`matplotlib.pyplot` is a collection of command style functions
that make matplotlib work like MATLAB.
Each `pyplot` function makes
some change to a figure: eg, create a figure, create a plotting area
in a figure, plot some lines in a plotting area, decorate the plot
with labels, etc.... `matplotlib.pyplot` is stateful, in that it
keeps track of the current figure and plotting area, and the plotting
functions are directed to the current axes

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

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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])
```

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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^')
```

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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 the

`Line2D`instance.`plot`returns a list of lines; eg`line1, line2 = plot(x1,y1,x2,x2)`. Below I have only one line so it is a list of length 1. I use tuple unpacking in the`line, = plot(x, y, 'o')`to get the first element of the 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--')
```

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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 an 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, ie, 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: line_styles.py* for an
example with lots-o-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
this statefulness, annoying, 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 a long sequence 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
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)
```

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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` preceeding the title string is important – it signifies
that the string is a *raw* string and not to treate backslashes and
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 case of
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()
```

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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
*Annotating text* and *Annotating Axes* for
details. More examples can be found in
*pylab_examples example code: annotation_demo.py*.