Book Image

Matplotlib for Python Developers - Second Edition

By : Aldrin Yim, Claire Chung, Allen Yu
Book Image

Matplotlib for Python Developers - Second Edition

By: Aldrin Yim, Claire Chung, Allen Yu

Overview of this book

Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you’ll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you’ll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations.
Table of Contents (16 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Importing and visualizing data from a JSON API


Now, let's learn how to parse financial data from Quandl's API to create insightful visualizations. Quandl is a financial and economic data warehouse, storing millions of datasets from hundreds of publishers. The best thing about Quandl is that these datasets are delivered via the unified API, without worrying about the procedures to parse the data correctly. Anonymous users can get up to 50 API calls per day, or up to 500 free API calls if registered. Readers can sign up for a free API key at https://www.quandl.com/?modal=register.

At Quandl, every dataset is identified by a unique ID, as defined by the Quandl code on each search result web page. For example, the Quandl code GOOG/NASDAQ_SWTX defines the historical NASDAQ index data published by Google Finance. Every dataset is available in three different formats—CSV, JSON, and XML.

Although an official Python client library is available from Quandl, we are not going to use that, for the sake...