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This section focuses on becoming familiar with general-purpose Python, making use of existing libraries, writing our first scripts, learning the basics of Git, and using the IDE. In this section, we will also lay the foundation for our projects, building pipelines to process (project 1), collect (project 2), and simulate (project 3) data.

This section comprises the following chapters:

Chapter 1, Preparing the Workspace
Chapter 2, First Steps in Coding – Variables and Data Types
Chapter 3, Functions
Chapter 4, Data Structures
Chapter 5, Loops and Other Compound Statements
Chapter 6, First Script – Geocoding with Web APIs
Chapter 7, Scraping Data from the Web with Beautiful Soup 4
Chapter 8, Classes and Inheritance
Chapter 9, Shell, Git, Conda, and More – at Your Command

Key benefits

Learn the basics of developing applications with Python and deploy your first data application

Take your first steps in Python programming by understanding and using data structures, variables, and loops

Delve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in Python

Description

Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.

What you will learn

Code in Python using Jupyter and VS Code Explore the basics of coding – loops, variables, functions, and classes Deploy continuous integration with Git, Bash, and DVC Get to grips with Pandas, NumPy, and scikit-learn Perform data visualization with Matplotlib, Altair, and Datashader Create a package out of your code using poetry and test it with PyTest Make your machine learning model accessible to anyone with the web API

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