Using PySpark to read CSV files
As expected, PySpark provides native support for reading and writing CSV files. It also allows data engineers to pass diverse kinds of setups in case the CSV has a different type of delimiter, special encoding, and so on.
In this recipe, we are going to cover how to read CSV files using PySpark using the most common configurations, and we will explain why they are needed.
Getting ready
You can download the CSV dataset for this recipe from Kaggle: https://www.kaggle.com/datasets/jfreyberg/spotify-chart-data. We are going to use the same Spotify dataset as in Chapter 2.
As in the Creating a SparkSession for PySpark recipe, make sure PySpark is installed and running with the latest stable version. Also, using Jupyter Notebook is optional.
How to do it…
Let’s get started:
- We first import and create a SparkSession :
from pyspark.sql import spark = .builder \ .master("local...
