- Matplotlib Basics
- Matplotlib - Home
- Matplotlib - Introduction
- Matplotlib - Vs Seaborn
- Matplotlib - Environment Setup
- Matplotlib - Anaconda distribution
- Matplotlib - Jupyter Notebook
- Matplotlib - Pyplot API
- Matplotlib - Simple Plot
- Matplotlib - Saving Figures
- Matplotlib - Markers
- Matplotlib - Figures
- Matplotlib - Styles
- Matplotlib - Legends
- Matplotlib - Colors
- Matplotlib - Colormaps
- Matplotlib - Colormap Normalization
- Matplotlib - Choosing Colormaps
- Matplotlib - Colorbars
- Matplotlib - Text
- Matplotlib - Text properties
- Matplotlib - Subplot Titles
- Matplotlib - Images
- Matplotlib - Image Masking
- Matplotlib - Annotations
- Matplotlib - Arrows
- Matplotlib - Fonts
- Matplotlib - What are Fonts?
- Setting Font Properties Globally
- Matplotlib - Font Indexing
- Matplotlib - Font Properties
- Matplotlib - Scales
- Matplotlib - Linear and Logarthmic Scales
- Matplotlib - Symmetrical Logarithmic and Logit Scales
- Matplotlib - LaTeX
- Matplotlib - What is LaTeX?
- Matplotlib - LaTeX for Mathematical Expressions
- Matplotlib - LaTeX Text Formatting in Annotations
- Matplotlib - PostScript
- Enabling LaTex Rendering in Annotations
- Matplotlib - Mathematical Expressions
- Matplotlib - Animations
- Matplotlib - Artists
- Matplotlib - Styling with Cycler
- Matplotlib - Paths
- Matplotlib - Path Effects
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- Matplotlib - Radian Ticks
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- Matplotlib - Autoscaling
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- Matplotlib - Logarithmic Axes
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- Matplotlib - Unit Handling
- Matplotlib - Ellipse with Units
- Matplotlib - Spines
- Matplotlib - Axis Ranges
- Matplotlib - Axis Scales
- Matplotlib - Axis Ticks
- Matplotlib - Formatting Axes
- Matplotlib - Axes Class
- Matplotlib - Twin Axes
- Matplotlib - Figure Class
- Matplotlib - Multiplots
- Matplotlib - Grids
- Matplotlib - Object-oriented Interface
- Matplotlib - PyLab module
- Matplotlib - Subplots() Function
- Matplotlib - Subplot2grid() Function
- Matplotlib - Anchored Artists
- Matplotlib - Manual Contour
- Matplotlib - Coords Report
- Matplotlib - AGG filter
- Matplotlib - Ribbon Box
- Matplotlib - Fill Spiral
- Matplotlib - Findobj Demo
- Matplotlib - Hyperlinks
- Matplotlib - Image Thumbnail
- Matplotlib - Plotting with Keywords
- Matplotlib - Create Logo
- Matplotlib - Multipage PDF
- Matplotlib - Multiprocessing
- Matplotlib - Print Stdout
- Matplotlib - Compound Path
- Matplotlib - Sankey Class
- Matplotlib - MRI with EEG
- Matplotlib - Stylesheets
- Matplotlib - Background Colors
- Matplotlib - Basemap
- Matplotlib Event Handling
- Matplotlib - Event Handling
- Matplotlib - Close Event
- Matplotlib - Mouse Move
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- Matplotlib - Looking Glass
- Matplotlib - Path Editor
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- Matplotlib - Timers
- Matplotlib - Viewlims
- Matplotlib - Zoom Window
- Matplotlib Plotting
- Matplotlib - Bar Graphs
- Matplotlib - Histogram
- Matplotlib - Pie Chart
- Matplotlib - Scatter Plot
- Matplotlib - Box Plot
- Matplotlib - Violin Plot
- Matplotlib - Contour Plot
- Matplotlib - 3D Plotting
- Matplotlib - 3D Contours
- Matplotlib - 3D Wireframe Plot
- Matplotlib - 3D Surface Plot
- Matplotlib - Quiver Plot
- Matplotlib Useful Resources
- Matplotlib - Quick Guide
- Matplotlib - Useful Resources
- Matplotlib - Discussion
Matplotlib - Pie Chart
A Pie Chart can only display one series of data. Pie charts show the size of items (called wedge) in one data series, proportional to the sum of the items. The data points in a pie chart are shown as a percentage of the whole pie.
Matplotlib API has a pie() function that generates a pie diagram representing data in an array. The fractional area of each wedge is given by x/sum(x). If sum(x)< 1, then the values of x give the fractional area directly and the array will not be normalized. Theresulting pie will have an empty wedge of size 1 - sum(x).
The pie chart looks best if the figure and axes are square, or the Axes aspect is equal.
Parameters
Following table lists down the parameters foe a pie chart −
x | array-like. The wedge sizes. |
labels | list. A sequence of strings providing the labels for each wedge. |
Colors | A sequence of matplotlibcolorargs through which the pie chart will cycle. If None, will use the colors in the currently active cycle. |
Autopct | string, used to label the wedges with their numeric value. The label will be placed inside the wedge. The format string will be fmt%pct. |
Following code uses the pie() function to display the pie chart of the list of students enrolled for various computer language courses. The proportionate percentage is displayed inside the respective wedge with the help of autopct parameter which is set to %1.2f%.
from matplotlib import pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes([0,0,1,1]) ax.axis('equal') langs = ['C', 'C++', 'Java', 'Python', 'PHP'] students = [23,17,35,29,12] ax.pie(students, labels = langs,autopct='%1.2f%%') plt.show()