Pyplot tutorial

An introduction to the pyplot interface.

Intro to pyplot

matplotlib.pyplotopen in new window 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.pyplotopen in new window 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 axespart of a figure and not the strict mathematical term for more than one axis).


the pyplot API is generally less-flexible than the object-oriented API. Most of the function calls you see here can also be called as methods from an Axes object. We recommend browsing the tutorials and examples to see how this works.

Generating visualizations with pyplot is very quick:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')


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()open in new window 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()open in new window 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])


Formatting the style of your plot

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

plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')
plt.axis([0, 6, 0, 20])


See the plot()open in new window documentation for a complete list of line styles and format strings. The axis()open in new window 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 numpyopen in new window 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

# 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^')


Plotting with keyword strings

There are some instances where you have data in a format that lets you access particular variables with strings. For example, with numpy.recarrayopen in new window or pandas.DataFrameopen in new window.

Matplotlib allows you provide such an object with the data keyword argument. If provided, then you may generate plots with the strings corresponding to these variables.

data = {'a': np.arange(50),
        'c': np.random.randint(0, 50, 50),
        'd': np.random.randn(50)}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100

plt.scatter('a', 'b', c='c', s='d', data=data)
plt.xlabel('entry a')
plt.ylabel('entry b')


Plotting with categorical variables

It is also possible to create a plot using categorical variables. Matplotlib allows you to pass categorical variables directly to many plotting functions. For example:

names = ['group_a', 'group_b', 'group_c']
values = [1, 10, 100]

plt.figure(figsize=(9, 3))

plt.subplot(131), values)
plt.scatter(names, values)
plt.plot(names, values)
plt.suptitle('Categorical Plotting')


Controlling line properties

Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see matplotlib.lines.Line2Dopen in new window. 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 antialiasing
  • Use the setp()open in new window 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 Line2Dopen in new window 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

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()open in new window 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]

Working with multiple figures and axes

MATLAB, and pyplotopen in new window, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function gca()open in new window returns the current axes (a matplotlib.axes.Axesopen in new window instance), and gcf()open in new window returns the current figure (matplotlib.figure.Figureopen in new window 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.

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.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

plt.plot(t2, np.cos(2*np.pi*t2), 'r--')


The figure()open in new window 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()open in new window command specifies numrows, numcols, plot_number where plot_number 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()open in new window 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 Axes Demoopen in new window for an example of placing axes manually and Basic Subplot Demoopen in new window for an example with lots of subplots.

You can create multiple figures by using multiple figure()open in new window 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()open in new window and the current axes with cla()open in new window. 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 tutorialopen in new window)

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()open in new window. 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()open in new window is called.

Working with text

The text()open in new window command can be used to add text in an arbitrary location, and the xlabel()open in new window, ylabel()open in new window and title()open in new window are used to add text in the indicated locations (see Text in Matplotlib Plotsopen in new window for a more detailed example)

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)

plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])


All of the text()open in new window commands return an matplotlib.text.Textopen in new window instance. Just as with with lines above, you can customize the properties by passing keyword arguments into the text functions or using setp()open in new window:

t = plt.xlabel('my data', fontsize=14, color='red')

These properties are covered in more detail in Text properties and layoutopen in new window.

Using mathematical expressions in text

matplotlib accepts TeX equation expressions in any text expression. For example to write the expression (\sigma_i=15) in the title, you can write a TeX expression surrounded by dollar signs:


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 expressionsopen in new window. 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 LaTeXopen in new window.

Annotating text

The uses of the basic text()open in new window 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()open in new window 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.

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)


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 annotationopen in new window and Advanced Annotationopen in new window for details. More examples can be found in Annotating Plotsopen in new window.

Logarithmic and other nonlinear axes

matplotlib.pyplotopen in new window 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:

An example of four plots with the same data and different scales for the y axis is shown below.

from matplotlib.ticker import NullFormatter  # useful for `logit` scale

# Fixing random state for reproducibility

# 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)]
x = np.arange(len(y))

# plot with various axes scales

# linear
plt.plot(x, y)

# log
plt.plot(x, y)

# symmetric log
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthreshy=0.01)

# logit
plt.plot(x, y)
# Format the minor tick labels of the y-axis into empty strings with
# `NullFormatter`, to avoid cumbering the axis with too many labels.
# 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,


It is also possible to add your own scale, see Developer's guide for creating scales and transformationsopen in new window for details.

Total running time of the script: ( 0 minutes 1.262 seconds)