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)

Data

Now that you have been introduced to NumPy and pandas, you will use them to analyze some real data.

Data scientists analyze data that exists in the cloud or online. One strategy is to download data directly to your computer.

Note

It is recommended to create a new folder to store all of your data. You can open your Jupyter Notebook in this same folder.

Downloading Data

Data comes in many formats, and pandas is equipped to handle most of them. In general, when looking for data to analyze, it's worth searching the keyword "dataset." A dataset is a collection of data. Online, "data" is everywhere, whereas datasets contain data in its raw format.

You will start by examining the famous Boston Housing dataset from 1980, which is available on our GitHub repository.

This dataset can be found here https://packt.live/31Cd96j.

You can begin by first downloading the dataset onto our system.

Downloading the Boston Housing Data from GitHub...