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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
2.8 (4)
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

2.8 (4)
By: Kats, Katz

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.
Table of Contents (26 chapters)
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1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Installing a package in editable mode

As we have mentioned, you can install a package from GitHub and it will behave the same as any other installed package—it can be upgraded or uninstalled.

Often, however, you will want to use a package while developing it. It would be hard to do both in the normal installation routine; you'd have to either update or re-install the package every time you made any developmental changes, just to reflect those changes. To get around this, there is a great feature that keeps the advantages of both worlds—your code is treated as a package but can be easily modified in place. This feature is called editable mode. Essentially, it means the folder on your filesystem is registered as a package, and so the imported package will always reflect all the changes that you've made.

In order to reap these benefits, you have to have a...

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