Matplotlib tutorial
Matplotlib tutorial
Nicolas P. Rougier
Table of Contents
Sources are available from
All code and material is licensed under a .
Tutorial can be read at
Make sure to also read , N.P. Rougier, M. Droettboom & P. Bourne, Plos Computational Biology 10(9): e1003833. doi:10.1371/journal.pcbi.1003833.
matplotlib is probably the single most used Python package for 2D-graphics. It
provides both a very 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.
IPython and the pylab mode
is an enhanced interactive Python shell that
has lots of interesting features including named inputs and outputs, access to
shell commands, improved debugging and many more. When we start it with the
command line argument -pylab (--pylab since IPython version 0.12), it allows
interactive matplotlib sessions that have Matlab/Mathematica-like functionality.
pyplot provides a convenient interface to the matplotlib object-oriented
plotting library. It is modeled closely after Matlab(TM). Therefore, the
majority of plotting commands in pyplot have Matlab(TM) analogs with similar
arguments. Important commands are explained with interactive examples.
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,endpoint=True)
C,S = np.cos(X), np.sin(X)
X is now a numpy array with 256 values ranging from -π to +π (included). C is
the cosine (256 values) and S is the sine (256 values).
To run the example, you can download each of the examples and run it using:
$ python exercice_1.py
You can get source for each step by clicking on the corresponding figure.
Using defaults
Documentation
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. While matplotlib defaults
are rather good in most cases, you may want to modify some properties for
specific cases.
import numpy as np
import matplotlib.pyplot as plt
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C,S = np.cos(X), np.sin(X)
plt.plot(X,C)
plt.plot(X,S)
plt.show()
Instantiating defaults
Documentation
In the script below, we've instantiated (and commented) all the figure settings
that influence the appearance of the plot. The settings have been explicitly
set to their default values, but now you can interactively play with the values
to explore their affect (see
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(8,6), dpi=80)
plt.subplot(111)
X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C,S = np.cos(X), np.sin(X)
plt.plot(X, C, color=&blue&, linewidth=1.0, linestyle=&-&)
plt.plot(X, S, color=&green&, linewidth=1.0, linestyle=&-&)
plt.xlim(-4.0,4.0)
plt.xticks(np.linspace(-4,4,9,endpoint=True))
plt.ylim(-1.0,1.0)
plt.yticks(np.linspace(-1,1,5,endpoint=True))
plt.show()
Changing colors and line widths
Documentation
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=&-&)
Setting limits
Documentation
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)
Setting ticks
Documentation
Current ticks are not ideal because they do not show the interesting values
(+/-π,+/-π/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])
Setting tick labels
Documentation
Ticks are now properly placed but their label is not very explicit. We could
guess that 3.142 is π 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'$+1$'])
Moving spines
Documentation
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()
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))
Adding a legend
Documentation
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
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', frameon=False)
Annotate some points
Documentation
Let's annotate some interesting points using the annotate command. We chose the
2π/3 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'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
xy=(t, np.sin(t)), xycoords='data',
xytext=(+10, +30), textcoords='offset points', fontsize=16,
arrowprops=dict(arrowstyle=&-&&, connectionstyle=&arc3,rad=.2&))
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'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
xy=(t, np.cos(t)), xycoords='data',
xytext=(-90, -50), textcoords='offset points', fontsize=16,
arrowprops=dict(arrowstyle=&-&&, connectionstyle=&arc3,rad=.2&))
Devil is in the details
Documentation
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 ))
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. A figure in matplotlib means the whole window in the user
interface. Within this figure there can be subplots. 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.
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:
number of figure
figure.figsize
figure size in in inches (width, height)
figure.dpi
resolution in dots per inch
figure.facecolor
color of the drawing background
figure.edgecolor
color of edge around the drawing background
draw figure frame or not
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.
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).
As with other objects, you can set figure properties with the set_something methods.
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
command is a more
powerful alternative.
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.
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
Tick Locators
There are several locators for different kind of requirements:
NullLocator
IndexLocator
Place a tick on every multiple of some base number of points plotted.
FixedLocator
Tick locations are fixed.
LinearLocator
Determine the tick locations.
MultipleLocator
Set a tick on every integer that is multiple of some base.
AutoLocator
Select no more than n intervals at nice locations.
LogLocator
Determine the tick locations for log axes.
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.
For quite a long time, animation in matplotlib was not an easy task and was
done mainly through clever hacks. However, things have started to change since
version 1.1 and the introduction of tools for creating animation very
intuitively, with the possibility to save them in all kind of formats (but don't
expect to be able to run very complex animation at 60 fps though).
Documentation
The most easy way to make an animation in matplotlib is to declare a
FuncAnimation object that specifies to matplotlib what is the figure to
update, what is the update function and what is the delay between frames.
A very simple rain effect can be obtained by having small growing rings
randomly positioned over a figure. Of course, they won't grow forever since the
wave is supposed to damp with time. To simulate that, we can use a more and
more transparent color as the ring is growing, up to the point where it is no
more visible. At this point, we remove the ring and create a new one.
First step is to create a blank figure:
fig = plt.figure(figsize=(6,6), facecolor='white')
ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1)
Next, we need to create several rings. For this, we can use the scatter plot
object that is generally used to visualize points cloud, but we can also use it
to draw rings by specifying we don't have a facecolor. We have also to take
care of initial size and color for each ring such that we have all size between
a minimum and a maximum size and also to make sure the largest ring is almost
transparent.
size_min = 50
size_max = 50*50
P = np.random.uniform(0,1,(n,2))
C = np.ones((n,4)) * (0,0,0,1)
C[:,3] = np.linspace(0,1,n)
S = np.linspace(size_min, size_max, n)
scat = ax.scatter(P[:,0], P[:,1], s=S, lw = 0.5,
edgecolors = C, facecolors='None')
ax.set_xlim(0,1), ax.set_xticks([])
ax.set_ylim(0,1), ax.set_yticks([])
Now, we need to write the update function for our animation. We know that at
each time step each ring should grow be more transparent while largest ring
should be totally transparent and thus removed. Of course, we won't actually
remove the largest ring but re-use it to set a new ring at a new random
position, with nominal size and color. Hence, we keep the number of ring
def update(frame):
global P, C, S
C[:,3] = np.maximum(0, C[:,3] - 1.0/n)
S += (size_max - size_min) / n
i = frame % 50
P[i] = np.random.uniform(0,1,2)
S[i] = size_min
C[i,3] = 1
scat.set_edgecolors(C)
scat.set_sizes(S)
scat.set_offsets(P)
return scat,
Last step is to tell matplotlib to use this function as an update function for
the animation and display the result or save it as a movie:
animation = FuncAnimation(fig, update, interval=10, blit=True, frames=200)
plt.show()
Earthquakes
We'll now use the rain animation to visualize earthquakes on the planet from
the last 30 days. The USGS Earthquake Hazards Program is part of the National
Earthquake Hazards Reduction Program (NEHRP) and provides several data on their
. Those data are sorted according to
earthquakes magnitude, ranging from significant only down to all earthquakes,
major or minor. You would be surprised by the number of minor earthquakes
happening every hour on the planet. Since this would represent too much data
for us, we'll stick to earthquakes with magnitude & 4.5. At the time of writing,
this already represent more than 300 earthquakes in the last 30 days.
First step is to read and convert data. We'll use the urllib library that
allows to open and read remote data. Data on the website use the CSV format
whose content is given by the first line:
time,latitude,longitude,depth,mag,magType,nst,gap,dmin,rms,net,id,updated,place,type
T13:49:17.320Z,37.,4.01,mw,...
T07:47:06.640Z,-10.6,6.35,6.6,mwp,...
We are only interested in latitude, longitude and magnitude and we won't parse
time of event (ok, that's bad, feel free to send me a PR).
import urllib
from mpl_toolkits.basemap import Basemap
feed = &http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/&
url = urllib.urlopen(feed + &4.5_month.csv&)
data = url.read().split('\n')[+1:-1]
E = np.zeros(len(data), dtype=[('position',
float, 2),
('magnitude', float, 1)])
for i in range(len(data)):
row = data[i].split(',')
E['position'][i] = float(row[2]),float(row[1])
E['magnitude'][i] = float(row[4])
Now, we need to draw earth on a figure to show precisely where the earthquake
center is and to translate latitude/longitude in some coordinates matplotlib
can handle. Fortunately, there is the
project (that tends to be replaced by the
more complete ) that is really
simple to install and to use. First step is to define a projection to draw the
earth onto a screen (there exists many different projections) and we'll stick
to the mill projection which is rather standard for non-specialist like me.
fig = plt.figure(figsize=(14,10))
ax = plt.subplot(1,1,1)
earth = Basemap(projection='mill')
Next, we request to draw coastline and fill continents:
earth.drawcoastlines(color='0.50', linewidth=0.25)
earth.fillcontinents(color='0.95')
The earth object will also be used to translate coordinate quite
automatically. We are almost finished. Last step is to adapt the rain code and
put some eye candy:
P = np.zeros(50, dtype=[('position', float, 2),
float, 1),
('growth',
float, 1),
float, 4)])
scat = ax.scatter(P['position'][:,0], P['position'][:,1], P['size'], lw=0.5,
edgecolors = P['color'], facecolors='None', zorder=10)
def update(frame):
current = frame % len(E)
i = frame % len(P)
P['color'][:,3] = np.maximum(0, P['color'][:,3] - 1.0/len(P))
P['size'] += P['growth']
magnitude = E['magnitude'][current]
P['position'][i] = earth(*E['position'][current])
P['size'][i] = 5
P['growth'][i]= np.exp(magnitude) * 0.1
if magnitude & 6:
P['color'][i]
P['color'][i]
scat.set_edgecolors(P['color'])
scat.set_facecolors(P['color']*(1,1,1,0.25))
scat.set_sizes(P['size'])
scat.set_offsets(P['position'])
return scat,
animation = FuncAnimation(fig, update, interval=10)
plt.show()
If everything went well, you should obtain something like this (with animation):
Regular Plots
You need to use the
Starting from the code below, try to reproduce the graphic on the right taking
care of filled areas:
import numpy as np
import maplotlib.pyplot as plt
X = np.linspace(-np.pi,np.pi,n,endpoint=True)
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)
plt.show()
Click on figure for solution.
Scatter Plots
Color is given by angle of (X,Y).
Starting from the code below, try to reproduce the graphic on the right taking
care of marker size, color and transparency.
import numpy as np
import maplotlib.pyplot as plt
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)
plt.scatter(X,Y)
plt.show()
Click on figure for solution.
You need to take care of text alignment.
Starting from the code below, try to reproduce the graphic on the right by
adding labels for red bars.
import numpy as np
import maplotlib.pyplot as plt
X = np.arange(n)
Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
Y2 = (1-X/float(n)) * np.random.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)
plt.show()
Click on figure for solution.
Contour Plots
You need to use the
Starting from the code below, try to reproduce the graphic on the right taking
care of the colormap (see
import numpy as np
import maplotlib.pyplot as plt
def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
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)
plt.show()
Click on figure for solution.
You need to take care of the origin of the image in the imshow command and
Starting from the code below, try to reproduce the graphic on the right taking
care of colormap, image interpolation and origin.
import numpy as np
import maplotlib.pyplot as plt
def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
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))
plt.show()
Click on figure for solution.
Pie Charts
You need to modify Z.
Starting from the code below, try to reproduce the graphic on the right taking
care of colors and slices size.
import numpy as np
import maplotlib.pyplot as plt
Z = np.random.uniform(0,1,n)
plt.pie(Z)
plt.show()
Click on figure for solution.
Quiver Plots
You need to draw arrows twice.
Starting from the code above, try to reproduce the graphic on the right taking
care of colors and orientations.
import numpy as np
import maplotlib.pyplot as plt
X,Y = np.mgrid[0:n,0:n]
plt.quiver(X,Y)
plt.show()
Click on figure for solution.
Starting from the code below, try to reproduce the graphic on the right taking
care of line styles.
import numpy as np
import maplotlib.pyplot as plt
axes = gca()
axes.set_xlim(0,4)
axes.set_ylim(0,3)
axes.set_xticklabels([])
axes.set_yticklabels([])
plt.show()
Click on figure for solution.
Multi Plots
You can use several subplots with different partition.
Starting from the code below, try to reproduce the graphic on the right.
import numpy as np
import maplotlib.pyplot as plt
plt.subplot(2,2,1)
plt.subplot(2,2,3)
plt.subplot(2,2,4)
plt.show()
Click on figure for solution.
Polar Axis
You only need to modify the axes line
Starting from the code below, try to reproduce the graphic on the right.
import numpy as np
import maplotlib.pyplot as plt
plt.axes([0,0,1,1])
theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)
radii = 10*np.random.rand(N)
width = np.pi/4*np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)
for r,bar in zip(radii, bars):
bar.set_facecolor( cm.jet(r/10.))
bar.set_alpha(0.5)
plt.show()
Click on figure for solution.
You need to use
Starting from the code below, try to reproduce the graphic on the right.
import numpy as np
import maplotlib.pyplot as plt
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')
plt.show()
Click on figure for solution.
Have a look at the .
Try to do the same from scratch !
Click on figure for solution.
Matplotlib benefits from extensive documentation as well as a large
community of users and developpers. Here are some links of interest:
Introduction
Controlling line properties
Working with multiple figures and axes
Working with text
Startup commands
Importing image data into Numpy arrays
Plotting numpy arrays as images
Text introduction
Basic text commands
Text properties and layout
Writing mathematical expressions
Text rendering With LaTeX
Annotating text
Introduction
Customizing your objects
Object containers
Figure container
Axes container
Axis containers
Tick containers
Introduction
Bézier example
Compound paths
Introduction
Data coordinates
Axes coordinates
Blended transformations
Using offset transforms to create a shadow effect
The transformation pipeline
Matplotlib documentation
Installation
Troubleshooting
Environment Variables
Code documentation
The code is fairly well documented and you can quickly access a specific
command from within a python session:
&&& from pylab import *
&&& help(plot)
Help on function plot in module matplotlib.pyplot:
plot(*args, **kwargs)
Plot lines and/or markers to the
:class:`~matplotlib.axes.Axes`.
*args* is a variable length
argument, allowing for multiple *x*, *y* pairs with an
optional format string.
For example, each of the following is
plot(x, y)
# plot x and y using default line style and color
plot(x, y, 'bo')
# plot x and y using blue circle markers
# plot y using x as index array 0..N-1
plot(y, 'r+')
# ditto, but with red plusses
If *x* and/or *y* is 2-dimensional, then the corresponding columns
will be plotted.
also incredibly useful when you search how to render a given graphic. Each
example comes with its source.
A smaller gallery is also available .
Mailing lists
Finally, there is a
where you can
ask for help and a
that is more
technical.
Here is a set of tables that show main properties and styles.
Line properties
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
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
Line styles
solid line
dashed line
dash-dot line
dotted line
triangle up
triangle down
triangle left
triangle right
thin diamond
tripod down
tripod left
tripod right
rotated hexagon
vertical line
horizontal line
tick right
caret left
caret right
caret down
horizontal line
tripod down
tripod left
tripod right
triangle up
triangle down
triangle left
triangle right
thin diamond
vertical line
r'$\sqrt{2}$'
any latex expression
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, checks .
gist_earth
gist_rainbow
gist_stern
Sequential
Qualitative
Miscellaneous