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The 10 Most Useful Code Snippets for Data Wrangling in PySpark
Learn 10 introductory functions to leverage PySpark, a great framework for Data Wrangling.
Introduction
An API that I really enjoy working with is PySpark. Since I started using it daily for data wrangling, I found it very intuitive to handle, and there’s plenty of documentation online, especially when you work with platforms like Databricks.
PySpark is your trusty tool to get the job done when you’re dealing with big data. It brings the power of Spark’s distributed computing to Python, making it perfect for handling large datasets.
That said, in this article, we’re diving into 10 super useful code snippets that’ll make your data wrangling tasks in PySpark a whole lot easier. Whether you’re a seasoned data scientist or a budding data engineer, these snippets will become your go-to solutions for common data challenges.
Prerequisites
Before we jump in, make sure to use Google Colab, as it is the fastest way for you to code along and test these snippets. You will import the SparkSession and the pyspark.sql.functions.
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
# Creating a spark session (not needed if working in Databricks)
spark =…
