plt.plot(x, y, linewidth=2.0)

2、利用setter方法

line1, line2 = plot(x1,y1,x2,y2)

line.set_antialiased(False) # turn off antialising

3、使用 setp() 命令

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.

lines = plt.plot([1,2,3])

plt.setp(lines)

  alpha: float

  animated: [True | False]

  antialiased or aa: [True | False]

  ...snip

以上为调用setp()第二种方法。

Working with multiple figures and axes

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 (amatplotlib.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.

gca()方法返回当前轴（一个matplotlib.axes.axes实例）。和gcf()方法返回当前图形（matplotlib.figure.figure实例）。通常，你不用操心这个，由于它是幕后自己主动管理的。以下是一个脚本来创建两个图。

# -*- coding: utf-8 -*-

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()

结果为：

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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. Thesubplot() 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.
Sosubplot(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 theaxes() 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:

这里的figure()指令是可选的由于figure(1)默认会被创建，就像subplot(111)将默认创建当你不手动指定axes的情况下。该subplot()命令指定numrows，numcols，fignum范围从1到numrows
* numcols【即211为2行1列第1幅图。和MATLAB同样】。

假设numrows * numcols＜10，subplot()命令中的逗号是可选的。您能够创建随意数量的subplots和axes。假设你想手动设置一个axes，能够使用axes()命令，它同意你指定的位置为axes([left, bottom, width, height])。全部的值都是分数（0～1）坐标。

# -*- coding: utf-8 -*-

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

plt.show()

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.

Working with text

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)

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# -*- coding: utf-8 -*-

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(u'概率', fontproperties='SimHei')

plt.title(u'IQ直方图', fontproperties='SimHei')

plt.text(60, .025, r'$\mu=100,\ \sigma=15$')

plt.axis([40, 160, 0, 0.03])

plt.grid(True)

plt.show()

结果例如以下所看到的：

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.

plt.title(r'$\sigma_i=15$')

# -*- coding: utf-8 -*-

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()

结果为：

from matplotlib import pyplot as plt

from numpy import sin, exp,  absolute, pi, arange

from numpy.random import normal

def f(t):

    s1 = sin(2 * pi * t)

    e1 = exp(-t)

    return absolute((s1 * e1)) + .05

t = arange(0.0, 5.0, 0.1)

s = f(t)

nse = normal(0.0, 0.3, t.shape) * s

fig = plt.figure(figsize=(12, 6))

vax = fig.add_subplot(121)

hax = fig.add_subplot(122)

vax.plot(t, s + nse, 'b^')

vax.vlines(t, [0], s)

vax.set_xlabel('time (s)')

vax.set_title('Vertical lines demo')

hax.plot(s + nse, t, 'b^')

hax.hlines(t, [0], s, lw=2)

hax.set_xlabel('time (s)')

hax.set_title('Horizontal lines demo')

plt.show()

结果为：

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import numpy as np

import matplotlib.pyplot as plt

N = 50

x = np.random.rand(N)

y = np.random.rand(N)

area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses

plt.scatter(x, y, s=area, alpha=0.5)

plt.show()

结果为：

未完待续。

。

。随时更新。

欢迎提问。共同学习，一起进步。

本文由@The_Third_Wave（Blog地址：http://blog.csdn.net/zhanh1218）原创。不定期更新，有错误请指正。

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