From Pandas to PySpark DataFrame

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1.Introduction

Getting Started Overview of Dataset

2.Data Input/Output

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

3.Data Transformation

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

4.User Defined Function (UDF)

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

5.Wrapping Up

6.Appendix

Amazon Review Data (2018)pandas and PySpark: Behind the Scenes

Project

Apriori Algorithm for Finding Frequent Itemsets with PySpark

From Pandas to PySpark DataFrame/

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Introduction to Data Transformation

Introduction to Data Transformation

Learn about the data transformation content flow.

We'll cover the following...

Overview

PySpark and pandas’ native API provides almost all the commonly used data transformation techniques as functions or methods. Because this API’s list of objects, functions, and methods is so extensive, we’ll only explore a few ...