Book Image

Numerical Computing with Python

By : Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou
Book Image

Numerical Computing with Python

By: Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou

Overview of this book

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: • Statistics for Machine Learning by Pratap Dangeti • Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim • Pandas Cookbook by Theodore Petrou
Table of Contents (21 chapters)
Title Page
Contributors
About Packt
Preface
Index

Scraping information from websites


Governments or jurisdictions around the world are increasingly embracing the importance of open data, which aims to increase citizen involvement and informs about decision making, making policies more open to public scrutiny. Some examples of open data initiatives around the world include data.gov (United States of America), data.gov.uk (United Kingdom), and data.gov.hk (Hong Kong).

These data portals often provide Application Programming Interfaces (APIs; see Chapter 7, Visualizing Online Data, for more details) for programmatic access to data. However, APIs are not available for some datasets; hence, we resort to good old web scraping techniques to extract information from websites.

BeautifulSoup (https://www.crummy.com/software/BeautifulSoup/) is an incredibly useful package used to scrape information from websites. Basically, everything marked with an HTML tag can be scraped with this wonderful package, from text, links, tables, and styles, to images...