1.4. Matplotlib: plotting¶

Authors: Nicolas Rougier, Mike Müller, Gaël Varoquaux

1.4.1. Introduction¶

Tip

Matplotlib is probably the most used Python package for 2D-graphics. It provides both a quick way to visualize data from Python and publication-quality figures in many formats. We are going to explore matplotlib in interactive mode covering most common cases.

1.4.1.1. IPython, Jupyter, and matplotlib modes¶

Tip

The Jupyter notebook and the IPython enhanced interactive Python, are tuned for the scientific-computing workflow in Python, in combination with Matplotlib:

For interactive matplotlib sessions, turn on the matplotlib mode

IPython console:

When using the IPython console, use:

In [1]: %matplotlib

Jupyter notebook:

In the notebook, insert, at the beginning of the notebook the following magic:

%matplotlib inline


1.4.1.2. pyplot¶

Tip

pyplot provides a procedural interface to the matplotlib object-oriented plotting library. It is modeled closely after Matlab™. Therefore, the majority of plotting commands in pyplot have Matlab™ analogs with similar arguments. Important commands are explained with interactive examples.

import matplotlib.pyplot as plt


1.4.2. Simple plot¶

Tip

In this section, we want to draw the cosine and sine functions on the same plot. Starting from the default settings, we’ll enrich the figure step by step to make it nicer.

First step is to get the data for the sine and cosine functions:

import numpy as np

X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)


X is now a numpy array with 256 values ranging from to (included). C is the cosine (256 values) and S is the sine (256 values).

To run the example, you can type them in an IPython interactive session:

$ipython --matplotlib  This brings us to the IPython prompt: IPython 0.13 -- An enhanced Interactive Python. ? -> Introduction to IPython's features. %magic -> Information about IPython's 'magic' % functions. help -> Python's own help system. object? -> Details about 'object'. ?object also works, ?? prints more.  Tip You can also download each of the examples and run it using regular python, but you will lose interactive data manipulation: $ python plot_exercise_1.py


You can get source for each step by clicking on the corresponding figure.

1.4.2.1. Plotting with default settings¶

Hint

Documentation

Tip

Matplotlib comes with a set of default settings that allow customizing all kinds of properties. You can control the defaults of almost every property in matplotlib: figure size and dpi, line width, color and style, axes, axis and grid properties, text and font properties and so on.

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)

plt.plot(X, C)
plt.plot(X, S)

plt.show()


1.4.2.2. Instantiating defaults¶

Hint

Documentation

In the script below, we’ve instantiated (and commented) all the figure settings that influence the appearance of the plot.

Tip

The settings have been explicitly set to their default values, but now you can interactively play with the values to explore their affect (see Line properties and Line styles below).

import numpy as np
import matplotlib.pyplot as plt

# Create a figure of size 8x6 inches, 80 dots per inch
plt.figure(figsize=(8, 6), dpi=80)

# Create a new subplot from a grid of 1x1
plt.subplot(1, 1, 1)

X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)

# Plot cosine with a blue continuous line of width 1 (pixels)
plt.plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# Plot sine with a green continuous line of width 1 (pixels)
plt.plot(X, S, color="green", linewidth=1.0, linestyle="-")

# Set x limits
plt.xlim(-4.0, 4.0)

# Set x ticks
plt.xticks(np.linspace(-4, 4, 9))

# Set y limits
plt.ylim(-1.0, 1.0)

# Set y ticks
plt.yticks(np.linspace(-1, 1, 5))

# Save figure using 72 dots per inch
# plt.savefig("exercise_2.png", dpi=72)

# Show result on screen
plt.show()


1.4.2.3. Changing colors and line widths¶

Hint

Documentation

Tip

First step, we want to have the cosine in blue and the sine in red and a slightly thicker line for both of them. We’ll also slightly alter the figure size to make it more horizontal.

...
plt.figure(figsize=(10, 6), dpi=80)
plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-")
...


1.4.2.4. Setting limits¶

Hint

Documentation

Tip

Current limits of the figure are a bit too tight and we want to make some space in order to clearly see all data points.

...
plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.ylim(C.min() * 1.1, C.max() * 1.1)
...


1.4.2.5. Setting ticks¶

Hint

Documentation

Tip

Current ticks are not ideal because they do not show the interesting values (,:math:pm pi/2) for sine and cosine. We’ll change them such that they show only these values.

...
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
plt.yticks([-1, 0, +1])
...


1.4.2.6. Setting tick labels¶

Hint

Documentation

Tip

Ticks are now properly placed but their label is not very explicit. We could guess that 3.142 is but it would be better to make it explicit. When we set tick values, we can also provide a corresponding label in the second argument list. Note that we’ll use latex to allow for nice rendering of the label.

...
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.yticks([-1, 0, +1],
[r'$-1$', r'$0$', r'$+1$'])
...


1.4.2.7. Moving spines¶

Hint

Documentation

Tip

Spines are the lines connecting the axis tick marks and noting the boundaries of the data area. They can be placed at arbitrary positions and until now, they were on the border of the axis. We’ll change that since we want to have them in the middle. Since there are four of them (top/bottom/left/right), we’ll discard the top and right by setting their color to none and we’ll move the bottom and left ones to coordinate 0 in data space coordinates.

...
ax = plt.gca()  # gca stands for 'get current axis'
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
...


Hint

Documentation

Tip

Let’s add a legend in the upper left corner. This only requires adding the keyword argument label (that will be used in the legend box) to the plot commands.

...
plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-", label="sine")

plt.legend(loc='upper left')
...


1.4.2.9. Annotate some points¶

Hint

Documentation

Tip

Let’s annotate some interesting points using the annotate command. We chose the value and we want to annotate both the sine and the cosine. We’ll first draw a marker on the curve as well as a straight dotted line. Then, we’ll use the annotate command to display some text with an arrow.

...

t = 2 * np.pi / 3
plt.plot([t, t], [0, np.cos(t)], color='blue', linewidth=2.5, linestyle="--")
plt.scatter([t, ], [np.cos(t), ], 50, color='blue')

plt.annotate(r'$cos(\frac{2\pi}{3})=-\frac{1}{2}$',
xy=(t, np.cos(t)), xycoords='data',
xytext=(-90, -50), textcoords='offset points', fontsize=16,

plt.plot([t, t],[0, np.sin(t)], color='red', linewidth=2.5, linestyle="--")
plt.scatter([t, ],[np.sin(t), ], 50, color='red')

plt.annotate(r'$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
xy=(t, np.sin(t)), xycoords='data',
xytext=(+10, +30), textcoords='offset points', fontsize=16,
...


1.4.2.10. Devil is in the details¶

Hint

Documentation

Tip

The tick labels are now hardly visible because of the blue and red lines. We can make them bigger and we can also adjust their properties such that they’ll be rendered on a semi-transparent white background. This will allow us to see both the data and the labels.

...
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(16)
label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65))
...


1.4.3. Figures, Subplots, Axes and Ticks¶

A “figure” in matplotlib means the whole window in the user interface. Within this figure there can be “subplots”.

Tip

So far we have used implicit figure and axes creation. This is handy for fast plots. We can have more control over the display using figure, subplot, and axes explicitly. While subplot positions the plots in a regular grid, axes allows free placement within the figure. Both can be useful depending on your intention. We’ve already worked with figures and subplots without explicitly calling them. When we call plot, matplotlib calls gca() to get the current axes and gca in turn calls gcf() to get the current figure. If there is none it calls figure() to make one, strictly speaking, to make a subplot(111). Let’s look at the details.

1.4.3.1. Figures¶

Tip

A figure is the windows in the GUI that has “Figure #” as title. Figures are numbered starting from 1 as opposed to the normal Python way starting from 0. This is clearly MATLAB-style. There are several parameters that determine what the figure looks like:

Argument

Default

Description

num

1

number of figure

figsize

figure.figsize

figure size in inches (width, height)

dpi

figure.dpi

resolution in dots per inch

facecolor

figure.facecolor

color of the drawing background

edgecolor

figure.edgecolor

color of edge around the drawing background

frameon

True

draw figure frame or not

Tip

The defaults can be specified in the resource file and will be used most of the time. Only the number of the figure is frequently changed.

As with other objects, you can set figure properties also setp or with the set_something methods.

When you work with the GUI you can close a figure by clicking on the x in the upper right corner. But you can close a figure programmatically by calling close. Depending on the argument it closes (1) the current figure (no argument), (2) a specific figure (figure number or figure instance as argument), or (3) all figures ("all" as argument).

plt.close(1)     # Closes figure 1


1.4.3.2. Subplots¶

Tip

With subplot you can arrange plots in a regular grid. You need to specify the number of rows and columns and the number of the plot. Note that the gridspec command is a more powerful alternative.

1.4.3.3. Axes¶

Axes are very similar to subplots but allow placement of plots at any location in the figure. So if we want to put a smaller plot inside a bigger one we do so with axes.

1.4.3.4. Ticks¶

Well formatted ticks are an important part of publishing-ready figures. Matplotlib provides a totally configurable system for ticks. There are tick locators to specify where ticks should appear and tick formatters to give ticks the appearance you want. Major and minor ticks can be located and formatted independently from each other. Per default minor ticks are not shown, i.e. there is only an empty list for them because it is as NullLocator (see below).

Tick Locators¶

Tick locators control the positions of the ticks. They are set as follows:

ax = plt.gca()
ax.xaxis.set_major_locator(eval(locator))


There are several locators for different kind of requirements:

All of these locators derive from the base class matplotlib.ticker.Locator. You can make your own locator deriving from it. Handling dates as ticks can be especially tricky. Therefore, matplotlib provides special locators in matplotlib.dates.

1.4.4. Other Types of Plots: examples and exercises¶

1.4.4.1. Regular Plots¶

Starting from the code below, try to reproduce the graphic taking care of filled areas:

Hint

You need to use the fill_between() command.

n = 256
X = np.linspace(-np.pi, np.pi, n)
Y = np.sin(2 * X)

plt.plot(X, Y + 1, color='blue', alpha=1.00)
plt.plot(X, Y - 1, color='blue', alpha=1.00)


Click on the figure for solution.

1.4.4.2. Scatter Plots¶

Starting from the code below, try to reproduce the graphic taking care of marker size, color and transparency.

Hint

Color is given by angle of (X,Y).

n = 1024
rng = np.random.default_rng()
X = rng.normal(0,1,n)
Y = rng.normal(0,1,n)

plt.scatter(X,Y)


Click on figure for solution.

1.4.4.3. Bar Plots¶

Starting from the code below, try to reproduce the graphic by adding labels for red bars.

Hint

You need to take care of text alignment.

n = 12
X = np.arange(n)
rng = np.random.default_rng()
Y1 = (1 - X / float(n)) * rng.uniform(0.5, 1.0, n)
Y2 = (1 - X / float(n)) * rng.uniform(0.5, 1.0, n)

plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x, y in zip(X, Y1):
plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va='bottom')

plt.ylim(-1.25, +1.25)


Click on figure for solution.

1.4.4.4. Contour Plots¶

Starting from the code below, try to reproduce the graphic taking care of the colormap (see Colormaps below).

Hint

You need to use the clabel() command.

def f(x, y):
return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 -y ** 2)

n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
X, Y = np.meshgrid(x, y)

plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap='jet')
C = plt.contour(X, Y, f(X, Y), 8, colors='black', linewidth=.5)


Click on figure for solution.

1.4.4.5. Imshow¶

Starting from the code below, try to reproduce the graphic taking care of colormap, image interpolation and origin.

Hint

You need to take care of the origin of the image in the imshow command and use a colorbar()

def f(x, y):
return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)

n = 10
x = np.linspace(-3, 3, 4 * n)
y = np.linspace(-3, 3, 3 * n)
X, Y = np.meshgrid(x, y)
plt.imshow(f(X, Y))


Click on the figure for the solution.

1.4.4.6. Pie Charts¶

Starting from the code below, try to reproduce the graphic taking care of colors and slices size.

Hint

You need to modify Z.

rng = np.random.default_rng()
Z = rng.uniform(0, 1, 20)
plt.pie(Z)


Click on the figure for the solution.

1.4.4.7. Quiver Plots¶

Starting from the code below, try to reproduce the graphic taking care of colors and orientations.

Hint

You need to draw arrows twice.

n = 8
X, Y = np.mgrid[0:n, 0:n]
plt.quiver(X, Y)


Click on figure for solution.

1.4.4.8. Grids¶

Starting from the code below, try to reproduce the graphic taking care of line styles.

axes = plt.gca()
axes.set_xlim(0, 4)
axes.set_ylim(0, 3)
axes.set_xticklabels([])
axes.set_yticklabels([])


Click on figure for solution.

1.4.4.9. Multi Plots¶

Starting from the code below, try to reproduce the graphic.

Hint

You can use several subplots with different partition.

plt.subplot(2, 2, 1)
plt.subplot(2, 2, 3)
plt.subplot(2, 2, 4)


Click on figure for solution.

1.4.4.10. Polar Axis¶

Hint

You only need to modify the axes line

Starting from the code below, try to reproduce the graphic.

plt.axes([0, 0, 1, 1])

N = 20
theta = np.arange(0., 2 * np.pi, 2 * np.pi / N)
rng = np.random.default_rng()
width = np.pi / 4 * rng.random(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)

for r, bar in zip(radii, bars):
bar.set_facecolor(plt.cm.jet(r / 10.))
bar.set_alpha(0.5)


Click on figure for solution.

1.4.4.11. 3D Plots¶

Starting from the code below, try to reproduce the graphic.

Hint

You need to use contourf()

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')


Click on figure for solution.

1.4.4.12. Text¶

Try to do the same from scratch !

Hint

Have a look at the matplotlib logo.

Click on figure for solution.

1.4.5. Beyond this tutorial¶

Matplotlib benefits from extensive documentation as well as a large community of users and developers. Here are some links of interest:

1.4.5.1. Tutorials¶

 Pyplot tutorial Introduction Controlling line properties Working with multiple figures and axes Working with text Image tutorial Startup commands Importing image data into NumPy arrays Plotting NumPy arrays as images Text tutorial Text introduction Basic text commands Text properties and layout Writing mathematical expressions Text rendering With LaTeX Annotating text Artist tutorial Introduction Customizing your objects Object containers Figure container Axes container Axis containers Tick containers Path tutorial Introduction Bézier example Compound paths Transforms tutorial Introduction Data coordinates Axes coordinates Blended transformations Using offset transforms to create a shadow effect The transformation pipeline

1.4.5.2. Matplotlib documentation¶

 User guide FAQ Installation Usage How-To Troubleshooting Environment Variables

1.4.5.3. Code documentation¶

The code is well documented and you can quickly access a specific command from within a python session:

>>> import matplotlib.pyplot as plt
>>> help(plt.plot)
Help on function plot in module matplotlib.pyplot:

plot(*args: ...) -> 'list[Line2D]'
Plot y versus x as lines and/or markers.

Call signatures::

plot([x], y, [fmt], *, data=None, **kwargs)
plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
...


1.4.5.4. Galleries¶

The matplotlib gallery is also incredibly useful when you search how to render a given graphic. Each example comes with its source.

1.4.5.5. Mailing lists¶

Finally, there is a user mailing list where you can ask for help and a developers mailing list that is more technical.

1.4.6. Quick references¶

Here is a set of tables that show main properties and styles.

1.4.6.1. Line properties¶

Property

Description

Appearance

alpha (or a)

alpha transparency on 0-1 scale

antialiased

True or False - use antialised rendering

color (or c)

matplotlib color arg

linestyle (or ls)

linewidth (or lw)

float, the line width in points

solid_capstyle

Cap style for solid lines

solid_joinstyle

Join style for solid lines

dash_capstyle

Cap style for dashes

dash_joinstyle

Join style for dashes

marker

see Markers

markeredgewidth (mew)

line width around the marker symbol

markeredgecolor (mec)

edge color if a marker is used

markerfacecolor (mfc)

face color if a marker is used

markersize (ms)

size of the marker in points

1.4.6.4. Colormaps¶

All colormaps can be reversed by appending _r. For instance, gray_r is the reverse of gray.

If you want to know more about colormaps, check the documentation on Colormaps in matplotlib.

1.4.7. Full code examples¶

1.4.7.1. Code samples for Matplotlib¶

The examples here are only examples relevant to the points raised in this chapter. The matplotlib documentation comes with a much more exhaustive gallery.

Pie chart

Pie chart

A simple, good-looking plot

A simple, good-looking plot

Plotting a scatter of points

Plotting a scatter of points

Subplots

Subplots

Horizontal arrangement of subplots

Horizontal arrangement of subplots

Subplot plot arrangement vertical

Subplot plot arrangement vertical

A simple plotting example

A simple plotting example

Simple axes example

Simple axes example

3D plotting

3D plotting

Imshow elaborate

Imshow elaborate

Plotting a vector field: quiver

Plotting a vector field: quiver

A example of plotting not quite right

A example of plotting not quite right

Displaying the contours of a function

Displaying the contours of a function

Plotting in polar coordinates

Plotting in polar coordinates

Plot and filled plots

Plot and filled plots

Subplot grid

Subplot grid

Bar plots

Bar plots

Axes

Axes

Grid

Grid

3D plotting

3D plotting

GridSpec

GridSpec

Demo text printing

Demo text printing

Exercise 1

Exercise 1

Exercise 4

Exercise 4

Exercise 3

Exercise 3

Exercise 5

Exercise 5

Exercise 6

Exercise 6

Exercise 2

Exercise 2

Exercise 7

Exercise 7

Exercise 8

Exercise 8

Exercise 9

Exercise 9

Exercise

Exercise

1.4.7.3. Example demoing choices for an option¶

The colors matplotlib line plots

The colors matplotlib line plots

Linewidth

Linewidth

Alpha: transparency

Alpha: transparency

Aliased versus anti-aliased

Aliased versus anti-aliased

Aliased versus anti-aliased

Aliased versus anti-aliased

Marker size

Marker size

Marker edge width

Marker edge width

Colormaps

Colormaps

Solid joint style

Solid joint style

Solid cap style

Solid cap style

Marker edge color

Marker edge color

Marker face color

Marker face color

Dash capstyle

Dash capstyle

Dash join style

Dash join style

Markers

Markers

Linestyles

Linestyles

Locators for tick on axis

Locators for tick on axis

1.4.7.4. Code generating the summary figures with a title¶

3D plotting vignette

3D plotting vignette

Plotting in polar, decorated

Plotting in polar, decorated

Plot example vignette

Plot example vignette

Multiple plots vignette

Multiple plots vignette

Boxplot with matplotlib

Boxplot with matplotlib

Plot scatter decorated

Plot scatter decorated

Pie chart vignette

Pie chart vignette

Imshow demo

Imshow demo

Plotting quiver decorated

Plotting quiver decorated

Display the contours of a function

Display the contours of a function

Grid elaborate

Grid elaborate

Text printing decorated

Text printing decorated

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