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Crash Visualization
  • Welcome
  • Preface
    • Who the book is written for
    • How the book is organized
  • 1. Introduction of Data Visualization
    • 1.1 What is data visualization?
    • 1.2 Why does visualization matter?
  • 2. Tricks in Visualization
    • 2.1 Choose Appropriate Chart
    • 2.2 Features of Charts
      • 2.2.1 Table
      • 2.2.2 Column Chart
      • 2.2.3 Line Chart
      • 2.2.4 Pie Chart
      • 2.2.5 Scatter Chart
      • 2.2.6 Map Chart
    • 2.3 Misused Graph
    • 2.4 Tips in Visualization
  • 3. Matplotlib
    • 3.1 Basic Concepts
    • 3.2 Line Chart
    • 3.3 Area Chart
    • 3.4 Column Chart
    • 3.5 Histogram Chart
    • 3.6 Scatter Chart
    • 3.7 Lollipop Chart
    • 3.8 Pie Chart
    • 3.9 Venn Chart
    • 3.10 Waffle Chart
    • 3.11 Animation
  • 4. Seaborn
    • 4.1 Trends
    • 4.2 Ranking
      • 4.2.1 Barplot
      • 4.2.2 Boxplot
    • 4.3 Composition
      • 4.3.1 Stacked Chart
    • 4.4 Correlation
      • 4.4.1 Scatter Plot
      • 4.4.2 Linear Relationship
      • 4.4.3 Heatmap
      • 4.4.4 Pairplot
    • 4.5 Distribution
      • 4.5.1 Boxplot
      • 4.5.2 Violin plot
      • 4.5.3 Histogram plot
      • 4.5.4 Density plot
      • 4.5.5 Joint plot
  • 5. Bokeh
    • 5.1 Basic Plotting
    • 5.2 Data Sources
    • 5.3 Annotations
    • 5.4 Categorical Data
    • 5.5 Presentation and Layouts
    • 5.6 Linking and Interactions
    • 5.7 Network Graph
    • 5.8 Widgets
  • 6. Plotly
    • 6.1 Fundamental Concepts
      • 6.1.1 Plotly Express
      • 6.1.2 Plotly Graph Objects
    • 6.2 Advanced Charts
      • 6.2.1 Advanced Scatter Chart
      • 6.2.2 Advanced Bar Chart
      • 6.2.3 Advanced Pie Chart
      • 6.2.4 Advanced Heatmap
      • 6.2.5 Sankey Chart
      • 6.2.6 Tables
    • 6.3 Statistical Charts
      • 6.3.1 Common Statistical Charts
      • 6.3.2 Dendrograms
      • 6.3.3 Radar Chart
      • 6.3.4 Polar Chart
      • 6.3.5 Streamline Chart
    • 6.4 Financial Charts
      • 6.4.1 Funnel Chart
      • 6.4.2 Candlestick Chart
      • 6.4.3 Waterfall Chart
  • Support
    • Donation
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Last updated 4 years ago

is a plotting library for the programming language and its numerical mathematics extension .

Pyplot is a Matplotlib module which provides a MATLAB-like interface. Matplotlib is designed to be as usable as MATLAB, with the ability to use Python and the advantage of being free and open-source.

1. Installing an official release

Matplotlib and its dependencies are available as wheel packages for macOS, Windows and Linux distributions:

2. Elements of Figure

let's have a deeper look at the components of a Matplotlib figure.

3. Check and Quickstart

4. Content

Matplotlib graphs your data on s (i.e., windows), each of which can contain one or more (i.e., an area where points can be specified in terms of x-y coordinates (or theta-r in a polar plot, or x-y-z in a 3D plot, etc.). The most simple way of creating a figure with axes is using .

python -m pip install -U pip
python -m pip install -U matplotlib
Figure
Axes
pyplot.subplots
import matplotlib.pyplot as plt   # import the data exploration package
import numpy as np                # import the data computing package
  • 1. Installing an official release
  • 2. Elements of Figure
  • 3. Check and Quickstart
  • 4. Content
Previous2.4 Tips in VisualizationNext3.1 Basic Concepts

3. Matplotlib

3.1 Basic Concepts
3.2 Line Chart
3.3 Area Chart
3.4 Column Chart
3.5 Histogram Chart
3.6 Scatter Chart
3.7 Lollipop Chart
3.8 Pie Chart
3.9 Venn Chart
3.10 Waffle Chart
3.11 Animation
Matplotlib
Python
NumPy
Anatomy of a figure