Gain insights into enhancing Python data processing with PySpark. Delve into reading, transforming, aggregating data, and creating user-defined functions, boosting efficiency with Apache Spark.

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Pandas is a popular Python library used to manipulate data, but it has certain limitations in its ability to process large datasets. The Apache Spark analytics library offers significant performance improvements.

This course will help improve your Python-based data processing by leveraging Apache Spark’s multithreading capabilities through the PySpark library. You’ll start by reading data into a PySpark DataFrame before performing basic input/output functions, such as renaming attributes, selecting, and writing data. You’ll move onto transformation functions like aggregation, statistical analysis, and joins before creating custom, user-defined functions. At each step, you’ll get a quick Pandas review before being walked through leveraging the more robust PySpark library to unlock Apache Spark.

By the end of this course, you’ll be able to quickly and reliably process large amounts of data, even stored across multiple files, using PySpark.

From Pandas to PySpark DataFrame

## Overview
The majority of the use cases we encounter in our day-to-day analysis or data engineering work can be resolved with methods or functions provided by the SQL or DataFrame API in PySpark. If built-in methods are not enough, we can write our own function, which we can use for a custom transformation.
Writing user-defined functions requires a deeper understanding of both data structure and how a pure Python data structure is represented in PySpark. The return type of **user defined functions (UDF)** must be static. Therefore, the return data structure must be provided by us in a form of PySpark type. Moreover, UDF are the most expensive (less optimized) operations, hence we use them only when necessary and have no other choice.



# Overview
The majority of the use cases we encounter in our day-to-day analysis or data engineering work can be resolved with methods or functions provided by the SQL or DataFrame API in PySpark. If built-in methods are not enough, we can write our own function, which we can use for a custom transformation.
Writing user-defined functions requires a deeper understanding of both data structure and how a pure Python data structure is represented in PySpark. The return type of **user defined functions (UDF)** must be static. Therefore, the return data structure must be provided by us in a form of PySpark type. Moreover, UDF are the most expensive (less optimized) operations, hence we use them only when necessary and have no other choice.



Learn about user-defined functions in detail.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Apriori Algorithm for Finding Frequent Itemsets with PySpark

Introduction to User-defined Functions

Overview