How to Compare Two Excel Revisions in Python Pandas

Learn how to effectively use `Python Pandas` to compare two Excel files, identify changes, and list discontinued products.
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How to Compare Two Excel Revisions in Python Pandas

Comparing two revisions of Excel files is a common task that many data analysts face. Whether you are trying to track changes in product pricing or identify new entries, understanding how to efficiently utilize tools like Python and Pandas can make this process much easier. In this guide, we’ll explore how to compare two Excel files using Pandas, highlight the changes, identify discontinued products, and output the results in a structured format.

The Problem

When working with two Excel files containing product data, it can be challenging to compare them in a way that effectively illustrates the changes between revisions. Consider the following two CSV files used as a reference:

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The challenge arises when trying to capture the differences. A common error encountered in Python Pandas is "ValueError: can only compare identically labeled objects", which happens when the number of rows in both files doesn’t match.

The Solution

Let’s break down the solution step-by-step for better understanding. You will need to follow these main steps:

Read the Excel files.

Merge the data frames.

Identify changes and new entries.

Output the results to a new Excel file.

Step 1: Read the Excel Files

Start by importing the required libraries and reading both old and new Excel files. You can use the following code:

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Step 2: Merge the Data Frames

To compare the two datasets, we will merge them using the product identifier. This allows us to see both the old and new data side by side. We will do a full outer join, which includes all entries from both data frames:

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Step 3: Identify Changes and New Entries

Next, we will focus on extracting the changes. Specifically, we want to find the products with updated prices or new entries. Use the following code to filter the changes:

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In this line, we check for two conditions: if the new price is greater than the old price or if the old price is missing, indicating a new entry.

Step 4: Output Results

Finally, we will save the identified changes into a new Excel file. We will utilize the openpyxl library for this purpose:

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This code creates a new Excel workbook and appends the changes we identified to it, saving the final output for your review.

Bonus: Identifying Discontinued Products

One additional feature you might want to implement is to identify discontinued products. This can be done by checking entries in the old data that do not appear in the new data:

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This will give you a list of products that are no longer present in the new data.

Conclusion

In this guide, we've covered how to compare two Excel files using Python and Pandas effectively. By following these steps, you can easily identify changes, new entries, and discontinued products. This process can greatly enhance your data management workflow and ensure that you keep accurate records as revisions occur. Happy coding!