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)

Parsing Command-Line Arguments in Scripts

Scripts often need input from their user in order to make certain choices about what the script does or how it runs. For instance, consider a script to train a deep learning network used for image classification. A user of this script will want to tell it where the training images are, what the labels are, and may want to choose what model to use, the learning rate, where to save the trained model configuration, and other features.

It's conventional to use command-line arguments; that is, values that the user supplies from their shell or from their own script when running your script. Using command-line arguments makes it easy to automate using the script in different ways and will be familiar to users who have experience of using the Unix or Windows command shells.

Python's standard library module for interpreting command-line arguments, argparse, supplies a host of features, making it easy to add argument handling to scripts...