Скачать или смотреть How to Drop Rows from Dask Based on Value Count Threshold

Learn how to efficiently filter rows from a Dask DataFrame based on a specific count threshold for values in a column. Simplify your data manipulation tasks using Dask!
---
This video is based on the question https://stackoverflow.com/q/72192266/ asked by the user 'Sarah Bolton' ( https://stackoverflow.com/u/9258477/ ) and on the answer https://stackoverflow.com/a/72193717/ provided by the user 'constantstranger' ( https://stackoverflow.com/u/18135454/ ) 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: How do you drop rows from Dask where the value count doesn't meet a certain threshold?

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.
---
Dropping Rows from Dask Based on Value Count Threshold

When working with large datasets, especially in formats like CSV, efficient data manipulation becomes crucial. For those familiar with Pandas, Dask can feel a bit daunting. One common task is filtering rows in a DataFrame based on whether the count of specific values meets a given threshold.

Imagine you have a substantial dataset, for instance, an uncompressed CSV that weighs around 20 GB. Now, you need to filter out rows where the count of instances for certain values in a column is less than a specified threshold, say, 3. Let’s break down how we can accomplish this in Dask with ease.

Problem Overview

You might have a dataset structured like this:

icaocallsignregacftTypeabcdefETH720ET-ASJB738abcdefETH720ET-ASJB738123456IBE6827EC-LUKA333789ghiFRH571OO-ACEB744In this case, if we set our threshold to 3, we want to retain rows with icao values that have at least three occurrences or more. From our initial dataset, abcdef clearly meets this requirement, while 123456 does not. Thus, we would want our output to look like:

icaocallsignregacftTypeabcdefETH720ET-ASJB738789ghiFRH571OO-ACEB744Solution Strategies

While it may seem complex at first, Dask provides flexible ways to achieve this goal. Here are two effective strategies:

Strategy # 1: Using GroupBy with Dummy Column

Count occurrences: Create a dummy column to enable counting per icao.

Join with initial DataFrame: Merge the count back into the original DataFrame.

Drop unnecessary columns: Get rid of the dummy column from your final output.

Here’s the code for this strategy:

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

Strategy # 2: GroupBy, Count and Filter

Count only the necessary column: Group by icao and focus on counting occurrences of relevant columns.

Drop unnecessary columns: Keep groups with counts meeting or exceeding the threshold, then merge this back with your original DataFrame.

This approach can be implemented like this:

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

Quick Example

Let’s visualize this with the provided example input:

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

If you apply either of the above strategies with a threshold of 3, your output should successfully filter down to:

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

Conclusion

Filtering rows in a Dask DataFrame based on the count of occurrences may seem a bit convoluted at first, especially for those used to Pandas. However, with the strategies outlined above, you can effortlessly drop rows that don't meet your specified threshold. Remember, practice makes perfect, and as you get more familiar with Dask, these operations will become second nature!

For more insights into Dask and data manipulation techniques, stay tuned for our upcoming posts!