Clean a CSV with Pandas

Project Description

In this project, you will clean a messy synthetic employee dataset using a structured, step-by-step workflow. The dataset includes encoding issues, wrong date formats, mixed types, and inconsistent categorical values.

The focus is on building a repeatable cleaning process, not just fixing one specific file.

Project Requirements

• Load the dataset and inspect it before doing anything

• Handle encoding and delimiter issues at load time

• Fix column data types explicitly using the dtype argument

• Convert date columns using pd.to_datetime() with errors='coerce'

• Standardize categorical columns (strip whitespace, fix capitalisation)

• Export a cleaned version of the dataset and do a final audit

Technologies to Use

• Python

• Pandas

• Jupyter Notebook

What You Will Learn

The data cleaning workflow I’ll be working with consists of 5 simple stages(Load, Inspect, Clean, Review, Export) that you can reuse on any dataset. You will also understand subtle issues like silent type casting and why checking the first few rows before loading a large file can save you a lot of time.

Want to See a Solution?

A full walkthrough of this project is available on Towards Data Science: 🔗 I Cleaned a Messy CSV File Using Pandas