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.

frompandastopyspark.tar.gz

PandasAndPyspark

jupyter

spa-jupyter

spa-snapshot

spa-copy

spa-copy-copy

spa-copy-copy-5klyxp

spa-snapshot-copy

spa-snapshot-copy-copy

spa-copy-copy-5klyxp-copy

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 writing data. You’ll move onto transformation functions like aggregation, statistical analysis, and joins before creating custom, user-defined functions. At each step, you’ll get a quick Pandas review before being walked through leveraging the more robust PySpark library to unlock Apache Spark.

By the end of this course, you’ll be able to quickly and reliably process large amounts of data, even stored across multiple files, using PySpark.

From Pandas to PySpark DataFrame

# Overview
 In production-level environments, writing code that is easy for the entire team to understand and maintain is key. There are a few general guidelines for writing production-level code, as shown below:

* Use global scope as little as possible for variables, as this will reduce mutation bugs in the code.

* Add docstrings in the function definition. This helps people understand the code.

* Use type annotation or hint in the function arguments and the return value. This helps co-developers use your code as an API.

# Create production-level code

Let’s look at an example of creating production-level code, using the code we’ve written so far. First, we separate the general variables and functions to another file so that we can reuse the codes for other projects which use the same data source.



Learn to set up production-level code in PySpark and pandas.


Write Production Code

Learn to set up production-level code in PySpark and pandas.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Write Production Code

Overview