Скачать или смотреть How to Groupby and Aggregate in Python with Pandas Using Two Columns

Learn how to efficiently summarize your dataset using Pandas' `groupby` and `agg` functions in a single line of code, without complex merges.
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How to Groupby and Aggregate in Python with Pandas Using Two Columns

When working with data in Python, specifically using the Pandas library, you'll often encounter scenarios where you need to summarize your datasets based on multiple columns. For instance, you may wish to obtain both the sum and count of certain columns—but doing so can sometimes complicate your code. Let's explore how to handle this effectively in a clean and straightforward manner.

The Problem

Suppose you have a dataset that contains various categorical columns such as country, item, date, and order, along with numerical columns like size and weight. You want to aggregate this data to get the total sum and the count of size and weight for each unique combination of country, item, date, and order.

Initially, one might think about using separate lines of code to retrieve the sum and count individually, like below:

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

However, you want to achieve this in one line using Pandas' agg function. Your initial attempt may return an error, like this:

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

This error occurs because the way you are attempting to structure your agg function is incorrect.

The Solution

The good news is that you can indeed summarize your dataset elegantly in one go! Here’s how you can do it step by step:

Step 1: Grouping Data

Use the groupby function to specify which columns you want to group by. In this case, you'll group by country, item, date, and order.

Step 2: Aggregating with agg

Next, use the agg function to aggregate the size and weight columns by both sum and count. Here’s the code to accomplish this:

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

Step 3: Flattening Multi-level Columns

Once you've aggregated the data, the resulting DataFrame will have multi-level column names. To make this more manageable, you can flatten the column names:

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

Step 4: Resetting the Index

Finally, you'll want to reset the index of the summary DataFrame to restore the grouped columns to their own columns:

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

Final Code

Putting it all together, your complete code will look like this:

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

Conclusion

By following these steps, you can efficiently summarize your data based on multiple columns in a single line of code. This method not only simplifies your code but also enhances readability and performance. Now, you can explore your dataset with ease and derive insights without excessive complexity. Happy coding!