Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.
Table of Contents (13 chapters)

Dependency Management

In the IT world, most complex programs depend on libraries beyond the Python standard library. You may use numpy or pandas to deal with multidimensional data or matplotlib to visualize data in graphs (this will be covered in Chapter 10, Data Analytics with pandas and NumPy), or any number of other libraries available to Python developers.

Just like your own software, the libraries developed by other teams frequently change as bugs are fixed, features are added, and old code is removed or refactored, which is the process of restructuring existing code. That means it's important that your team uses the same version of a library so that it works in the same way for all of them.

Additionally, you want your customers or the servers where you deploy your software to use the same versions of the same libraries as well, so that everything works the same way on their computers, too.

There are multiple tools for solving this problem. These include pip, easy_install...