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Intermediate

3h 3min

From Pandas to PySpark DataFrame
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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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Table of Contents

Learner Reviews

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

Pandas is a popular Python library used to manipulate data, but it has certain limitations in its ability to process large datas...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

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

Course Content

39 Lessons3 Quizzes

Introduction

2 Lessons

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

Getting Started

Overview of Dataset

Data Input/Output

10 Lessons

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

16 Lessons

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)

8 Lessons

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

1 Lessons

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.

Amazon Review Data (2018)

pandas and PySpark: Behind the Scenes

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

MrDataPsycho

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From Pandas to PySpark DataFrame Save for later

From Pandas to PySpark DataFrame
Save for later