From Pandas to PySpark DataFrame

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.

Intermediate

39 Lessons

3h 3min

Certificate of Completion

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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

54 Playgrounds

This course includes

54 Playgrounds

Course Overview

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 wr...Show More

What You'll Learn

A working knowledge of Apache Spark and the PySpark library for Python

A strong understanding of the advantages of using PySpark instead of Pandas for processing large datasets

The ability to calculate some Metrics or produce aggregated analytics reporting solutions

The ability to write Production Code in PySpark

What You'll Learn

A working knowledge of Apache Spark and the PySpark library for Python

Course Content

Introduction

Learn how to use PySpark for large-scale data processing and Amazon Review Data analysis.

Getting Started

Overview of Dataset

Data Input/Output

Walk through data input/output processes including reading, renaming, selecting, saving, and challenges.

Introduction to Data Input and Output Read Data into DataFrame Rename Attributes Select a Subset of Attributes Data Input and Output: Save a Snapshot Read Parquet Data Source Write Production Code Quiz: Data Input and Output Challenge: Data Input and Output Solution: Data Input and Output

Data Transformation

Work your way through transforming data, handling date-time, imputing, and evaluating reviews using pandas and PySpark.

Introduction to Data Transformation Setup Handling Date-time Impute Unavailable Data Points Average Review per Product Total Number of Reviews for Each Product Distribution of the Review Text Length Yearly Median Review Top reviews of 2017 Compare Total Review of 2016 and 2017 Conversion Between Wide and Long Format using melt and pivot Date Transformation: Save a Snapshot Avoid Global Scope Quiz: Data Transformation Challenge: Data Transformation Solution: Data Transformation

User Defined Function (UDF)

Build a foundation in creating and using UDFs in PySpark for custom transformations.

Introduction to User-defined Functions Object Conversion Between Python and Scala Writing UDF UDF in Action UDF: Save a snapshot Quiz: User-defined Functions Challenge: User-defined Functions Solution: User Defined Function

Wrapping Up

Solve problems in PySpark and pandas with newly acquired foundational skills.

Conclusion

Appendix

2 Lessons

Focus on the Amazon Review Data (2018) and Pandas vs. PySpark performance.

Apriori Algorithm for Finding Frequent Itemsets with PySpark

Project

Course Author

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Emma Bostian 🐞

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