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Learn how to drop duplicates in a Pandas DataFrame only when specific conditions are met, especially when dealing with boolean values like holiday status.
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How to Drop Duplicates in Pandas Based on Specific Conditions: A Step-by-Step Guide

When working with data in Python, particularly with the Pandas library, you may encounter situations where you want to remove duplicate rows. However, sometimes, you may only want to drop duplicates under certain conditions, specifically if a boolean value or specific criteria are met. In this guide, we will show you how to drop duplicates in a DataFrame, focusing on cases where you want to keep rows based on certain boolean flags, like a Holiday status.

The Problem: Removing Duplicates with Specific Conditions

Imagine you have a DataFrame that contains data regarding employee leaves, including their names, requested dates, a holiday flag, and a subject of leave. Here’s a sample of what the DataFrame might look like:

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In this example, the goal is to drop duplicate entries only when the holiday column is True and the name and date match. Using the standard drop_duplicates() function without any condition will not yield the desired results.

The Solution: A Step-by-Step Approach

To effectively meet this requirement, we can utilize a combination of DataFrame filtering and the drop_duplicates() function. Here’s how you can do this:

Step 1: Create a Mask

The first step is to create a mask that indicates which rows have holiday set to True. This will help us filter the DataFrame into two parts: those rows where holiday is True and those where it isn’t.

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Step 2: Split and Deduplicate

Next, we will split the DataFrame based on our mask. For the True condition of holiday, we use the drop_duplicates() function on the subset of data we're interested in (name and date). Meanwhile, we leave the other part untouched.

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Explanation of Code

Filtering by Holiday: We use the mask to filter the DataFrame. df.loc[mask] selects rows where holiday is True.

Removing Duplicates: We apply the drop_duplicates() function to this filtered DataFrame with the subset parameter set to the relevant columns (name and date). The keep='first' argument ensures that we maintain the first occurrence of each duplicate.

Combining Results: Finally, we concatenate the deduplicated DataFrame with the untouched part of the original DataFrame, ensuring that none of the False holiday entries are removed.

Step 3: Check the New DataFrame

After executing the code above, your DataFrame should now reflect the conditions specified:

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Conclusion

By using the above steps, we successfully dropped duplicate rows in a Pandas DataFrame based on specific boolean criteria (in this case, the holiday column). This method allows for flexibility and precision when handling data, ensuring that only the rows you want to keep remain in your DataFrame.

Now you can apply this technique to your own datasets, adapting the conditions as needed. Happy coding!