PySpark SQL count() Function: How to Count Rows and Column Values

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Hello everyone! In this video, we’ll be exploring the count function in PySpark—a fundamental tool for anyone working with data. Whether you need to count the total number of rows in your dataset, count non-null values in a column, or calculate the frequency of specific entries, the count function has got you covered. We’ll go over the different ways you can use count to understand your data better, from counting rows to counting values after grouping, making your data analysis tasks easier and more efficient.

What you’ll learn:

Introduction to the count function in PySpark
How to count the total number of rows in a DataFrame
Counting non-null values in a specific column
Using count in combination with groupBy to count occurrences
Practical examples for everyday data analysis tasks
By the end of this video, you'll have a solid understanding of how to use the count function to gain quick insights into your dataset. If this video helps you, make sure to like, share, and subscribe for more PySpark and data science content!

Hashtags: #PySpark #countFunction #RowCount #DataAnalysis #DataScience #BigData #ApacheSpark #PySparkSQL #DataEngineering #DataProcessing #SQL #Python #DataExploration #PySparkTutorial
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