Navigating the Data Landscape: A Comprehensive Guide to Data Cleaning with Python Pandas
Demystifying the Art of Data Preparation in Real-World ETL Projects
Machine learning and deep learning projects are becoming increasingly crucial for many organizations. The entire process involves data preparation, constructing an analytical model, and deploying it to production.
There are various techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming ingestion and data wrangling, etc. But how can you sort it all out?
In this article, we will be diving into data cleaning and how to work with data using Python Pandas.
At the end of this guide, we will get into a how-to demonstrating data cleaning with Pandas step by step in a real-world ETL project.
In this walkthrough, we will cover the following content:
Here’s the final source code of what we will be creating!
