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Removing ticks with NullLocator
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Removing ticks with NullLocator
by Allen Yu, Claire Chung, Aldrin Yim
Matplotlib for Python Developers
Title Page
Copyright and Credits
Matplotlib for Python Developers Second Edition
Dedication
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Introduction to Matplotlib
What is Matplotlib?
Merits of Matplotlib
Easy to use
Diverse plot types
Hackable to the core (only when you want)
Open source and community support
What's new in Matplotlib 2.x?
Improved functionality and performance
Improved color conversion API and RGBA support
Improved image support
Faster text rendering
Change in the default animation codec
Changes in default styles
Matplotlib website and online documentation
Output formats and backends 
Static output formats
Raster images
Vector images
Setting up Matplotlib
Installing Python
Python installation for Windows 
Python installation for macOS
Python installation for Linux
Installing Matplotlib
About the dependencies
Installing the pip Python package manager
Installing Matplotlib with pip
Setting up Jupyter Notebook
Starting a Jupyter Notebook session
Running Jupyter Notebook on a remote server
Editing and running code
Manipulating notebook kernel and cells
Embed your Matplotlib plots
Documenting in Markdown
Save your hard work!
Summary
Getting Started with Matplotlib
Loading data
List
NumPy array
pandas DataFrame
Our first plots with Matplotlib
Importing the pyplot
Line plot
Scatter plot
Overlaying multiple data series in a plot
Multiline plots
Scatter plot to show clusters
Adding a trendline over a scatter plot
Adjusting axes, grids, labels, titles, and legends
Adjusting axis limits
Adding axis labels
Adding a grid
Titles and legends
Adding a title
Adding a legend
A complete example
Saving plots to a file
Setting the output format
Setting the figure resolution
Jupyter support
Interactive navigation toolbar
Configuring Matplotlib
Configuring within Python code
Reverting to default settings
Global setting via configuration rc file
Finding the rc configuration file
Editing the rc configuration file
Summary
Decorating Graphs with Plot Styles and Types
Controlling the colors
Default color cycle
Single-lettered abbreviations for basic colors
Standard HTML color names
RGB or RGBA color code
Hexadecimal color code
Depth of grayscale
Colormaps
Creating custom colormaps
Line and marker styles
Marker styles
Choosing the shape of markers
Using custom characters as markers
Adjusting marker sizes and colors
Fine-tuning marker styles with keyword arguments
Line styles
Color
Line thickness
Dash patterns
Designing a custom dash style
Cap styles
Spines
More native Matplotlib plot types
Choosing the right plot
Histogram
Bar plot
Setting bar plot properties
Drawing bar plots with error bars using multivariate data
Mean-and-error plots
Pie chart
Polar chart
Controlling radial and angular grids
Text and annotations
Adding text annotations
Font
Mathematical notations
Mathtext
LaTeX support
External text renderer
Arrows
Using style sheets
Applying a style sheet
Creating own style sheet
Resetting to default styles
Aesthetics and readability considerations in styling
Suitable font styles
Effective use of colors
Keeping it simple
Summary
Advanced Matplotlib
Drawing Subplots
Initiating a figure with plt.figure()
Initiating subplots as axes with plt.subplot()
Adding subplots with plt.figure.add_subplot()
Initiating an array of subplots with plt.subplots()
Shared axes
Setting the margin with plt.tight_layout()
Aligning subplots of different dimensions with plt.subplot2grid()
Drawing inset plots with fig.add_axes()
Adjusting subplot dimensions post hoc with plt.subplots_adjust
Adjusting axes and ticks
Customizing tick spacing with locators
Removing ticks with NullLocator
Locating ticks in multiples with MultipleLocator
Locators to display date and time
Customizing tick formats with formatters
Using a non-linear axis scale
More on Pandas-Matplotlib integration
Showing distribution with the KDE plot
Showing the density of bivariate data with hexbin plots
Expanding plot types with Seaborn 
Visualizing multivariate data with a heatmap
Showing hierarchy in multivariate data with clustermap
Image plotting
Financial plotting
3D plots with Axes3D
Geographical plotting
Basemap
GeoPandas
Summary
Embedding Matplotlib in GTK+3
Installing and setting up GTK+3
A brief introduction to GTK+3
Introduction to the GTK+3 signal system
Installing Glade
Designing the GUI using Glade
Summary
Embedding Matplotlib in Qt 5
A brief introduction to Qt 5 and PyQt 5
Differences between Qt 4 and PyQt 4
Introducing QT Creator / QT Designer
Summary
Embedding Matplotlib in wxWidgets Using wxPython
A brief introduction to wxWidgets and wxPython
Embedding Matplotlib in a GUI from wxGlade
Summary
Integrating Matplotlib with Web Applications
Installing Docker
Docker for Windows users
Docker for Mac users
More about Django
Django development in Docker containers
Starting a new Django site
Installation of Django dependencies
Django environment setup
Running the development server
Showing Bitcoin prices using Django and Matplotlib
Creating a Django app
Creating a simple Django view
Creating a Bitcoin candlestick view
Integrating more pricing indicators
Integrating the image into a Django template
Summary
Matplotlib in the Real World
Typical API data formats
CSV
JSON
Importing and visualizing data from a JSON API
Using Seaborn to simplify visualization tasks
Scraping information from websites
Matplotlib graphical backends
Non-interactive backends
Interactive backends
Creating animated plot
Summary
Integrating Data Visualization into the Workflow
Getting started
Visualizing sample images from the dataset
Importing the UCI ML handwritten digits dataset
Plotting sample images
Extracting one sample each of digits 0-9
Examining the randomness of the dataset
Plotting the 10 digits in subplots
Exploring the data nature by the t-SNE method
Understanding t-Distributed stochastic neighbor embedding 
Importing the t-SNE method from scikit-learn
Drawing a t-SNE plot for our data
Creating a CNN to recognize digits
Evaluating prediction results with visualizations
Examining the prediction performance for each digit 
Extracting falsely predicted images
Summary
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Customizing tick spacing with locators
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Locating ticks in multiples with MultipleLocator
Removing ticks with NullLocator
When
NullLocator
is used, ticks are removed from view.
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