Скачать или смотреть How to Group By Multiple Columns in Pandas

Learn how to efficiently use the `groupby` function in pandas to aggregate and analyze data across multiple columns. Improve your data analysis skills with this easy-to-follow guide!
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How to Group By Multiple Columns in Pandas: A Complete Guide

When working with data in Python, especially in the realm of data science, you often need to analyze and aggregate data for insights. One common scenario is grouping by multiple columns in a DataFrame to summarize the information. Today, we'll explore how to effectively use the groupby feature of the pandas library to achieve this.

The Problem

Imagine you have a DataFrame containing data concerning various entities, and each entity has multiple attributes, such as whether they are associated with a Factory, Restaurant, Store, or Building. In this scenario, you want to know how many of these attributes are True for each entity.

Let's take a closer look at our DataFrame, which appears as follows:

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

You may start with code to group by a single column like 'Factory' and sum its values:

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

However, you're looking to extend this to count True values across multiple columns, such as 'Restaurant', 'Store', and 'Building'. This requires a more sophisticated approach.

The Solution

To achieve the desired output, you can follow these steps:

Step 1: Define the Columns

First, identify the columns you want to aggregate. In this example, we'll work with the following columns:

Factory

Restaurant

Store

Building

Define them in a list:

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

Step 2: Group By Name and Sum the Values

Next, use the groupby function, and apply the sum function directly on these columns. This approach is more efficient than using apply and avoids potential issues with performance in pandas due to non-vectorized operations.

Here’s how you do it:

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

Expected Output

Once you execute the above code, your output will look something like this:

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

Breakdown of the Output

Brian has 2 instances of True for Factory, 0 for Restaurant, 1 for Store, and 1 for Building.

Mike has 2 for Factory, 1 for Restaurant, 1 for Store, and 1 for Building.

Sam has 1 for Factory, 0 for Restaurant, 1 for Store, and 1 for Building.

The resulting DataFrame summarizes the number of True values across all specified columns for each individual, as desired.

Conclusion

Grouping by multiple columns in pandas is a straightforward process once you understand how to utilize the groupby function effectively. By applying an efficient method and avoiding potential pitfalls from non-vectorized operations, you can enhance your data analysis workflows significantly.

Feel free to incorporate this method into your data analysis toolkit for better insights into your datasets!