Скачать или смотреть Mastering Multi-Level GroupBy and Sum in Pandas

Learn how to implement `multi-level groupby` and sum in Pandas DataFrames effectively. This guide provides clear instructions and examples for filtering and analyzing data.
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Mastering Multi-Level GroupBy and Sum in Pandas: A Complete Guide

When working with data in Python, particularly using the Pandas library, you'll face a range of challenges. One common problem arises when attempting to filter a DataFrame based on multiple columns and then aggregating the results, especially when you need to analyze data at multiple levels. In this guide, we'll discuss how to accomplish multi-level groupby operations and sums effectively.

Understanding the Problem

Imagine you have a DataFrame that contains sales data, broken down by product over different weeks and years. Your goal is to summarize this data at two levels: first by year and then by week. You want to find the total sales for the product as well as counts for two additional metrics, se and sqe, without merging these metrics into a single value.

Example DataFrame

Consider the following sample DataFrame:

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

This DataFrame looks like this:

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

Expected Output

The desired output aggregates the product sales along with the se and sqe counts for each week within each year, structured in a nested dictionary format:

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

Implementing the Solution

Now, let’s break down how to achieve this using Pandas.

Step 1: Grouping the Data

To perform a multi-level groupby operation, we can use the following code snippet:

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

Step 2: Understanding the Code

Let’s dissect what each part of this line does:

df.groupby(by="year"): This part groups the entire DataFrame by the year column.

.apply(lambda grp: ...): We then apply a function to each group (each year) created in the previous step.

grp.groupby(by="week"): Inside the lambda function, we group the data further, this time by the week within each year.

[["product", "se", "sqe"]].sum(): This selects the columns we want to aggregate (here, these are product, se, sqe) and computes their sums.

.to_dict("index"): This converts the resulting DataFrame into a dictionary format, where each inner dictionary corresponds to a week and contains sums for product, se, and sqe.

.to_dict(): Finally, we convert the result to a dictionary at the outer level so that it provides one key for each year.

Final Output

When you run the provided code, you will get:

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

Conclusion

Utilizing the groupby functionality in Pandas allows you to effectively summarize and analyze complex datasets by implementing multi-level groupings. With the method outlined above, you can derive insightful summaries from your data on multiple criteria effortlessly. Whether you are handling sales data, survey results, or any other form of quantitative data, mastering these aggregation techniques can significantly improve your data analysis capabilities in Python.

Feel free to experiment with your own datasets and see how you can apply these methods to gain even deeper insights!