matplotlib.pyplot is a collection of command style functions
that make matplotlib work like MATLAB.
Each pyplot function makes
some change to a figure: e.g., creates a figure, creates a plotting area
in a figure, plots some lines in a plotting area, decorates the plot
with labels, etc. In matplotlib.pyplot various states are preserved
across function calls, so that it keeps track of things like
the current figure and plotting area, and the plotting
functions are directed to the current axes (please note that “axes” here
and in most places in the documentation refers to the axes
part of a figure
and not the strict mathematical term for more than one axis).
import matplotlib.pyplot as plt
plt.plot([1,2,3,4])
plt.ylabel('some numbers')
plt.show()
(Source code, png, pdf)
You may be wondering why the x-axis ranges from 0-3 and the y-axis
from 1-4. If you provide a single list or array to the
plot() command, matplotlib assumes it is a
sequence of y values, and automatically generates the x values for
you. Since python ranges start with 0, the default x vector has the
same length as y but starts with 0. Hence the x data are
[0,1,2,3].
plot() is a versatile command, and will take
an arbitrary number of arguments. For example, to plot x versus y,
you can issue the command:
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is ‘b-‘, which is a solid blue line. For example, to plot the above with red circles, you would issue
import matplotlib.pyplot as plt
plt.plot([1,2,3,4], [1,4,9,16], 'ro')
plt.axis([0, 6, 0, 20])
plt.show()
(Source code, png, pdf)
See the plot() documentation for a complete
list of line styles and format strings. The
axis() command in the example above takes a
list of [xmin, xmax, ymin, ymax] and specifies the viewport of the
axes.
If matplotlib were limited to working with lists, it would be fairly useless for numeric processing. Generally, you will use numpy arrays. In fact, all sequences are converted to numpy arrays internally. The example below illustrates a plotting several lines with different format styles in one command using arrays.
import numpy as np
import matplotlib.pyplot as plt
# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)
# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()
(Source code, png, pdf)
Lines have many attributes that you can set: linewidth, dash style,
antialiased, etc; see matplotlib.lines.Line2D. There are
several ways to set line properties
Use keyword args:
plt.plot(x, y, linewidth=2.0)
Use the setter methods of a Line2D instance. plot returns a list
of Line2D objects; e.g., line1, line2 = plot(x1, y1, x2, y2). In the code
below we will suppose that we have only
one line so that the list returned is of length 1. We use tuple unpacking with
line, to get the first element of that list:
line, = plt.plot(x, y, '-')
line.set_antialiased(False) # turn off antialising
Use the setp() command. The example below
uses a MATLAB-style command to set multiple properties
on a list of lines. setp works transparently with a list of objects
or a single object. You can either use python keyword arguments or
MATLAB-style string/value pairs:
lines = plt.plot(x1, y1, x2, y2)
# use keyword args
plt.setp(lines, color='r', linewidth=2.0)
# or MATLAB style string value pairs
plt.setp(lines, 'color', 'r', 'linewidth', 2.0)
Here are the available Line2D properties.
| Property | Value Type |
|---|---|
| alpha | float |
| animated | [True | False] |
| antialiased or aa | [True | False] |
| clip_box | a matplotlib.transform.Bbox instance |
| clip_on | [True | False] |
| clip_path | a Path instance and a Transform instance, a Patch |
| color or c | any matplotlib color |
| contains | the hit testing function |
| dash_capstyle | ['butt' | 'round' | 'projecting'] |
| dash_joinstyle | ['miter' | 'round' | 'bevel'] |
| dashes | sequence of on/off ink in points |
| data | (np.array xdata, np.array ydata) |
| figure | a matplotlib.figure.Figure instance |
| label | any string |
| linestyle or ls | [ '-' | '--' | '-.' | ':' | 'steps' | ...] |
| linewidth or lw | float value in points |
| lod | [True | False] |
| marker | [ '+' | ',' | '.' | '1' | '2' | '3' | '4' ] |
| markeredgecolor or mec | any matplotlib color |
| markeredgewidth or mew | float value in points |
| markerfacecolor or mfc | any matplotlib color |
| markersize or ms | float |
| markevery | [ None | integer | (startind, stride) ] |
| picker | used in interactive line selection |
| pickradius | the line pick selection radius |
| solid_capstyle | ['butt' | 'round' | 'projecting'] |
| solid_joinstyle | ['miter' | 'round' | 'bevel'] |
| transform | a matplotlib.transforms.Transform instance |
| visible | [True | False] |
| xdata | np.array |
| ydata | np.array |
| zorder | any number |
To get a list of settable line properties, call the
setp() function with a line or lines
as argument
In [69]: lines = plt.plot([1, 2, 3])
In [70]: plt.setp(lines)
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
...snip
MATLAB, and pyplot, have the concept of the current
figure and the current axes. All plotting commands apply to the
current axes. The function gca() returns the
current axes (a matplotlib.axes.Axes instance), and
gcf() returns the current figure
(matplotlib.figure.Figure instance). Normally, you don’t have
to worry about this, because it is all taken care of behind the
scenes. Below is a script to create two subplots.
import numpy as np
import matplotlib.pyplot as plt
def f(t):
return np.exp(-t) * np.cos(2*np.pi*t)
t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)
plt.figure(1)
plt.subplot(211)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()
(Source code, png, pdf)
The figure() command here is optional because
figure(1) will be created by default, just as a subplot(111)
will be created by default if you don’t manually specify any axes. The
subplot() command specifies numrows,
numcols, fignum where fignum ranges from 1 to
numrows*numcols. The commas in the subplot command are
optional if numrows*numcols<10. So subplot(211) is identical
to subplot(2, 1, 1). You can create an arbitrary number of subplots
and axes. If you want to place an axes manually, i.e., not on a
rectangular grid, use the axes() command,
which allows you to specify the location as axes([left, bottom,
width, height]) where all values are in fractional (0 to 1)
coordinates. See pylab_examples example code: axes_demo.py for an example of
placing axes manually and pylab_examples example code: subplots_demo.py for an
example with lots of subplots.
You can create multiple figures by using multiple
figure() calls with an increasing figure
number. Of course, each figure can contain as many axes and subplots
as your heart desires:
import matplotlib.pyplot as plt
plt.figure(1) # the first figure
plt.subplot(211) # the first subplot in the first figure
plt.plot([1, 2, 3])
plt.subplot(212) # the second subplot in the first figure
plt.plot([4, 5, 6])
plt.figure(2) # a second figure
plt.plot([4, 5, 6]) # creates a subplot(111) by default
plt.figure(1) # figure 1 current; subplot(212) still current
plt.subplot(211) # make subplot(211) in figure1 current
plt.title('Easy as 1, 2, 3') # subplot 211 title
You can clear the current figure with clf()
and the current axes with cla(). If you find
it annoying that states (specifically the current image, figure and axes)
are being maintained for you behind the scenes, don’t despair: this is just a thin
stateful wrapper around an object oriented API, which you can use
instead (see Artist tutorial)
If you are making lots of figures, you need to be aware of one
more thing: the memory required for a figure is not completely
released until the figure is explicitly closed with
close(). Deleting all references to the
figure, and/or using the window manager to kill the window in which
the figure appears on the screen, is not enough, because pyplot
maintains internal references until close()
is called.
The text() command can be used to add text in
an arbitrary location, and the xlabel(),
ylabel() and title()
are used to add text in the indicated locations (see Text introduction
for a more detailed example)
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()
(Source code, png, pdf)
All of the text() commands return an
matplotlib.text.Text instance. Just as with with lines
above, you can customize the properties by passing keyword arguments
into the text functions or using setp():
t = plt.xlabel('my data', fontsize=14, color='red')
These properties are covered in more detail in Text properties and layout.
matplotlib accepts TeX equation expressions in any text expression.
For example to write the expression 
in the title,
you can write a TeX expression surrounded by dollar signs:
plt.title(r'$\sigma_i=15$')
The r preceding the title string is important – it signifies
that the string is a raw string and not to treat backslashes as
python escapes. matplotlib has a built-in TeX expression parser and
layout engine, and ships its own math fonts – for details see
Writing mathematical expressions. Thus you can use mathematical text across platforms
without requiring a TeX installation. For those who have LaTeX and
dvipng installed, you can also use LaTeX to format your text and
incorporate the output directly into your display figures or saved
postscript – see Text rendering With LaTeX.
The uses of the basic text() command above
place text at an arbitrary position on the Axes. A common use for
text is to annotate some feature of the plot, and the
annotate() method provides helper
functionality to make annotations easy. In an annotation, there are
two points to consider: the location being annotated represented by
the argument xy and the location of the text xytext. Both of
these arguments are (x,y) tuples.
import numpy as np
import matplotlib.pyplot as plt
ax = plt.subplot(111)
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = plt.plot(t, s, lw=2)
plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05),
)
plt.ylim(-2,2)
plt.show()
(Source code, png, pdf)
In this basic example, both the xy (arrow tip) and xytext
locations (text location) are in data coordinates. There are a
variety of other coordinate systems one can choose – see
Basic annotation and Advanced Annotation for
details. More examples can be found in
pylab_examples example code: annotation_demo.py.
matplotlib.pyplot supports not only linear axis scales, but also
logarithmic and logit scales. This is commonly used if data spans many orders
of magnitude. Changing the scale of an axis is easy:
plt.xscale(‘log’)
An example of four plots with the same data and different scales for the y axis is shown below.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter # useful for `logit` scale
# Fixing random state for reproducibility
np.random.seed(19680801)
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))
# plot with various axes scales
plt.figure(1)
# linear
plt.subplot(221)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)
# log
plt.subplot(222)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)
# symmetric log
plt.subplot(223)
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthreshy=0.01)
plt.title('symlog')
plt.grid(True)
# logit
plt.subplot(224)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
# Format the minor tick labels of the y-axis into empty strings with
# `NullFormatter`, to avoid cumbering the axis with too many labels.
plt.gca().yaxis.set_minor_formatter(NullFormatter())
# Adjust the subplot layout, because the logit one may take more space
# than usual, due to y-tick labels like "1 - 10^{-3}"
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
wspace=0.35)
plt.show()
(Source code, png, pdf)
It is also possible to add your own scale, see Developer’s guide for creating scales and transformations for details.
