The structure of a Python package is defined by a few specifications (https://packaging.python.org/specifications/) and PEPs (short for Python Enhancement Proposals, such as PEP517—https://www.python.org/dev/peps/pep-0517/, PEP518—https://www.python.org/dev/peps/pep-0518/, and PEP427—https://www.python.org/dev/peps/pep-0427/), and the overall definition comes from the Python Packaging Authority (PyPA). In essence, a package is required to have, in addition to the actual code, a special file with metadata, including the package name, the description version, the tags, Python version support details, the authors, and the dependencies. This file could be a Python setup.py file—which was the standard solution for a long time—or a pyproject.toml file. The latter is a new, safer approach, but does not have as well-designed...
Learn Python by Building Data Science Applications
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Learn Python by Building Data Science Applications
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Overview of this book
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.
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Table of Contents (26 chapters)
Preface
Preparing the Workspace
First Steps in Coding - Variables and Data Types
Functions
Data Structures
Loops and Other Compound Statements
First Script – Geocoding with Web APIs
Scraping Data from the Web with Beautiful Soup 4
Simulation with Classes and Inheritance
Shell, Git, Conda, and More – at Your Command
Section 2: Hands-On with Data
Python for Data Applications
Data Cleaning and Manipulation
Data Exploration and Visualization
Training a Machine Learning Model
Improving Your Model – Pipelines and Experiments
Section 3: Moving to Production
Packaging and Testing with Poetry and PyTest
Data Pipelines with Luigi
Let's Build a Dashboard
Serving Models with a RESTful API
Serverless API Using Chalice
Best Practices and Python Performance
Assessments
Other Books You May Enjoy
Customer Reviews