Introducing Jupyter and Pandas

This article is the first in a series that helps working developers get up to speed on data science tools and techniques. We'll start with a brief introduction to the series, and explain everything we're going to cover. Developers and data scientists working on data analysis and machine learning (ML) projects spend the majority of their time finding, cleaning, and organizing datasets. We'll do this by using Python, Pandas, and Seaborn in a Jupyter notebook to clean up a sample retail store's messy customer database. This seven-part series will take the initial round of messy data, clean it, and develop a set of visualizations that highlight our work. Here's what the series will cover: Before we start cleaning our dataset, let's take a quick look at two of the tools we'll use: Pandas and Jupyter Notebooks.