In this hands-on lab, you will master your knowledge of PySpark, a very popular Python library for big data analysis and modeling. Here, you will learn how to create a dataset using the PySpark library, and to manipulate it using standard filtering and slicing techniques. Your data management skills will be challenged, and by the end of this lab, you should have a deep understanding of how PySpark practically works to build data analysis pipelines.
Learning Objectives
Upon completion of this lab you will be able to:
- create a Spark Session, and store the data into a Spark DataFrame;
- query data with PySpark using standard SQL;
- create a new column inside the Spark DataFrame;
- perform standard data cleaning - type consistency, filtering, slicing;
- pivoting and manipulating a Spark DataFrame.
Intended Audience
This lab is intended for:
- Those interested in performing data analysis with Python.
- Anyone involved in data science and engineering pipelines.
Prerequisites
You should possess:
- An intermediate understanding of Python.
- Basic knowledge of SQL.
- Basic knowledge of the following libraries: Pandas.
Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.
He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.