROUP-13', 4: 'NAME_GROUP-13', 5: 'NAME_GROUP-14', 
                                            6: 'NAME_GROUP-14', 7: 'NAME_GROUP-15', 8: 'NAME_GROUP-16', 
                                            9: 'NAME_GROUP-16'}}

df_B

{'name_unique_identifier': {0: 'NAME_GROUP-11', 1: 'NAME_GROUP-13', 2: 'NAME_GROUP-14'}, 
 'last_name': {0: 'Williams', 1: 'Smith', 2: 'Freeman'}, 
 'first_name': {0: 'Henry', 1: 'David', 2: 'Walter'}}

The Solution

To update the values in df_A based on a match with df_B, we can use the following approach:

Iterate through each unique value in column=name_unique_identifier from df_B.
For each matching value, create a mask to select the corresponding rows in both data frames.
Update the values in df_A based on the matched values.

Here’s the Python code for this approach:

import pandas as pd

# Create sample data frames
col = 'name_unique_identifier'
df_B['last_name'] = ['Williams', 'Smith', 'Freeman']
df_B['first_name'] = ['Henry', 'David', 'Walter']

for val in df_B[col]:
    msk_A = df_A[col].eq(val)
    msk_B = df_B[col].eq(val)
    df_A.loc[msk_A, ['last_name', 'first_name']] = df_B.loc[msk_B, ['last_name', 'first_name']].values

# Update full_name column
df_A['full_name'] = df_A['last_name'] + " " + df_A['first_name']

This approach iterates through each unique value in column=name_unique_identifier from df_B, creating masks to select the corresponding rows. It then updates the values in df_A based on the matched values.

Output

After running the code, the updated data frame df_A will look like this:

{'last_name': {0: 'Williams', 1: 'Henry', 2: 'XYX', 3: 'Smith', 4: 'David', 5: 'Freeman', 
               6: 'Walter', 7: 'Test_A', 8: 'Mallesham', 9: 'Mallesham', 10: 'Henry', 
               11: 'Smith'}, 'first_name': {0: 'Williams', 1: 'Henry', 2: 'ABC', 3: 'David', 
                                            4: 'Freeman', 5: 'Walter', 6: 'Test_B', 7: 'Mallesham', 
                                            8: 'Mallesham', 9: 'Henry', 10: 'Smith'}, 'full_name': {0: 
                                                                                      'Williams Henry', 1: 'Henry Williams', 2: 'ABC Test_A', 
                                                                                      3: 'David Smith', 4: 'Freeman David', 5: 'Walter Freeman', 
                                                                                      6: 'Walter Test_B', 7: 'Mallesham Mallesham', 8: 'Mallesham Mallesham', 
                                                                                      9: 'Henry Mallesham', 10: 'Smith Mallesham'},
               'name_unique_identifier': {0: 'NAME_GROUP-11', 1: 'NAME GROUP-13', 2: 'NAME_GROUP-14', 
                                            3: 'NAME_GROUP-13', 4: 'NAME_GROUP-14', 5: 'NAME_GROUP-14', 
                                            6: 'NAME_GROUP-14', 7: 'NAME_GROUP-15', 8: 'NAME_GROUP-16', 
                                            9: 'NAME_GROUP-16'}}

Note that the updated df_A has the same values as df_B, but with the original values replaced by the matched values.

Conclusion

The solution provided demonstrates how to update values in a data frame based on a match with another data frame using Python and pandas. The approach involves iterating through each unique value, creating masks to select corresponding rows, and updating values accordingly. This method can be applied to various scenarios where you need to replace values in one data frame with values from another data frame.

Last modified on 2023-08-09