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Essential PySpark for Scalable Data Analytics

You're reading from   Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Length 322 pages
Edition 1st Edition
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Author (1):
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Sreeram Nudurupati Sreeram Nudurupati
Author Profile Icon Sreeram Nudurupati
Sreeram Nudurupati
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer FREE CHAPTER 3. Chapter 2: Data Ingestion 4. Chapter 3: Data Cleansing and Integration 5. Chapter 4: Real-Time Data Analytics 6. Section 2: Data Science
7. Chapter 5: Scalable Machine Learning with PySpark 8. Chapter 6: Feature Engineering – Extraction, Transformation, and Selection 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Machine Learning Life Cycle Management 12. Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark 13. Section 3: Data Analysis
14. Chapter 11: Data Visualization with PySpark 15. Chapter 12: Spark SQL Primer 16. Chapter 13: Integrating External Tools with Spark SQL 17. Chapter 14: The Data Lakehouse 18. Other Books You May Enjoy

Considerations for PySpark to pandas conversion

This section will introduce pandas, demonstrate the differences between pandas and PySpark, and the considerations that need to be kept in mind while converting datasets between PySpark and pandas.

Introduction to pandas

pandas is one of the most widely used open source data analysis libraries for Python. It contains a diverse set of utilities for processing, manipulating, cleaning, munging, and wrangling data. pandas is much easier to work with than Pythons lists, dictionaries, and loops. In some ways, pandas is like other statistical data analysis tools such as R or SPSS, which makes it very popular with data science and machine learning enthusiasts.

The primary abstractions of pandas are Series and DataFrames, with the former essentially being a one-dimensional array and the latter a two-dimensional array. One of the fundamental differences between pandas and PySpark is that pandas represents its datasets as one- and two-dimensional...

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