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

# Comparison in Pandas


To compare the total reviews of 2016 and 2017, we first need to aggregate the data by the review year and month. Next, we need to count the number of `asin` for each month. Then we can subset the new DataFrame with a filter to create new, separate DataFrames for 2016 and 2017. Finally, we join the two new DataFrames to create a wide DataFrame, where the total reviews for each month for the years 2016 and 2017 will be side by side.

Learn to compare data in pandas and PySpark.


Compare Total Review of 2016 and 2017

Learn to compare data in pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Compare Total Review of 2016 and 2017

Comparison in Pandas