PySpark Data Cleansing Tutorial
🚀 PySpark Data Cleansing Tutorial – Step by Step
In this video, I’ll show you how to clean a messy dataset using PySpark. We’ll start with a raw CSV file full of missing values, duplicates, inconsistent formats, and outliers, and transform it into a clean dataset ready for analysis or machine learning.
✅ What You’ll Learn:
1. How to initialize a SparkSession
2. Cleaning messy column names
3. Handling missing values and placeholder text (NA, null, “-”)
4. Converting columns to the right data types (numbers, booleans, dates)
5. Deduplicating data using PySpark Window functions
6. Normalizing categorical values (like gender fields)
7. Detecting and handling outliers
8. Saving the clean dataset in Delta table format
This is a real-world PySpark example you can follow along with, perfect for beginners and intermediates who want to learn data engineering and big data cleaning techniques.
📂 Resources:
🔹 Example dataset (messy): dataset.csv
🔹 Clean PySpark script: https://github.com/smadathil700/Learn...
🔔 Don’t forget to:
👍 Like this video if it helps
💬 Comment with your questions
📌 Subscribe for more PySpark & Data Engineering tutorials
#PySpark #BigData #DataEngineering #ETL #DataCleaning #ApacheSpark