Polar Charts in Matplotlib

Curriculum

173 Sections
961 Lessons
10 Weeks

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Matplotlib home
7
- 1.1
  What Is Matplotlib?
- 1.2
  Matplotlib and Pyplot
- 1.3
  Online Editor
- 1.4
  Applications of Matplotlib
- 1.5
  Why To Learn Matplotlib?
- 1.6
  Who Should Learn Matplotlib?
- 1.7
  Prerequisites To Learn Matplotlib
Matplotlib - Ellipse Selector
4
- 2.1
  Introduction
- 2.2
  Key Features of aImplementation Approachesn Ellipse Selector
- 2.3
  Implementation Approaches
- 2.4
  Use Cases and Considerations
Matplotlib - Polar Charts
7
- 3.1
  overview
- 3.2
  Polar Charts in Matplotlib
- 3.3
  The polar() Function
- 3.4
  Basic Polar Plot with Sine Function
- 3.5
  Polar Scatter Plot
- 3.6
  Multiple Data Series in Polar Plot
- 3.7
  Rose Plot
Frequently Asked Questions about Matplotlib
There are some very Frequently Asked Questions(FAQ) about SQL, this section tries to answer them briefly.
16
- 4.1
  What is Matplotlib used for?
- 4.2
  Why Can Matplotlib Be Confusing?
- 4.3
  Why is it called Matplotlib?
- 4.4
  Why is Matplotlib so Popular?
- 4.5
  Why is Matplotlib helpful?
- 4.6
  What are the advantages of Matplotlib?
- 4.7
  Who uses Matplotlib?
- 4.8
  Who invented Matplotlib?
- 4.9
  How to Learn Matplotlib?
- 4.10
  Is Matplotlib a data visualization?
- 4.11
  What are the two approaches to Matplotlib?
- 4.12
  What are the benefits of Matplotlib?
- 4.13
  What type of histogram is Matplotlib?
- 4.14
  Which is the best place to learn Matplotlib?
- 4.15
  What are the 3 layers of Matplotlib architecture?
- 4.16
  What is Matplotlib font?
Matplotlib - Introduction
10
- 5.1
  overview
- 5.2
  Components of Matplotlib
- 5.3
  Axes/Subplot
- 5.4
  Axis
- 5.5
  Artist
- 5.6
  Key Features
- 5.7
  Types of Plots
- 5.8
  Subplots
- 5.9
  Subplots
- 5.10
  Saving Plots
Matplotlib VS Seaborn
11
- 6.1
  overview
- 6.2
  What is Matplotlib?
- 6.3
  Core Library
- 6.4
  Customization
- 6.5
  Basic Plotting
- 6.6
  Simple plot by matplotlib
- 6.7
  What is Seaborn?
- 6.8
  Statistical Data Visualization
- 6.9
  Default Aesthetics
- 6.10
  Specialized Plots
- 6.11
  Basic seaborn plot
Matplotlib - Environment Setup
5
- 7.1
  overview
- 7.2
  Installation
- 7.3
  Checking the Installation
- 7.4
  Backend Selection
- 7.5
  Configuration and Style
Matplotlib - Anaconda Distribution
4
- 8.1
  overview
- 8.2
  What is Anaconda Distribution?
- 8.3
  Matplotlib in Anaconda
- 8.4
  Installing or Updating Matplotlib in Anaconda
Matplotlib - Jupyter Notebook
6
- 9.1
  overview
- 9.2
  Starting Jupyter Notebook
- 9.3
  Launch Jupyter Notebook
- 9.4
  Interact with Plots
- 9.5
  Closing Jupyter Notebook
- 9.6
  Hide Matplotlib descriptions in Jupyter notebook
Matplotlib - Pyplot API
5
- 10.1
  overview
- 10.2
  Types of Plots
- 10.3
  Image Functions
- 10.4
  Axis Functions
- 10.5
  Figure Functions
Matplotlib - Simple Plot
1
- 11.1
  overview
Matplotlib - Saving Figures
3
- 12.1
  overview
- 12.2
  Common File Formats for Saving
- 12.3
  Note
Matplotlib - Markers
4
- 13.1
  overview
- 13.2
  Syntax
- 13.3
  Scatterplot with pentagonal marker
- 13.4
  Line plot with triangular marker
Matplotlib - Figures
5
- 14.1
  overview
- 14.2
  Syntax
- 14.3
  Creating a Figure
- 14.4
  Adding Plots to a Figure
- 14.5
  Displaying and Customizing Figures
Matplotlib - Styles
8
- 15.1
  What is Style in Matplotlib?
- 15.2
  Built-in Styles
- 15.3
  Changing Styles
- 15.4
  Key Aspects of Matplotlib Styles
- 15.5
  Using Styles
- 15.6
  Setting a Style
- 15.7
  Available Styles
- 15.8
  Applying Custom Styles
Matplotlib - Legends
5
- 16.1
  overview
- 16.2
  Adding legend to a matplotlib plot
- 16.3
  Manually adding the legend
- 16.4
  Adding legend in subplots
- 16.5
  Adding multiple legends in one axes
Matplotlib - Colors
9
- 17.1
  overview
- 17.2
  Color Representation Formats in Matplotlib
- 17.3
  The RGB or RGBA Tuple format
- 17.4
  The Hex RGB or RGBA String format
- 17.5
  The Gray Level String format
- 17.6
  The “Cn” Color notation
- 17.7
  The Single Letter String format
- 17.8
  Other formats
- 17.9
  Darken or lighten a color
Matplotlib - Colormaps
6
- 18.1
  overview
- 18.2
  Colormaps in Matplotlib
- 18.3
  Accessing Colormaps and their Values
- 18.4
  Creating and Manipulating Colormaps
- 18.5
  Changing the default colormap
- 18.6
  Plotting Lines with Colors through Colormap
Matplotlib - Colormap Normalization
2
- 19.1
  overview
- 19.2
  Colormap Normalization in Matplotlib
Matplotlib - Choosing Colormaps
3
- 20.1
  overview
- 20.2
  Choosing Colormaps in Matplotlib
- 20.3
  Categories of Colormaps
Matplotlib - Colorbars
5
- 21.1
  overview
- 21.2
  Colorbars in Matplotlib
- 21.3
  Automatic Colorbar Placement
- 21.4
  Manual Colorbar Placement
- 21.5
  Customizing Colorbars
Matplotlib - Working With Text
1
- 22.1
  overview
Matplotlib - Text properties
6
- 23.1
  overview
- 23.2
  Text layout and positioning properties
- 23.3
  Text color and transparency properties
- 23.4
  Different font properties
- 23.5
  The bounding box and Clipping Properties
- 23.6
  Other Properties
Matplotlib - Subplot Titles
3
- 24.1
  What is Subplot Title?
- 24.2
  Purpose of Subplot title
- 24.3
  Importance of Subplot title
Matplotlib - Images
2
- 25.1
  What are Images in Matplotlib?
- 25.2
  Use Cases for Images in Matplotlib
Matplotlib - Image Masking
3
- 26.1
  overview
- 26.2
  Process of Image Masking
- 26.3
  Types of Image Masks
Matplotlib - Annotations
2
- 27.1
  overview
- 27.2
  Key Aspects of Annotations in Matplotlib
Matplotlib - Arrows
1
- 28.1
  What are Arrows in Matplotlib?
Matplotlib - Fonts
19
- 29.1
  overview
- 29.2
  Key Aspects of Fonts in Matplotlib library
- 29.3
  Controlling Fonts in Matplotlib
- 29.4
  Importance of Fonts in Visualization
- 29.5
  Setting Font Properties Globally
- 29.6
  Common Font-related Functions in Matplotlib
- 29.7
  Controlling Font Properties
- 29.8
  Changing Font Family
- 29.9
  Setting Font Properties Globally
- 29.10
  plt.rcParams[‘font.family’] = font_name
- 29.11
  plt.rcParams[‘font.size’] = font_size
- 29.12
  plt.rcParams[‘font.style’] = ‘fontstyle’
- 29.13
  plt.rcParams[‘font.weight’] = font_weight
- 29.14
  Change the font properties of a Matplotlib colorbar label
- 29.15
  Setting the size of the plotting canvas
- 29.16
  Auto adjust font size in Seaborn heatmap using Matplotlib
- 29.17
  Set the font size of Matplotlib axis Legend
- 29.18
  Modify the font size in Matplotlib-venn
- 29.19
  Change xticks font size
Matplotlib - What are Fonts?
6
- 30.1
  overview
- 30.2
  Key Aspects of Fonts in Matplotlib library
- 30.3
  Controlling Fonts in Matplotlib
- 30.4
  Importance of Fonts in Visualization
- 30.5
  Setting Font Properties Globally
- 30.6
  Common Font-related Functions in Matplotlib
Matplotlib - Setting Font Properties Globally
11
- 31.1
  overview
- 31.2
  plt.rcParams[‘font.family’] = font_name
- 31.3
  plt.rcParams[‘font.size’] = font_size
- 31.4
  plt.rcParams[‘font.style’] = ‘fontstyle’
- 31.5
  plt.rcParams[‘font.weight’] = font_weight
- 31.6
  Change the font properties of a Matplotlib colorbar label
- 31.7
  Setting the size of the plotting canvas
- 31.8
  Auto adjust font size in Seaborn heatmap using Matplotlib
- 31.9
  Set the font size of Matplotlib axis Legend
- 31.10
  Modify the font size in Matplotlib-venn
- 31.11
  Change xticks font size
Matplotlib - Font Indexing
6
- 32.1
  overview
- 32.2
  Font Indexing in Matplotlib
- 32.3
  Accessing Fonts by Name
- 32.4
  Available Fonts on the System
- 32.5
  Accessing Fonts by Indexing
- 32.6
  Note
Matplotlib - Font Properties
5
- 33.1
  What are Font properties?
- 33.2
  Key Font Properties in Matplotlib
- 33.3
  Setting Font Properties in Matplotlib
- 33.4
  Importance of Font Properties
- 33.5
  Using Font Properties for Customization
Matplotlib - Scales
5
- 34.1
  What are Scales in Matplotlib?
- 34.2
  Linear Scale
- 34.3
  Logarithmic Scale
- 34.4
  Symmetrical Logarithmic Scale
- 34.5
  Logit Scale
Matplotlib - Linear and Logarthmic Scales
3
- 35.1
  What are Scales?
- 35.2
  Linear Scale
- 35.3
  Logarithmic Scale
Matplotlib - Symmetrical Logarithmic and Logit Scales
2
- 36.1
  Symmetrical Logarithmic Scale
- 36.2
  Logit Scale
Matplotlib - Latex
24
- 37.1
  What is LaTeX?
- 37.2
  Key Aspects of LaTeX
- 37.3
  Advantages of LaTeX
- 37.4
  Disadvantages of LaTeX
- 37.5
  Usage of LaTeX
- 37.6
  Basic document structure of the LaTex
- 37.7
  Writing our own LaTeX preamble
- 37.8
  Rendering mathematical expressions
- 37.9
  LaTeX for Mathematical Expressions
- 37.10
  What is Text formatting in LaTex?
- 37.11
  LaTeX Formatting in Annotations Includes
- 37.12
  LaTeX Text Formatting in Annotations
- 37.13
  Annotations with Bold text using LaTex
- 37.14
  Mathematical Notation
- 37.15
  Subscripts and Superscripts
- 37.16
  Combining Text and Math
- 37.17
  Combining Text and Math using Latex on a plot
- 37.18
  Text Color and Font Styles
- 37.19
  Combined Usage of text and font styles on annotations
- 37.20
  What is LaTex Rendering?
- 37.21
  Key Aspects of LaTeX Rendering
- 37.22
  Benefits of LaTeX Rendering
- 37.23
  Enabling Latex Rendering
- 37.24
  LaTeX Rendering in Matplotlib for Annotations
Matplotlib - What is LaTeX?
6
- 38.1
  overview
- 38.2
  Key Aspects of LaTeX
- 38.3
  Advantages of LaTeX
- 38.4
  Disadvantages of LaTeX
- 38.5
  Usage of LaTeX
- 38.6
  Basic document structure of the LaTex
Matplotlib - LaTeX for Mathematical Expressions
3
- 39.1
  What is Rendering mathematical expressions?
- 39.2
  Importance of LaTeX for Mathematics
- 39.3
  LaTeX for Mathematical Expressions
Matplotlib - LaTeX Text Formatting in Annotations
14
- 40.1
  What is Text formatting in LaTex?
- 40.2
  LaTeX Formatting in Annotations Includes
- 40.3
  LaTeX Text Formatting in Annotations
- 40.4
  Annotations with Bold text using LaTex
- 40.5
  Mathematical Notation
- 40.6
  Subscripts and Superscripts
- 40.7
  Combining Text and Math
- 40.8
  Combining Text and Math using Latex on a plot
- 40.9
  Text Color and Font Styles
- 40.10
  Combined Usage of text and font styles on annotations
- 40.11
  Important Points to be noted
- 40.12
  Bold font weight for LaTeX axes label
- 40.13
  Format a float using matplotlib’s LaTeX formatter
- 40.14
  Obtain the same font in Matplotlib output as in LaTex output
Matplotlib - PostScript
4
- 41.1
  overview
- 41.2
  PostScript in Matplotlib
- 41.3
  Create a PostScript file
- 41.4
  Customizing PostScript Output
Matplotlib - Mathematical Expressions
7
- 42.1
  overview
- 42.2
  Mathematical Expressions in Matplotlib
- 42.3
  Mathtext in Matplotlib
- 42.4
  Basic Mathematical Text
- 42.5
  Radical, Greek Letters, and Delimiters
- 42.6
  Fractions, Binomials, and Stacked Numbers
- 42.7
  Subscripts, Superscripts and Standard Function Names
Matplotlib - Enabling LaTex Rendering in Annotations
4
- 43.1
  What is LaTex Rendering?
- 43.2
  Key Aspects of LaTeX Rendering
- 43.3
  Benefits of LaTeX Rendering
- 43.4
  Enabling Latex Rendering
Matplotlib - Animations
4
- 44.1
  overview
- 44.2
  Animations in Matplotlib
- 44.3
  Creating Animations
- 44.4
  Saving animations
Matplotlib - Artists
7
- 45.1
  Understanding Artists in Matplotlib
- 45.2
  Artist Hierarchy
- 45.3
  Creating and Accessing the Artists
- 45.4
  Modifying the Artists Properties
- 45.5
  Manipulating Artist Data
- 45.6
  Manual Addition of Artists
- 45.7
  Removing Artists
Matplotlib - Styling with Cycler
4
- 46.1
  overview
- 46.2
  Cycling Through Multiple Properties
- 46.3
  Setting Default Property Cycle (rc parameter)
- 46.4
  Setting Property Cycle for a Single Pair of Axes
Matplotlib - Paths
6
- 47.1
  overview
- 47.2
  Paths in Matplotlib
- 47.3
  Straight Line Path
- 47.4
  Circle Path
- 47.5
  Bezier Curve Path
- 47.6
  Polygon Path
Matplotlib - Path Effects
6
- 48.1
  overview
- 48.2
  Path Effects in Matplotlib
- 48.3
  Simple Line with Shadow Path Effect
- 48.4
  Dashed Line with Outline Path Effect
- 48.5
  Bold Outlined Scatter Plot Path Effect
- 48.6
  Combined Path Effects
Matplotlib - Transforms
2
- 49.1
  overview
- 49.2
  Transforms in Matplotlib
Matplotlib - Setting Ticks and Tick Labels
1
- 50.1
  overview
Matplotlib - Radian Ticks
5
- 51.1
  overview
- 51.2
  Radian Ticks in Matplotlib
- 51.3
  Radian Ticks for the x axis
- 51.4
  Radian Ticks for the y axis
- 51.5
  Custom Package for Radian Ticks
Matplotlib - Dateticks
5
- 52.1
  overview
- 52.2
  Dateticks in Matplotlib
- 52.3
  Basic Dateticks with DefaultFormatter
- 52.4
  Customizing Dateticks with DateFormatter
- 52.5
  Advanced Formatting with ConciseDateFormatter
Matplotlib - Tick Formatters
5
- 53.1
  Understanding Ticks and Tick Labels
- 53.2
  Tick Formatters in Matplotlib
- 53.3
  String Formatting
- 53.4
  Function Based Formatting
- 53.5
  Formatter Object Formatting
Matplotlib - Tick Locators
10
- 54.1
  overview
- 54.2
  Tick Locators in Matplotlib
- 54.3
  Auto Locator
- 54.4
  Null Locator
- 54.5
  Multiple Locator
- 54.6
  Fixed Locator
- 54.7
  Linear Locator
- 54.8
  Index Locator
- 54.9
  MaxN Locator
- 54.10
  Log Locator
Matplotlib - Basic Units
2
- 55.1
  What are Basic Units?
- 55.2
  Common Basic Units in Matplotlib
Matplotlib - Autoscaling
2
- 56.1
  What is Autoscaling?
- 56.2
  Key Aspects of Autoscaling in Matplotlib
Matplotlib - Reverse Axes
4
- 57.1
  What is Reverse Axes?
- 57.2
  Reversing X-axis
- 57.3
  Steps to Reverse the X-axis
- 57.4
  Use Cases for Reversing the X-axis
Matplotlib - Logarithmic Axes
4
- 58.1
  What is Logarithmic axes?
- 58.2
  What is Linear Axes?
- 58.3
  Linear vs. Logarithmic Scales
- 58.4
  Types of Logarithmic Scales
Matplotlib - Symlog
7
- 59.1
  What is Symlog?
- 59.2
  Characteristics of Symlog Scale
- 59.3
  The use cases of Linear near zero
- 59.4
  Symmetric logarithmic scaling
- 59.5
  Characteristics of Symlog Scaling
- 59.6
  Use Cases
- 59.7
  Logarithmic away from zero
Matplotlib - Unit Handling
4
- 60.1
  What is Unit Handling?
- 60.2
  Key Aspects of Unit Handling in Matplotlib
- 60.3
  Functions and Techniques for Unit Handling
- 60.4
  Use Cases for Unit Handling
Matplotlib - Ellipse with Units
6
- 61.1
  overview
- 61.2
  Ellipse with Units in Matplotlib
- 61.3
  Fixed Size Ellipse in Data Coordinates
- 61.4
  Ellipse with Variable Size in Axes Coordinates
- 61.5
  Ellipse Centered at Figure Origin
- 61.6
  Rotated Ellipse with Custom Angle
Matplotlib - Spines
4
- 62.1
  What are Spines?
- 62.2
  Key Characteristics of Spines
- 62.3
  Uses of Spines
- 62.4
  Types of spines
Matplotlib - Axis Ranges
4
- 63.1
  What is Axis Range?
- 63.2
  The Key Points about Axis Ranges
- 63.3
  Use Cases for Axis Ranges
- 63.4
  Key Concepts in Axis Range
Matplotlib - Axis Scales
3
- 64.1
  What is Axis Scale?
- 64.2
  Use Cases for Different Scales
- 64.3
  Implementing Different Scales
Matplotlib - Axis Ticks
21
- 65.1
  What are Axis Ticks?
- 65.2
  Key Concepts in Axis ticks
- 65.3
  Customizing Axis Ticks
- 65.4
  Setting Ticks
- 65.5
  Tick Formatting
- 65.6
  Tick Frequency and Appearance
- 65.7
  Minor Ticks
- 65.8
  Use Cases
- 65.9
  Adding extra axis ticks
- 65.10
  Customize X-axis ticks
- 65.11
  Remove the X-axis ticks while keeping the grids
- 65.12
  Turn off the ticks and marks
- 65.13
  Remove the digits after the decimal point in axis ticks
- 65.14
  Add Matplotlib Colorbar Ticks
- 65.15
  Adjusting gridlines and ticks in Matplotlib imshow
- 65.16
  Change the spacing between ticks
- 65.17
  Move the Y-axis ticks from the left side of the plot to the right side
- 65.18
  Add third level of ticks
- 65.19
  Create minor ticks for a polar plot
- 65.20
  Overlapping Y-axis tick label and X-axis tick label
- 65.21
  Disable the minor ticks of a log-plot
Matplotlib - Formatting Axes
6
- 66.1
  What is Formatting Axes?
- 66.2
  Axes Formatting Options
- 66.3
  Ticks and Tick Labels
- 66.4
  Axis Limits and Scale
- 66.5
  Axis Labels and Titles
- 66.6
  Grid Lines and Spines
Matplotlib - Axes Class
6
- 67.1
  overview
- 67.2
  Axes class in matplotlib
- 67.3
  Changing an Axes properties
- 67.4
  Plotting on an Axes
- 67.5
  Customizing the axes Data
- 67.6
  Clearing the Axes
Matplotlib - Twin Axes
1
- 68.1
  overview
Matplotlib - Figure Class
5
- 69.1
  overview
- 69.2
  Figure Class in Matplotlb
- 69.3
  Creating a Figure with Grids of Subplots
- 69.4
  Saving a Figure
- 69.5
  Saving a Figure
Matplotlib - Multiplots
1
- 70.1
  overview
Matplotlib - Grids
8
- 71.1
  overview
- 71.2
  Introduction to Grids in Matplotlib
- 71.3
  Basic Plot with Grids
- 71.4
  Customizing Grid
- 71.5
  Adding Minor Gridlines
- 71.6
  Manually adding the grids
- 71.7
  Hiding the gridlines
- 71.8
  Gridlines Across The Subplots
Matplotlib - Object-oriented Interface
1
- 72.1
  overview
Matplotlib - PyLab module
2
- 73.1
  overviw
- 73.2
  Basic Plotting
Matplotlib - Subplots() Function
1
- 74.1
  overview
Matplotlib - Subplot2grid() Function
1
- 75.1
  overview
Matplotlib - Anchored Artists
2
- 76.1
  overview
- 76.2
  Anchored Artists in Matplotlib
Matplotlib - Manual Contour
5
- 77.1
  overview
- 77.2
  Manual Contour in Matplotlib
- 77.3
  Basic Manual Contour Plot
- 77.4
  Manual Contour Plot with Labeling
- 77.5
  Manual Contour Plot with Filled Regions
Matplotlib - Coords Report
6
- 78.1
  overview
- 78.2
  Coords Report in Matplotlib
- 78.3
  Scatter Plot Coord Report
- 78.4
  Line Plot Coord Report
- 78.5
  Bar Plot Coord Report
- 78.6
  Pie Chart Coord Report
Matplotlib - AGG filter
5
- 79.1
  overview
- 79.2
  AGG filter in Matplotlib
- 79.3
  Image Smoothing with AGG Filter
- 79.4
  Sharpening Image with AGG Filter
- 79.5
  Embossing Image with AGG Filter
Matplotlib - Ribbon Box
6
- 80.1
  overview
- 80.2
  Ribbon Box in Matplotlib
- 80.3
  Ribbon Box Plot with Confidence Interval
- 80.4
  Multiple Ribbon Box Plots
- 80.5
  Stacked Ribbon Box Plot
- 80.6
  Horizontal Ribbon Box Plot
Matplotlib - Fill Spiral
3
- 81.1
  overview
- 81.2
  Creating a Basic Fill Spiral
- 81.3
  Creating the Logarithmic Fill Spiral
Matplotlib - Findobj Demo
3
- 82.1
  overview
- 82.2
  Finding object
- 82.3
  Modifying Objects
Matplotlib - Hyperlinks
4
- 83.1
  overview
- 83.2
  Creating SVG figures with hyperlinks
- 83.3
  Adding Hyperlinks in PdfPages
- 83.4
  Hyperlinks in Interactive Plots
Matplotlib - Image Thumbnail
4
- 84.1
  overview
- 84.2
  Image thumbnail in Matplotlib
- 84.3
  Generating Thumbnails for a single image
- 84.4
  Generating Thumbnails for multiple Images
Matplotlib - Plotting with Keywords
6
- 85.1
  overview
- 85.2
  Plotting with Keywords in Matplotlib
- 85.3
  Plotting with Keyword “Color”
- 85.4
  Plotting with Keyword “marker”
- 85.5
  Plotting with Keyword “linestyle”
- 85.6
  Plotting with Keyword “grid”
Matplotlib - Create Logo
6
- 86.1
  overview
- 86.2
  Create Logo in Matplotlib
- 86.3
  Simple Geometric Logo
- 86.4
  Creating a Text Logo
- 86.5
  Creating a Complex Shape Logo
- 86.6
  Creating an Image Logo
Matplotlib - Multipage PDF
3
- 87.1
  overview
- 87.2
  Creating a Basic multipage PDF
- 87.3
  Adding Metadata and Annotations
Matplotlib - Multiprocessing
4
- 88.1
  overview
- 88.2
  Multiprocessing in Matplotlib
- 88.3
  Creating Multiple Matplotlib Plots
- 88.4
  Saving Multiple Matplotlib Figures
Matplotlib - Print Stdout
6
- 89.1
  overview
- 89.2
  Print Stdout in Matplotlib
- 89.3
  Print Text on Plot
- 89.4
  Print Text on Polar Plot
- 89.5
  Print Text with Rotation
- 89.6
  Print Text with Box
Matplotlib - Compound Path
6
- 90.1
  overview
- 90.2
  Compound Path in Matplotlib
- 90.3
  Compound Path: Intersection of Circle and Rectangle
- 90.4
  Compound Path of Union of Two Rectangles
- 90.5
  Compound Path: Difference of Two Circles
- 90.6
  Compound Path: Exclusive XOR of Two Polygons
Matplotlib - Sankey Class
5
- 91.1
  overview
- 91.2
  Key Components of a Sankey Diagram
- 91.3
  Sankey Class in Matplotlib
- 91.4
  Drawing a Basic Sankey Diagram
- 91.5
  Customized Sankey Diagram
Matplotlib - MRI with EEG
5
- 92.1
  overview
- 92.2
  MRI with EEG in Matplotlib
- 92.3
  Displaying MRI Slice
- 92.4
  Plot EEG Signal
- 92.5
  MRI Overlay with EEG Electrodes
Matplotlib - Stylesheets
5
- 93.1
  What are StyleSheets?
- 93.2
  Viewing Available Stylesheets
- 93.3
  Using Stylesheets
- 93.4
  Applying Stylesheet Temporarily
- 93.5
  Creating Custom Stylesheets
Matplotlib - Background Colors
4
- 94.1
  What are Background Colors in Matplotlib?
- 94.2
  Figure and Plot Background Color
- 94.3
  Stylesheets for Background Colors
- 94.4
  Importance of Background Colors
Matplotlib - Basemap
4
- 95.1
  What is Basemap?
- 95.2
  Installing Basemap
- 95.3
  Basic Workflow with Basemap
- 95.4
  Applications of Matplotlib Basemap
Matplotlib - Event Handling
7
- 96.1
  overview
- 96.2
  overview
- 96.3
  Event Handling in Matplotlib
- 96.4
  Event Handling in Matplotlib
- 96.5
  Connecting to Events
- 96.6
  Common Events in Matplotlib
- 96.7
  Event Attributes
Matplotlib - Close Event
4
- 97.1
  overview
- 97.2
  Close Events in Matplotlib
- 97.3
  Detecting Closed Axes
- 97.4
  Continuing Code After Closing
Matplotlib - Mouse Move
4
- 98.1
  overview
- 98.2
  Mouse Move in Matplotlib
- 98.3
  Real-Time Plotting of Mouse Movement
- 98.4
  Highlighting Triangles with TriFinder
Matplotlib - Click Events
4
- 99.1
  overview
- 99.2
  Click Events in Matplotlib
- 99.3
  Storing Click Event Coordinates
- 99.4
  Click Event with Checkbox
Matplotlib - Scroll Event
4
- 100.1
  overview
- 100.2
  Scroll event in Matplotlib
- 100.3
  Zooming with Scroll Event
- 100.4
  Interactive Scrolling through Images
Matplotlib - Keypress Event
3
- 101.1
  overview
- 101.2
  Example
- 101.3
  Creating a Custom Keypress Event Function
Matplotlib - Pick Event
6
- 102.1
  overview
- 102.2
  Pick Event in Matplotlib
- 102.3
  Picking the Points, Rectangles, and Text
- 102.4
  Picking on a Scatter Plot
- 102.5
  Images Picking
- 102.6
  Legend Picking
Matplotlib - Looking Glass
2
- 103.1
  overview
- 103.2
  Looking Glass in Matplotlib
Matplotlib - Path Editor
2
- 104.1
  overview
- 104.2
  Step by Step implementation
Matplotlib - Poly Editor
5
- 105.1
  overview
- 105.2
  Creating the Polygon Interactor Class
- 105.3
  Initializing the Polygon Editor
- 105.4
  Defining Utility Function
- 105.5
  Running the Poly Editor
Matplotlib - Timers
4
- 106.1
  overview
- 106.2
  Timers in Matplotlib
- 106.3
  Timer for Real-Time Updates
- 106.4
  Animated Brownian Walk with Timer
Matplotlib - Viewlims
4
- 107.1
  overview
- 107.2
  Creating an Interactive Zoom Plot
- 107.3
  Updating View Limits
- 107.4
  Connecting Callbacks
Matplotlib - Zoom Window
2
- 108.1
  overview
- 108.2
  Zoom Window in Matplotlib
Matplotlib - Cursor Widget
2
- 109.1
  What is Cursor Widget?
- 109.2
  Basic Workflow with Cursor Widget
Matplotlib - Annotated Cursor
4
- 110.1
  What is Annotated Cursor?
- 110.2
  Annotated Cursor Features
- 110.3
  Annotated Cursor in Matplotlib
- 110.4
  Use Cases
Matplotlib - Buttons Widget
2
- 111.1
  What is Buttons Widget?
- 111.2
  Basic Usage of Button Widget
Matplotlib - Check Buttons
3
- 112.1
  What is Check Buttons?
- 112.2
  Creating Check Buttons
- 112.3
  Customization and Interaction
Matplolib - Lasso Selector
2
- 113.1
  What is Lasso Selector?
- 113.2
  Key Features and Components of the Lasso selector
Matplotlib - Menu Widget
5
- 114.1
  Introduction
- 114.2
  Overview of Creating a Menu in Matplotlib with Tkinter
- 114.3
  Steps for creating the Menu widget using Tkinter
- 114.4
  Use Cases
- 114.5
  Customization
Matplotlib - Mouse Cursor
5
- 115.1
  Introduction
- 115.2
  Handling Mouse Events in Matplotlib
- 115.3
  Retrieving Cursor Coordinates
- 115.4
  Creating Custom Mouse Cursors
- 115.5
  Use Cases
Matplotlib - Multicursor
7
- 116.1
  Introduction
- 116.2
  Features of MultiCursor
- 116.3
  Basic Cursor Example
- 116.4
  Using mplcursors for Multicursor
- 116.5
  Usage of mplcursors
- 116.6
  Extending to Multi-Cursor
- 116.7
  Use Cases for Multicursor
Matplotlib - Polygon Selector
5
- 117.1
  Introduction
- 117.2
  Key Concepts
- 117.3
  Use Cases and Extensions
- 117.4
  Customization
- 117.5
  Implementation Steps
Matplotlib - Radio Buttons
5
- 118.1
  Introduction
- 118.2
  Key Features
- 118.3
  Implementation of the Radiobuttons on a plot
- 118.4
  Use Cases
- 118.5
  Customization
Matplotlib - RangeSlider
5
- 119.1
  introduction
- 119.2
  Key Features of a RangeSlider
- 119.3
  Conceptual Implementation
- 119.4
  Dynamic selection
- 119.5
  Use Cases
Matplotlib - Rectangle Selector
6
- 120.1
  Introduction
- 120.2
  Key Concepts of a Rectangle Selector
- 120.3
  Use Cases
- 120.4
  Implementation Steps
- 120.5
  Example
- 120.6
  Considerations for Implementation
Matplotlib - Slider Widget
5
- 121.1
  Introduction
- 121.2
  Key Features
- 121.3
  Implementation
- 121.4
  Use Cases
- 121.5
  Customization
Matplotlib - Span Selector
4
- 122.1
  Introduction
- 122.2
  Key Features of SpanSelector
- 122.3
  Implementation of SpanSelector
- 122.4
  Use Cases and Considerations
Matplotlib - Textbox
6
- 123.1
  overview
- 123.2
  Textbox in Matplotlib
- 123.3
  Basic Text Box
- 123.4
  Rounded Text Box
- 123.5
  Custom Text Box with Arrow
- 123.6
  Multiline Text Box
Matplotlib - Line Plots
7
- 124.1
  overview
- 124.2
  Line Plots in Matplotlib
- 124.3
  The Plot() Function
- 124.4
  Creating a Basic Line Plot
- 124.5
  Line plot with Multiple lines
- 124.6
  Creating Customized Line Plot
- 124.7
  Line plot with customized axes limits
Matplotlib - Area Plots
7
- 125.1
  introduction
- 125.2
  Area Plots in Matplotlib
- 125.3
  The fill_between() Function
- 125.4
  The stackplot() Function
- 125.5
  Gradient-Filled Area Plot
- 125.6
  Percentage Stacked Area Plot
- 125.7
  Area Plot with Annotations
Matplotlib - Bar Graphs
8
- 126.1
  overview
- 126.2
  Bar Graphs in Matplotlib
- 126.3
  The bar() Function
- 126.4
  Basic Vertical Bar Graph
- 126.5
  Horizontal Bar Graph
- 126.6
  Grouped Bar Graph
- 126.7
  Stacked Bar Graph
- 126.8
  Dynamically Updating a Bar Graph
Matplotlib - Histogram
7
- 127.1
  overview
- 127.2
  Histogram in Matplotlib
- 127.3
  Creating a Vertical Histogram
- 127.4
  Customized Histogram with Density
- 127.5
  Cumulative Histogram
- 127.6
  Histogram with Different Color and Edge Color
- 127.7
  Stacked Histogram with Multiple Datasets
Matplotlib - Pie Chart
7
- 128.1
  overview
- 128.2
  Pie Chart in Matplotlib
- 128.3
  The pie() Function
- 128.4
  Exploded Pie Chart
- 128.5
  Custom Colored Pie Chart
- 128.6
  Percentage-labeled Pie Chart
- 128.7
  Shadowed Pie Chart
Matplotlib - Scatter Plot
7
- 129.1
  overview
- 129.2
  Scatter Plot in Matplotlib
- 129.3
  The scatter() Function
- 129.4
  Colored and Sized Scatter Plot
- 129.5
  Custom Marker Scatter Plot
- 129.6
  Transparent Scatter Plot
- 129.7
  Connected Scatter Plot
Matplotlib - Box Plots
6
- 130.1
  overview
- 130.2
  Box Plot in Matplotlib
- 130.3
  Horizontal Box Plot with Notches
- 130.4
  Box Plot with Custom Colors
- 130.5
  Grouped Box Plot
- 130.6
  Box Plot with Outliers
Matplotlib - Arrow Demo
7
- 131.1
  overview
- 131.2
  Arrow Demo in Matplotlib
- 131.3
  The arrow() Function
- 131.4
  Arrow Demo with Custom Color
- 131.5
  Double-Headed Arrow Demo
- 131.6
  Arrow Demo with Dashed Line
- 131.7
  Arrow Demo with Custom Arrowhead Shape
Matplotlib - Fancy Boxes
7
- 132.1
  overview
- 132.2
  Fancy Boxes in Matplotlib
- 132.3
  The FancyBboxPatch() function
- 132.4
  Rounded Fancy Box
- 132.5
  Squared Fancy Box with Dashed Edge
- 132.6
  Oval Fancy Box
- 132.7
  Serrated Fancy Box
Matplotlib - Zorder Demo
7
- 133.1
  overview
- 133.2
  Zorder Demo in Matplotlib
- 133.3
  The set_zorder() Function
- 133.4
  Controlling Drawing Order with zorder
- 133.5
  Layering Bars and Text with zorder
- 133.6
  Ordering Scatter Plots with zorder
- 133.7
  Overlaying Multiple Plots with zorder
Matplotlib - Hatch Demo
7
- 134.1
  overview
- 134.2
  Hatch Demo in Matplotlib
- 134.3
  The hatch Parameter
- 134.4
  Multiple Hatch Patterns
- 134.5
  Hatch Density Variation
- 134.6
  Hatch Styles for Bar Charts
- 134.7
  Hatch Demo for Patch
Matplotlib - Mmh Donuts
7
- 135.1
  overview
- 135.2
  Mmh Donuts in Matplotlib
- 135.3
  The matplotlib.patches.Wedge Class
- 135.4
  Basic Donut
- 135.5
  Double Donut
- 135.6
  Patterned Donut
- 135.7
  Custom Donut
Matplotlib - Ellipse Demo
7
- 136.1
  overview
- 136.2
  Ellipse Demo in Matplotlib
- 136.3
  The matplotlib.patches.Ellipse Class
- 136.4
  Rotated Ellipse
- 136.5
  Ellipse with Custom Color
- 136.6
  Multiple Ellipses
- 136.7
  Ellipse with Transparency
Matplotlib - Bezier Curve
7
- 137.1
  overview
- 137.2
  Bezier Curve in Matplotlib
- 137.3
  The matplotlib.patches.Ellipse Class
- 137.4
  Basic Bezier Curve
- 137.5
  Quadratic Bezier Curve
- 137.6
  Animated Bezier Curve
- 137.7
  Custom Bezier Curve with Arrowhead
Matplotlib - Bubble Plots
7
- 138.1
  overview
- 138.2
  Bubble Plots in Matplotlib
- 138.3
  The scatter() Function
- 138.4
  Basic Bubble Plot
- 138.5
  Color-Mapped Bubble Plot
- 138.6
  Customized Bubble Plot
- 138.7
  Bubble Plot with Annotations
Matplotlib - Stacked Plots
7
- 139.1
  overview
- 139.2
  Stacked Plots in Matplotlib
- 139.3
  The stackplot() Function
- 139.4
  Basic Stacked Area Plot
- 139.5
  Stacked Plot with Symmetric Baseline
- 139.6
  Stacked Plot with Transparency
- 139.7
  Stacked Percentage Plot
Matplotlib - Table Charts
7
- 140.1
  overview
- 140.2
  Table Charts in Matplotlib
- 140.3
  The table() Function
- 140.4
  Basic Table Chart
- 140.5
  Table Chart with Row and Column Labels
- 140.6
  Table Chart with Customized Borders
- 140.7
  Table Chart with Custom Location and Size
Matplotlib - Hexagonal Bin Plots
7
- 141.1
  overview
- 141.2
  Hexagonal Bin Plots in Matplotlib
- 141.3
  The hexbin() Function
- 141.4
  Basic Hexagonal Bin Plot
- 141.5
  Hexagonal Bin Plot with Marginal Histograms
- 141.6
  Hexagonal Bin Plot with Log-Scaled Colorbar
- 141.7
  Hexagonal Bin Plot with Customized Edge Color
Matplotlib - Violin Plot
7
- 142.1
  overview
- 142.2
  Violin Plot in Matplotlib
- 142.3
  The violinplot() Function
- 142.4
  Basic Violin Plot
- 142.5
  Horizontal Violin Plot
- 142.6
  Multiple Violins in One Plot
- 142.7
  Violin Plot with Quantiles
Matplotlib - Event Plot
7
- 143.1
  overview
- 143.2
  Event Plot in Matplotlib
- 143.3
  The eventplot() Function
- 143.4
  Basic Horizontal Event Plot
- 143.5
  Vertical Event Plot with Custom Colors
- 143.6
  Combined Horizontal and Vertical Event Plot
- 143.7
  Multiple Event Plots
Matplotlib - Heatmap
7
- 144.1
  overview
- 144.2
  Heatmap in Matplotlib
- 144.3
  The imshow() Function
- 144.4
  Basic Heatmap
- 144.5
  Annotated Heatmap
- 144.6
  Clustered Heatmap
- 144.7
  Heatmap with Row and Column Labels
Matplotlib - Stairs Plots
7
- 145.1
  overview
- 145.2
  Stairs Plots in Matplotlib
- 145.3
  The step() Function
- 145.4
  Basic Stairs Plot
- 145.5
  Multiple Stairs Plots
- 145.6
  Staircase Plot with Annotation
- 145.7
  Filled Staircase Plot
Matplotlib - Errorbar
7
- 146.1
  overview
- 146.2
  Errorbar in Matplotlib
- 146.3
  The errorbar() Function
- 146.4
  Basic Errorbar Plot
- 146.5
  Horizontal Errorbars
- 146.6
  Asymmetric Error Bars
- 146.7
  Subsampled Errorbars
Matplotlib - Hinton Diagram
7
- 147.1
  overview
- 147.2
  Hinton Diagram in Matplotlib
- 147.3
  The hinton() Function
- 147.4
  Random Matrix Hinton Diagram
- 147.5
  Identity Matrix Hinton Diagram
- 147.6
  Sparse Matrix Hinton Diagram
- 147.7
  Symmetric Matrix Hinton Diagram
Matplotlib - Contour Plots
2
- 148.1
  overview
- 148.2
  Contour Plot in Matplotlib
Matplotlib - Wireframe Plots
7
- 149.1
  overview
- 149.2
  Wireframe Plots in Matplotlib
- 149.3
  The plot_wireframe() Function
- 149.4
  Basic Wireframe Plot
- 149.5
  Wireframe Plot with Increased Density
- 149.6
  Multiple Wireframe Plots
- 149.7
  Parametric Surface Wireframe Plot
Matplotlib - Surface Plots
7
- 150.1
  overview
- 150.2
  Surface Plots in Matplotlib
- 150.3
  The plot_surface() Function
- 150.4
  Saddle-shaped Surface Plot
- 150.5
  Gaussian Surface Plot
- 150.6
  Sine and Cosine Surface Plots
- 150.7
  Random Noise Surface Plot
Matplotlib - Triangulations
6
- 151.1
  overview
- 151.2
  Triangulations in Matplotlib
- 151.3
  Triangulated Surface Plot
- 151.4
  Delaunay Triangulation for Scatter Plot
- 151.5
  Contour Plot with Triangulation
- 151.6
  Triangulated Line Plot
Matplotlib - Stream Plot
7
- 152.1
  overview
- 152.2
  Stream Plot in Matplotlib
- 152.3
  The streamplot() Function
- 152.4
  Basic Stream Plot
- 152.5
  Multiple Stream Plots
- 152.6
  Stream Plot with Annotations
- 152.7
  Stream Plot with Start Points
Matplotlib - Ishikawa Diagram
6
- 153.1
  overview
- 153.2
  Ishikawa Diagram in Matplotlib
- 153.3
  Ishikawa Diagram – Software Development Issues
- 153.4
  Ishikawa Diagram – Manufacturing Defects
- 153.5
  Ishikawa Diagram – Project Management Challenges
- 153.6
  Ishikawa Diagram – Customer Service Issues
Matplotlib - 3D Plotting
7
- 154.1
  overview
- 154.2
  3D Plotting in Matplotlib
- 154.3
  The mpl_toolkits.mplot3d Module
- 154.4
  3D Scatter Plot
- 154.5
  3D Line Plot
- 154.6
  3D Surface Plot
- 154.7
  3D Bar Plot
Matplotlib - 3D Lines
6
- 155.1
  overview
- 155.2
  3D Lines in Matplotlib
- 155.3
  Basic 3D Line Plot
- 155.4
  Multiple 3D Lines
- 155.5
  Helix 3D Line
- 155.6
  Parametric 3D Line
Matplotlib - 3D Scatter Plots
6
- 156.1
  overview
- 156.2
  3D Scatter Plots in Matplotlib
- 156.3
  Basic 3D Scatter Plot
- 156.4
  Multiple 3D Scatter Plots
- 156.5
  3D Scatter Plot with Annotations
- 156.6
  Connected 3D Scatter Plot
Matplotlib - 3D Contours
5
- 157.1
  overview
- 157.2
  Basic 3D Contour
- 157.3
  Parametric 3D Contours
- 157.4
  3D Contours from Irregular Data
- 157.5
  Contour Lines in 3D Contours
Matplotlib - 3D Bar Plots
6
- 158.1
  overview
- 158.2
  3D Bar Graphs in Matplotlib
- 158.3
  Basic 3D Bar Graph
- 158.4
  Stacked 3D Bar Graphs
- 158.5
  Grouped 3D Bar Graphs
- 158.6
  3D Bar Graphs with Custom Colors
Matplotlib - 3D Wireframe Plots
2
- 159.1
  overview
- 159.2
  3D Wireframe Plot in Matplotlib
Matplotlib - 3D Surface Plots
2
- 160.1
  overview
- 160.2
  3D Surface Plot in Matplotlib
Matplotlib - 3D Vignettes
6
- 161.1
  overview
- 161.2
  3D Vignettes in Matplotlib
- 161.3
  Spherical Vignette
- 161.4
  Toroidal Vignette
- 161.5
  Cylindrical Vignette
- 161.6
  Helical Vignette
Matplotlib - 3D Volumes
6
- 162.1
  overview
- 162.2
  3D Volumes in Matplotlib
- 162.3
  Basic 3D Volume Plot
- 162.4
  3D Cone Volume
- 162.5
  3D Sphere Volume
- 162.6
  3D Parallelepiped Volume
Matplotlib - 3D Voxels
6
- 163.1
  overview
- 163.2
  3D Voxels in Matplotlib
- 163.3
  Basic 3D Voxels
- 163.4
  Gradient 3D Voxels
- 163.5
  Checkerboard Pattern 3D Voxels
- 163.6
  Spherical 3D Voxels
Matplotlib - Time Plots and Signals
6
- 164.1
  overview
- 164.2
  Time Plots and Signals in Matplotlib
- 164.3
  Basic Time Plot
- 164.4
  Sine Wave Signal
- 164.5
  Random Time Plot
- 164.6
  Step Function Signal
Matplotlib - Filled Plots
6
- 165.1
  overview
- 165.2
  Filled Plots in Matplotlib
- 165.3
  Basic Filled Plot
- 165.4
  Filled Plot between Two Lines
- 165.5
  Filled Polar Area Plot
- 165.6
  Filled Plot from Irregular Data
Matplotlib - Step Plots
6
- 166.1
  overview
- 166.2
  Step Plots in Matplotlib
- 166.3
  Basic Step Plot
- 166.4
  Post Step Plot
- 166.5
  Pre Step Plot
- 166.6
  Sinusoidal Step Plot
Matplotlib - XKCD Style
6
- 167.1
  overview
- 167.2
  XKCD Style in Matplotlib
- 167.3
  Basic XKCD Style Plot
- 167.4
  XKCD Style Scatter Plot
- 167.5
  XKCD Style Bar Plot
- 167.6
  XKCD Style Pie Chart
Matplotlib - Quiver Plot
6
- 168.1
  overview
- 168.2
  Quiver Plot in Matplotlib
- 168.3
  Basic Quiver Plot
- 168.4
  Meshgrid Quiver Plot
- 168.5
  Gradient Quiver Plot
- 168.6
  Colored Quiver Plot
Matplotlib - Stem Plots
6
- 169.1
  iverview
- 169.2
  Stem Plots in Matplotlib
- 169.3
  Basic Stem Plot
- 169.4
  Horizontal Stem Plot
- 169.5
  Sinusoidal Stem Plot
- 169.6
  Triangular Stem Plot
Matplotlib - Visualizing Vectors
6
- 170.1
  overview
- 170.2
  Visualizing Vectors in Matplotlib
- 170.3
  Basic 2D Vectors
- 170.4
  Multiple 2D Vectors
- 170.5
  Vector Addition
- 170.6
  Vector Dot Product
Matplotlib - Audio Visualization
5
- 171.1
  overview
- 171.2
  Audio Visualization in Matplotlib
- 171.3
  Audio Visualization: Mel Spectrogram
- 171.4
  Audio Visualization: Chromagram
- 171.5
  Audio Processing: Beat Tracker
Matplotlib - Audio Processing
5
- 172.1
  overview
- 172.2
  Audio Processing in Matplotlib
- 172.3
  Audio Processing: Waveform Visualization
- 172.4
  Audio Processing: Spectrogram
- 172.5
  Audio Processing: Frequency Spectrum
Matplotlib - Quick Guide
34
- 173.1
  Matplotlib – Introduction
- 173.2
  Matplotlib – Environment Setup
- 173.3
  Matplotlib – Anaconda distribution
- 173.4
  Matplotlib – Jupyter Notebook
- 173.5
  Matplotlib – Pyplot API
- 173.6
  Matplotlib – Simple Plot
- 173.7
  Matplotlib – PyLab module
- 173.8
  Matplotlib – Object-oriented Interface
- 173.9
  Matplotlib – Figure Class
- 173.10
  Matplotlib – Axes Class
- 173.11
  Matplotlib – Multiplots
- 173.12
  Matplotlib – Subplots() Function
- 173.13
  Matplotlib – Subplot2grid() Function
- 173.14
  Matplotlib – Grids
- 173.15
  Matplotlib – Formatting Axes
- 173.16
  Matplotlib – Setting Limits
- 173.17
  Matplotlib – Setting Ticks and Tick Labels
- 173.18
  Matplotlib – Twin Axes
- 173.19
  Matplotlib – Bar Plot
- 173.20
  Matplotlib – Histogram
- 173.21
  Matplotlib – Pie Chart
- 173.22
  Matplotlib – Scatter Plot
- 173.23
  Matplotlib – Contour Plot
- 173.24
  Matplotlib – Quiver Plot
- 173.25
  Matplotlib – Box Plot
- 173.26
  Matplotlib – Violin Plot
- 173.27
  Matplotlib – Three-dimensional Plotting
- 173.28
  Matplotlib – 3D Contour Plot
- 173.29
  Matplotlib – 3D Wireframe plot
- 173.30
  Matplotlib – 3D Surface plot
- 173.31
  Matplotlib – Working With Text
- 173.32
  Matplotlib – Mathematical Expressions
- 173.33
  Matplotlib – Working with Images
- 173.34
  Matplotlib – Transforms

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