Скачать или смотреть How to Count Distinct Values in Multiple Columns with Pyspark

Discover how to effectively count distinct values in multiple DataFrame columns using Pyspark, even when dealing with large datasets and many unique values.
---
This video is based on the question https://stackoverflow.com/q/67942729/ asked by the user 'drew_psy' ( https://stackoverflow.com/u/9381181/ ) and on the answer https://stackoverflow.com/a/67943368/ provided by the user 'werner' ( https://stackoverflow.com/u/2129801/ ) 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: Pyspark count for each distinct value in column for multiple columns

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.
---
Counting Distinct Values in Pyspark DataFrames

In the field of data analysis, it's often necessary to perform aggregations on different columns of a dataset. One common problem that arises is counting the distinct values within multiple columns of a DataFrame. If you're using Pyspark, you might find yourself needing to achieve this for large datasets. In this guide, we'll tackle how to count distinct values for multiple columns in a Pyspark DataFrame.

The Problem: Counting Distinct Values

Let's consider a scenario where you have a DataFrame with various columns such as state, country, and zip. Here’s an example of how that DataFrame might look:

idstatecountryzip1AAAUSA1232XXXCHN2343AAAUSA1234PPPUSA2225PPPUSA2225XXXCHN234Desired Output

The goal is to create a flat DataFrame that contains arrays of counts for each distinct value in the columns, yielding an output like the following:

statecountryzip[[AAA, 2],[PPP,2],[XXX,2]][[USA, 4],[CHN,2]][[123, 2],[234, 2],[222, 2]]In our case, we want to focus on columns that contain fewer than 100 unique values.

The Solution: Pyspark Implementation

The good news is that with Pyspark, this is a straightforward task. Here’s how you can achieve this in just a few steps:

Step 1: Setup Your DataFrame

First, ensure you have your DataFrame loaded. For the sake of example, we will consider our DataFrame is already created.

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

Step 2: Identify the Columns to Process

Next, retrieve the column names, excluding the id column since it's not relevant for counting distinct values.

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

Step 3: Count Distinct Values for Each Column

Now loop through the identified columns to compute counts for each unique value, aggregating them into a list format.

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

Step 4: Combine the Results

Finally, use a join to combine all the individual DataFrames into a single dataset.

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

Sample Output

After running the above code, the output should be similar to this:

statecountryzip[[PPP, 2], [XXX, 2], [AAA, 2]][[USA, 4], [CHN, 2]][[222, 2], [234, 2], [123, 2]]Conclusion

With this method, you can effortlessly count the distinct values across multiple columns in your Pyspark DataFrame, even when dealing with numerous unique entries. By leveraging aggregation functions, you can turn complex data analysis tasks into manageable and comprehensible operations.

Feel free to adapt the provided code snippet to fit your specific dataframe scenarios and requirements. Happy coding!