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)

Summary

You began our introduction to data analysis with NumPy, Python's incredibly fast library for handling massive matrix computations. Next, you learned about the fundamentals of pandas, Python's library for handling DataFrames. Taken together, you used NumPy and pandas to analyze the Boston Housing dataset, which included descriptive statistical methods and Matplotlib and Seaborn's graphical libraries. Along the way, you learned about fundamental statistical concepts, including the mean, standard deviation, median, quartiles, correlation, skewed data, and outliers. You also learned about advanced methods for creating clean, clearly labeled, publishable graphs.

In Chapter 11, Machine Learning, you will come across interesting machine learning concepts such as regression, different types of classifications, decision trees. You will use Python to build efficient machine learning models and predict new results.