Compare two sheets and show a summary with only rows where a specific column has changed for that primary key, without altering source data

2 min read 24-10-2024

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Compare two sheets and show a summary with only rows where a specific column has changed for that primary key, without altering source data

In the realm of data management, it is often necessary to compare two datasets to identify changes or discrepancies. This article will guide you through the process of comparing two sheets and summarizing only the rows where a specific column has changed for a given primary key, without altering the original data.

Original Problem Scenario

The task involves comparing two datasets (or sheets) and extracting a summary that displays only the rows where a particular column's value has changed based on a unique identifier, also known as the primary key. The goal is to ensure that the source data remains unaltered throughout this process.

Example Code for Comparison

Below is an example of code that achieves this functionality using Python's pandas library:

import pandas as pd

# Sample data: Creating two DataFrames to represent the two sheets
data_1 = {
    'ID': [1, 2, 3, 4],
    'Value': ['A', 'B', 'C', 'D']
}
data_2 = {
    'ID': [1, 2, 3, 4],
    'Value': ['A', 'X', 'C', 'Y']
}

df1 = pd.DataFrame(data_1)
df2 = pd.DataFrame(data_2)

# Compare the two sheets and find changed rows based on 'ID' and 'Value'
merged_df = pd.merge(df1, df2, on='ID', suffixes=('_old', '_new'))
changed_rows = merged_df[merged_df['Value_old'] != merged_df['Value_new']]

# Display the summary of changed rows
summary = changed_rows[['ID', 'Value_old', 'Value_new']]
print(summary)

Analysis and Explanation

How the Code Works

Data Creation: Two sample datasets are created as pandas DataFrames. The data_1 DataFrame represents the original data, while data_2 contains the modified data.
Merging DataFrames: The two DataFrames are merged using the ID column. The suffixes parameter helps distinguish between the old and new values.
Finding Changes: The comparison is performed using a simple condition that checks where the old value is not equal to the new value.
Summarizing Results: Finally, the relevant columns (ID, old value, and new value) are extracted into a summary DataFrame, which provides a clear view of the changes.

Practical Example

Suppose you are working in a marketing department where you track customer engagement through a table in a spreadsheet. In the first month, you collect data about customer interactions, and in the following month, you update that data. By using the above method, you can easily see which customer interactions have changed without risking the integrity of your original data.

Additional Considerations

Performance: When dealing with large datasets, consider performance optimizations. Using indexed DataFrames or employing more efficient data structures can reduce processing time.
Column Selection: The example above compares the "Value" column specifically, but you can adjust the code to compare any column by changing the variable name.
Data Validation: Always validate your data before performing comparisons to avoid inaccuracies that could lead to erroneous conclusions.

Conclusion

By implementing the provided code, you can effectively compare two datasets and create a summary of changes based on a specific column without altering your original data. This practice not only enhances your data management skills but also ensures the accuracy and integrity of your reports.

Useful Resources

In conclusion, understanding how to efficiently compare datasets is an invaluable skill in data analysis. By leveraging Python and pandas, you can streamline your workflow and produce clear, actionable insights from your data.