The complete guide to Pandas for beginners

When we talk about data science, we usually refer to the data analysis through summarization, visualizations, sophisticated algorithms that learn patterns in data (machine learning), and other fancy tools. When we discuss the term with software developers, we also hear a lot of Python, the popular programming language.

But why is Python so popular and special in the data science world? There are many reasons, and an important one is the Python ecosystem and libraries that make data science seem natural to Python.

One of these libraries is pandas , which every data science in the world uses, used, or at least heard of (if you are a data scientist who never used pandas, scream in comments).

Pandas is an essential part of the ecosystem that many other data science tools build on top or provide specific functionalities for pandas.

This guide introduces pandas for developers and aims to cover the what, why, and how of pandas’ most commonly used features.

Before we get started, if you want to access the full source code for this project to follow along, you can download the project’s source code from GitHub .

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