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  • Book Overview & Buying matplotlib Plotting Cookbook
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matplotlib Plotting Cookbook

matplotlib Plotting Cookbook

By : Alexandre Devert
3.7 (10)
Buy this Book
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matplotlib Plotting Cookbook

matplotlib Plotting Cookbook

3.7 (10)
By: Alexandre Devert
Buy this Book

Overview of this book

Table of Contents (15 chapters)
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matplotlib Plotting Cookbook
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matplotlib Plotting Cookbook
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
First Steps
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First Steps
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Introduction
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Installing matplotlib
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Plotting one curve
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Using NumPy
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Plotting multiple curves
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Plotting curves from file data
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Plotting points
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Plotting bar charts
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Plotting multiple bar charts
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Plotting stacked bar charts
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Plotting back-to-back bar charts
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Plotting pie charts
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Plotting histograms
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Plotting boxplots
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Plotting triangulations
2
Customizing the Color and Styles
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Customizing the Color and Styles
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Introduction
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Defining your own colors
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Using custom colors for scatter plots
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Using custom colors for bar charts
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Using custom colors for pie charts
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Using custom colors for boxplots
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Using colormaps for scatter plots
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Using colormaps for bar charts
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Controlling a line pattern and thickness
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Controlling a fill pattern
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Controlling a marker's style
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Controlling a marker's size
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Creating your own markers
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Getting more control over markers
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Creating your own color scheme
3
Working with Annotations
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Working with Annotations
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Introduction
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Adding a title
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Using LaTeX-style notations
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Adding a label to each axis
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Adding text
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Adding arrows
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Adding a legend
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Adding a grid
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Adding lines
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Adding shapes
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Controlling tick spacing
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Controlling tick labeling
4
Working with Figures
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Working with Figures
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Introduction
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Compositing multiple figures
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Scaling both the axes equally
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Setting an axis range
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Setting the aspect ratio
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Inserting subfigures
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Using a logarithmic scale
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Using polar coordinates
5
Working with a File Output
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Working with a File Output
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Introduction
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Generating a PNG picture file
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Handling transparency
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Controlling the output resolution
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Generating PDF or SVG documents
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Handling multiple-page PDF documents
6
Working with Maps
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Working with Maps
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Introduction
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Visualizing the content of a 2D array
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Adding a colormap legend to a figure
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Visualizing nonuniform 2D data
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Visualizing a 2D scalar field
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Visualizing contour lines
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Visualizing a 2D vector field
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Visualizing the streamlines of a 2D vector field
7
Working with 3D Figures
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Working with 3D Figures
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Introduction
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Creating 3D scatter plots
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Creating 3D curve plots
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Plotting a scalar field in 3D
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Plotting a parametric 3D surface
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Embedding 2D figures in a 3D figure
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Creating a 3D bar plot
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User Interface
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User Interface
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Introduction
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Making a user-controllable plot
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Integrating a plot to a Tkinter user interface
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Integrating a plot to a wxWidgets user interface
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Integrating a plot to a GTK user interface
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Integrating a plot in a Pyglet application
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Index
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Index

Plotting histograms

Histograms are graphical representations of a probability distribution. In fact, a histogram is just a specific kind of a bar chart. We could easily use matplotlib's bar chart function and do some statistics to generate histograms. However, histograms are so useful that matplotlib provides a function just for them. In this recipe, we are going to see how to use this histogram function.

How to do it...

The following script draws 1000 values from a normal distribution and then generates histograms with 20 bins:

import numpy as np
import matplotlib.pyplot as plt

X = np.random.randn(1000)

plt.hist(X, bins = 20)
plt.show()

The histogram will change a bit each time we run the script as the dataset is randomly generated. The preceding script will display the following graph:

How it works...

The pyplot.hist() function takes a list of values as the input. The range of the values will be divided into equal-sized bins (10 bins by default). The pyplot.hist() function will generate a bar chart, one bar for one bin. The height of one bar is the number of values following in the corresponding bin. The number of bins is determined by the optional parameter bins. By setting the optional parameter normed to True, the bar height is normalized and the sum of all bar heights is equal to 1.

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matplotlib Plotting Cookbook
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