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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PandasAndPyspark

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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

## System monitor in normal state

Let’s take a closer look at loading data using pandas or Pyspark with respect to hardware or operating systems. The image below represents the state of CPU and RAM when the computer is up and running, and we haven’tt started any data analysis tasks. As we can see below, the CPU utilization is significantly less, and other general applications in the system acquire 4GB of memory.

# System monitor in normal state

Let’s take a closer look at loading data using pandas or Pyspark with respect to hardware or operating systems. The image below represents the state of CPU and RAM when the computer is up and running, and we haven’tt started any data analysis tasks. As we can see below, the CPU utilization is significantly less, and other general applications in the system acquire 4GB of memory.

Learn about the memory usage of pandas and PySpark.


pandas and PySpark: Behind the Scenes


Learn about the memory usage of pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

pandas and PySpark: Behind the Scenes

System monitor in normal state