12 Most-Used Python Functions for Efficient Data Wrangling

The Ultimate Guide to “How To Simplify Your Data-Wrangling”

Photo by Hitesh Choudhary on Unsplash

In almost every data science job posting, you’ve likely seen this skill mentioned: “DATA WRANGLING.”

But what exactly is data wrangling?

Well, it’s something every data science professional does. In fact, 80% of their time is spent on data wrangling. It involves cleaning and transforming raw data into an enhanced format for better analysis.

How do we do it?

There are various ways, such as using Excel, SQL, or Python. It actually depends on personal preference. Personally, I prefer using Python for data wrangling tasks. So, if you’re like me,

Let’s dive into the 12 most fundamental Python functions that can simplify your data-wrangling process.

Data Wrangling in Python:

Data wrangling involves several key steps, such as:

1. Handling missing values,
2. Removing duplicates,
3. Correcting data inconsistencies, and
4. Structuring data for analysis.

But why is this so important?