Скачать или смотреть Mastering sum() on Specific DataFrame Columns in Python Pandas

Learn how to efficiently summarize your DataFrame by adding a total row with conditional summation using Python's Pandas library.
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Mastering sum() on Specific DataFrame Columns in Python Pandas

When working with data, especially in Python's Pandas library, it’s common to find yourself needing to calculate totals for specific columns in a DataFrame. This task can quickly become complicated, particularly when there are filters involved or when you need to append the result as a new row. If you’re struggling with how to apply sum() effectively while managing these complexities, you’re in the right place.

In this article, we’ll go through a common scenario: how to sum specific columns in a DataFrame and include this as a total row, while adhering to specific conditions.

The Problem

Imagine you have a DataFrame that summarizes different categories of data, like below:

[[See Video to Reveal this Text or Code Snippet]]

Your goal is to create an additional row, labeled Total, that summarizes col3, col4, and col5. Importantly, you want to exclude rows where CategID equals 2 from these totals. Additionally, col6 should reflect a custom formula based on a division of the sums of col4 and col5.

Desired Output

The expected result looks like this:

[[See Video to Reveal this Text or Code Snippet]]

The Solution

Let’s break down how we can accomplish this task using Pandas. We’ll use the mask() function alongside a few other Pandas methods to achieve our desired output.

Step 1: Filtering and Summing

First, we’ll filter out the rows where CategID is 2, and then we will apply the sum() method on the appropriate columns:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Calculating the Custom Value for col6

For col6, we want to compute a specific formula that involves the sums of col4 and col5. After summing the other columns, we can add this calculation:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Appending the Total Row

Now that we have our summary in s, we can append it back to the DataFrame as a new row:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Final Adjustments

It’s also important to note that we need to fill in the values for the new Total row appropriately. For example:

[[See Video to Reveal this Text or Code Snippet]]

Complete Code Example

Here’s the complete code wrapped up:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

With the above steps, we’ve successfully created a Total row that summarizes our DataFrame while adhering to the specified conditions. This procedure not only simplifies complex DataFrame manipulations but also provides a clear approach to summing specific columns in Pandas.

Feel free to experiment with this method for your own datasets, and streamline your data analysis process!