Скачать или смотреть How to Add a Second Group By to Value Counts in Pandas

Learn how to effectively group data in Pandas with multiple criteria using a practical example involving meal times and city values.
---
This video is based on the question https://stackoverflow.com/q/64052035/ asked by the user 'Chris90' ( https://stackoverflow.com/u/8797830/ ) and on the answer https://stackoverflow.com/a/64052064/ provided by the user 'Quang Hoang' ( https://stackoverflow.com/u/4238408/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Adding a second group by to valuecounts

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Add a Second Group By to Value Counts in Pandas

Handling data in Python using the Pandas library can sometimes pose challenges, especially when you need to analyze it from multiple perspectives. A common problem arises when you want to apply grouped value counts on more than one categorical column. In this guide, we will dive into how you can achieve this with a simple yet practical example.

The Problem: Need for Dual Grouping

Suppose you have a DataFrame that contains meal timings associated with different companies, and you want to understand how these meal times distribute across multiple cities. Here’s what the initial data looks like:

Sample DataFrame Structure:

company: Different companies associated with each meal time

mealtime: Categories like breakfast, lunch, dinner, and latemeal

city: Various city names where these companies operate

When running the code below, you get the value counts for mealtime across companies:

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

This generates a summary such as:

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

However, your goal is to further dissect this information by including cities, so that you can see mealtime distribution per city.

Desired Output:

You want to transform the output to show the following structured data:

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

The Solution: Implementing a Second Group By

To solve the problem of grouping by both city and mealtime, we use the groupby function in Pandas. This allows us to achieve a result that includes counts for each combination.

Solution Steps:

Filter DataFrame: First, ensure you're only working with relevant companies.

Group By Columns: Use groupby on both city and mealtime.

Value Counts: Count occurrences of each group.

Reset Index: Reformat the results for easier readability.

Implementation

Here’s the code snippet that accomplishes this:

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

Explanation:

df.loc[df['company'].isin(companies)]: This filters the DataFrame to only include rows where the company is in the specified list of companies.

.groupby(['city', 'mealtime']): This groups the data first by city and then by mealtime.

.value_counts(): This counts the entries for each group.

.reset_index(name='value'): This resets the index to provide a clean DataFrame that includes a new column called value with the counts.

Sorting the Results

If you’d like to see which city has the largest value in either breakfast or lunch, you can easily add a sorting command after the group operation:

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

This will sort your DataFrame by city names and the values in descending order.

Conclusion

By using Pandas effectively with the dual groupby strategy, you can unlock powerful insights from your datasets. Now, you can quickly summarize and analyze meal times across different cities, which can aid in making informed decisions for business strategies or operational improvements.

Feel free to implement the solution shared above on your datasets, and watch as complex data becomes easier to analyze with just a few lines of code!