Python for Data Science: A Hands-On Introduction

A Practical Starting Point for Python and Data

Python for Data Science: A Hands-On Introduction is a compact, example-driven book for readers who want to use Python to work with real data. Yuli Vasiliev builds the material from the ground up: first establishing what data is and how Python represents it, then moving into the structures, libraries, and workflows that turn raw numbers and text into useful analysis.

Published by No Starch Press in 2022, the book keeps its focus on doing. Rather than survey every corner of the Python ecosystem, it follows a clear sequence through the tasks that data work regularly demands.

From Data Structures to Data Science Libraries 💻

The early chapters cover Python data structures and the standard libraries that support data science. Readers see how lists, dictionaries, and related structures hold information, and how libraries extend Python’s reach into numerical work, data manipulation, and analysis. This foundation matters because data science code is rarely about a single clever line; it is about choosing the right representation and moving data through a reliable pipeline.

Files, APIs, and Databases

Data rarely appears in a neat, ready-to-use form. The book shows how to access data from files and APIs, then how to work with databases. That progression helps readers understand where data comes from, how to retrieve it, and how to prepare it for the questions they want to ask. For anyone who has only worked with toy datasets, these chapters provide a more realistic view of the data workflow.

Aggregating, Combining, and Visualizing Data 📊

Once data is accessible, the work turns to shaping it. Vasiliev covers aggregation and combining datasets, so readers can summarize large tables and join information from multiple sources. Visualization follows, giving shape to patterns and outliers. The book also addresses location data and time series, two areas where Python’s data tools are especially useful.

From Insight to Machine Learning

The later chapters move toward gaining insights from data and using machine learning for analysis. These sections extend the hands-on approach into predictive work, showing how the earlier skills in cleaning, combining, and exploring data feed into models. The result is a coherent path rather than a collection of disconnected techniques.

Who Will Get the Most from This Book

This is a strong fit for programmers who are new to data science, students who need a practical Python reference, and analysts who want to move beyond spreadsheets. It assumes some familiarity with Python syntax, but it does not assume prior data science experience. The chapters are designed to be worked through, with examples that invite readers to run code and inspect results.

Why It Belongs on Your Shelf

Python for Data Science avoids both math-heavy theory and shallow recipe collections. It offers a working introduction: enough explanation to understand the why, enough code to build the how. For readers who learn best by typing, testing, and iterating, this is a direct and useful guide to putting Python to work on data.