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

Interactive backends


Matplotlib can build interactive figures that are far more engaging for readers. Sometimes, a plot might be overwhelmed with graphical elements, making it hard to discern individual data points. On other occasions, some data points may appear so similar that it becomes hard to spot the differences with the naked eye. An interactive plot can address these two scenarios by allowing us to zoom in, zoom out, pan, and explore the plot in the way we want.

Through the use of interactive backends, plots in Matplotlib can be embedded in Graphical User Interface (GUI) applications. By default, Matplotlib supports the pairing of the Agg raster graphics renderer with a wide variety of GUI toolkits, including wxWidgets (Wx), GIMP Toolkit (GTK+), Qt, and Tkinter (Tk). As Tkinter is the de facto standard GUI for Python, which is built on top of Tcl/Tk, we can create an interactive plot just by calling plt.show() in a standalone Python script.

Tkinter-based backend 

Let's try to copy the...