Interactive Exploratory Data Analysis that Generates Python

A practical guide to interactive Exploratory Data Analysis on the Avocado dataset

Photo by Dirk Ribbler on Unsplash

Exploratory Data Analysis (EDA) is one of the first steps in the Data Science process — usually, it follows the data extraction. EDA helps us to get familiar with the data before we proceed with modeling or decide to repeat the extraction step.

EDA helps Data Scientists to:

• get familiar with the data
• find bugs in the data extraction process
• decide if the data needs cleaning
• decide what to do with the missing values if there are any
• visualize data distributions, etc.

With Exploratory Data Analysis we get the “feel” for the data

By reading this article you’ll get:

• A practical example of EDA in JupyterLab on Avocado dataset
• A code snippet of each interactive operation
• An efficient way to share your analysis with your colleagues

In case you’ve missed my previous articles about this topic, see Mito — A Spreadsheet that Generates Python.