# # Pyplot tutorial

An introduction to the pyplot interface.

## # Intro to pyplot

`matplotlib.pyplot`

(opens 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.pyplot`

(opens 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 *axes* part of a figure
and not the strict mathematical term for more than one axis).

Note

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')
plt.show()
```

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()`

(opens 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()`

(opens 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])
plt.show()
```

See the `plot()`

(opens new window) documentation for a complete
list of line styles and format strings. The
`axis()`

(opens 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 numpy (opens 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^')
plt.show()
```

## # 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.recarray`

(opens new window) or `pandas.DataFrame`

(opens 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')
plt.show()
```

## # 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)
plt.bar(names, values)
plt.subplot(132)
plt.scatter(names, values)
plt.subplot(133)
plt.plot(names, values)
plt.suptitle('Categorical Plotting')
plt.show()
```

## # Controlling line properties

Lines have many attributes that you can set: linewidth, dash style,
antialiased, etc; see `matplotlib.lines.Line2D`

(opens 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()`

(opens 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 `Line2D`

(opens 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()`

(opens 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]
...snip
```

## # Working with multiple figures and axes

MATLAB, and `pyplot`

(opens new window), have the concept of the current
figure and the current axes. All plotting commands apply to the
current axes. The function `gca()`

(opens new window) returns the
current axes (a `matplotlib.axes.Axes`

(opens new window) instance), and
`gcf()`

(opens new window) returns the current figure
(`matplotlib.figure.Figure`

(opens 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.figure()
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()
```

The `figure()`

(opens 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()`

(opens 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()`

(opens 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 Demo (opens new window) for an example of
placing axes manually and Basic Subplot Demo (opens new window) for an
example with lots of subplots.

You can create multiple figures by using multiple
`figure()`

(opens 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()`

(opens new window)
and the current axes with `cla()`

(opens 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 tutorial (opens 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()`

(opens 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()`

(opens new window)
is called.

## # Working with text

The `text()`

(opens new window) command can be used to add text in
an arbitrary location, and the `xlabel()`

(opens new window),
`ylabel()`

(opens new window) and `title()`

(opens new window)
are used to add text in the indicated locations (see Text in Matplotlib Plots (opens 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.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()
```

All of the `text()`

(opens new window) commands return an
`matplotlib.text.Text`

(opens 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()`

(opens new window):

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

These properties are covered in more detail in Text properties and layout (opens 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:

```
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 (opens 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 LaTeX (opens new window).

### # Annotating text

The uses of the basic `text()`

(opens 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()`

(opens 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)
plt.show()
```

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 (opens new window) and Advanced Annotation (opens new window) for
details. More examples can be found in
Annotating Plots (opens new window).

## # Logarithmic and other nonlinear axes

`matplotlib.pyplot`

(opens 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
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()
# 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()
```

It is also possible to add your own scale, see Developer's guide for creating scales and transformations (opens new window) for details.

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