According to Forbes, data scientists and analysts spend 60% of their time on cleaning and organizing data. This shows how important data cleaning and data wrangling are in a project.
Data cleaning consists in cleaning the data we’ll use, so we don’t have incorrect, corrupted, duplicate, or incomplete data. Real-world data isn’t as clean as most datasets you work with on an online course. This makes data cleaning essential in real-world projects.
In this guide, we’ll review the most common pandas functions used in data analysis.