这篇笔记整理自以下两篇文章：

• Pyplot tutorial
• Matplotlib tutorial

基本图像绘制

import matplotlib.pyplot as plt

plt.plot([1,2,3,4])
plt.ylabel("this is y label")
plt.show()

我们使用 plot 来绘制图像，使用 ylabel 来添加额外的轴标签信息，最后使用 show 来显示图像。
由于我们在 plot 函数中只给出 y 轴的值，而没有指定横轴刻度，所以默认从 0 开始以 1 自增。

plot 函数接收任意数量的参数，我们可以传入一个列表来指定 x 轴的刻度，并添加一个格式串来指定图像的绘制格式。关于可设定的格式，可以参考 plot()。

我们还可以使用 axis() 来接收一个列表 [xmin, xmax, ymin, ymax] ，用于限定横纵坐标轴的刻度范围。

import numpy as np
import matplotlib.pyplot as plt

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

通常情况下，我们传入一个 numpy 数组作为 plot 函数的参数。实际上，所有的序列都会在内部被转换为 numpy 数组。

import numpy as np
import matplotlib.pyplot as plt

t = np.arange(0., 5., 0.2)

plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'c^')
plt.show()

在这里，我们向 plot 函数传入了 3 组参数，按照（x轴坐标、y轴坐标、绘制格式）的顺序依次传入每组参数。

控制线条样式

绘制线条时，有多种属性可供我们设置：线条宽度（linewidth）, 线条样式（dash style）, 平滑处理（antialiased）等等。更多设定线条属性的方式，可以参考 matplotlib.lines.line2D。

• 使用关键字参数

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

• plot 返回一个 Line2D 列表对象，我们可以调用该 Line2D 实例的设定方法。

line = plt.plot(x, y, '-')
line.set_antialiased(False)

• 使用 setp 命令

lines = plt.plot(x1, y1, x2, y2)
plt.setp(lines, color='r', linewidth=2.0)

我们可以通过调用 setp 来获取能够设置的属性。

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

处理多个图像与坐标系

MATLAB 和 pyplot 中有当前图像和当前坐标系的概念。所有绘制命令都会应用于当前坐标系。
gca() 命令返回当前的坐标系（一个 matplotlib.axes.Axes 实例，而 gcf 命令则返回当前图像（一个 matplotlib.figure.Figure 实例）。

下面的代码创建两个子图（subplot）。

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()

此处 figure() 命令是可选的，因为默认会创建 figure(1)，就像当你没有手动指定任何坐标系时 subplot(111) 会被默认创建一样。subplot() 命令指定了 numrows、numcols、fignum，它们分别表示行数、列数、图像序号。其中 fignum 的取值从 1 到 numrows*numcols。如果 fignum < 10 的话，那么可以省略这三个参数之间的逗号。所以 subplot(211) 与 subplot(2, 1, 1) 等价。

我们可以创建任意数量的子图和坐标系，通过多次调用 figure() 来创建多幅图像。

import numpy as np
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()

import numpy as np
import matplotlib.pyplot as plt

# plot a line, implicitly creating a subplot(111)
plt.plot([1,2,3], 'r:')
# now create a subplot which represents the top plot of a grid
# with 2 rows and 1 column. Since this subplot will overlap the
# first, the plot (and its axes) previously created, will be removed
plt.figure(2)
plt.subplot(121)
plt.plot(range(12), 'bo', range(12), 'b')
plt.axis([0, 11, 0, 11])
plt.subplot(122) # creates 2nd subplot with yellow background 
plt.plot(range(10), [x**2 for x in range(10)], 'c--')
plt.show()

我们可以使用 clf() 来清除当前图像，使用 cla() 来清除当前坐标系。

如果你创建了很多图像，那么应该注意，这些图像所需要的空间只有在我们显式调用 close() 之后才会被释放，否则即使图像的窗口被关闭，pyplot 仍会保留内部的引用。

处理文本

text 命令可用于在任意位置添加文本，而 xlabel、ylabel 和 title 用于在它们所对应的位置添加文本。

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()

text() 命令返回一个 matplotlib.text.Text 对象。我们可以通过传入关键字参数或使用 setp() 函数来设定其属性。

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

更多有关属性的详细内容，可以参考 Text properties and layout。

在文本中使用数学表达式

matplotlib 的任何文本表达式都接受 TeX 表达式。例如，我们可以使用由一对美元符号包围的 TeX 表达式来让其显示在标题中：

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

matplotlib 内置了 TeX 表达式的分析器和布局引擎，因此我们能够跨平台使用 TeX 数学表达式而无需安装 TeX。

添加标注文本

在上面的示例中，我们使用 text() 来将文本放置在坐标系的任意位置。text 的通常用法是标注图像的某些特性，而 annotate() 提供了一些辅助函数来更方便地创建标注。在一个标注中，有两点需要考虑：标注显示位置 xy、文本显示位置 xytext。这两组参数都是 (x, y) 元组。

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()

在这个基本的示例中，xy（箭头）和 xytext 的位置都是在数据坐标系中。还有许多其它的坐标系统可以选择 —— 详细信息可查阅 Basic annotation 和 Advanced Annotation 。更多示例可以查看 pylab_examples example code: annotation_demo.py。

对数和其它非线性坐标轴

matplotlib.pyplot 不仅支持线性坐标轴刻度，还支持对数和分对数（logit）刻度。如果数据的变化范围非常巨大，我们通常会使用这些刻度。我们可以很容易的改变坐标轴的刻度：

plt.xscale('log')

下面是具有相同数据、不同 y 轴坐标刻度的四幅图像。

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.ticker import NullFormatter

np.random.seed(19680801)

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))

# linear
plt.figure(1)
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)

plt.gca().yaxis.set_minor_formatter(NullFormatter())

plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95,
                    hspace=0.25, wspace=0.35)

plt.show()

你也可以添加自己的刻度，具体信息可查阅 Developer's guid for creating scales and transformations。

绘图示例

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 256
X = np.linspace(-np.pi,np.pi,n,endpoint=True)
Y = np.sin(2*X)

plt.axes([0.025,0.025,0.95,0.95])

plt.plot (X, Y+1, color='blue', alpha=1.00)
plt.fill_between(X, 1, Y+1, color='blue', alpha=.25)

plt.plot (X, Y-1, color='blue', alpha=1.00)
plt.fill_between(X, -1, Y-1, (Y-1) > -1, color='blue', alpha=.25)
plt.fill_between(X, -1, Y-1, (Y-1) < -1, color='red',  alpha=.25)

plt.xlim(-np.pi,np.pi), plt.xticks([])
plt.ylim(-2.5,2.5), plt.yticks([])
# savefig('../figures/plot_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 1024
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)
T = np.arctan2(Y,X)

plt.axes([0.025,0.025,0.95,0.95])
plt.scatter(X,Y, s=75, c=T, alpha=.5)

plt.xlim(-1.5,1.5), plt.xticks([])
plt.ylim(-1.5,1.5), plt.yticks([])
# savefig('../figures/scatter_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 12
X = np.arange(n)
Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)

plt.axes([0.025,0.025,0.95,0.95])
plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x,y in zip(X,Y1):
    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')

for x,y in zip(X,Y2):
    plt.text(x+0.4, -y-0.05, '%.2f' % y, ha='center', va= 'top')

plt.xlim(-.5,n), plt.xticks([])
plt.ylim(-1.25,+1.25), plt.yticks([])

# savefig('../figures/bar_ex.png', dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

def f(x,y):
    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 256
x = np.linspace(-3,3,n)
y = np.linspace(-3,3,n)
X,Y = np.meshgrid(x,y)

plt.axes([0.025,0.025,0.95,0.95])

plt.contourf(X, Y, f(X,Y), 8, alpha=.75, cmap=plt.cm.hot)
C = plt.contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)
plt.clabel(C, inline=1, fontsize=10)

plt.xticks([]), plt.yticks([])
# savefig('../figures/contour_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

def f(x,y):
    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 10
x = np.linspace(-3,3,3.5*n)
y = np.linspace(-3,3,3.0*n)
X,Y = np.meshgrid(x,y)
Z = f(X,Y)

plt.axes([0.025,0.025,0.95,0.95])
plt.imshow(Z,interpolation='nearest', cmap='bone', origin='lower')
plt.colorbar(shrink=.92)

plt.xticks([]), plt.yticks([])
# savefig('../figures/imshow_ex.png', dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 20
Z = np.ones(n)
Z[-1] *= 2

plt.axes([0.025,0.025,0.95,0.95])

plt.pie(Z, explode=Z*.05, colors = ['%f' % (i/float(n)) for i in range(n)])
plt.gca().set_aspect('equal')
plt.xticks([]), plt.yticks([])

# savefig('../figures/pie_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 8
X,Y = np.mgrid[0:n,0:n]
T = np.arctan2(Y-n/2.0, X-n/2.0)
R = 10+np.sqrt((Y-n/2.0)**2+(X-n/2.0)**2)
U,V = R*np.cos(T), R*np.sin(T)

plt.axes([0.025,0.025,0.95,0.95])
plt.quiver(X,Y,U,V,R, alpha=.5)
plt.quiver(X,Y,U,V, edgecolor='k', facecolor='None', linewidth=.5)

plt.xlim(-1,n), plt.xticks([])
plt.ylim(-1,n), plt.yticks([])

# savefig('../figures/quiver_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

ax = plt.axes([0.025,0.025,0.95,0.95])

ax.set_xlim(0,4)
ax.set_ylim(0,3)
ax.xaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.yaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.yaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.grid(which='major', axis='x', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='x', linewidth=0.25, linestyle='-', color='0.75')
ax.grid(which='major', axis='y', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='y', linewidth=0.25, linestyle='-', color='0.75')
ax.set_xticklabels([])
ax.set_yticklabels([])

# savefig('../figures/grid_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
fig.subplots_adjust(bottom=0.025, left=0.025, top = 0.975, right=0.975)

plt.subplot(2,1,1)
plt.xticks([]), plt.yticks([])

plt.subplot(2,3,4)
plt.xticks([]), plt.yticks([])

plt.subplot(2,3,5)
plt.xticks([]), plt.yticks([])

plt.subplot(2,3,6)
plt.xticks([]), plt.yticks([])

# plt.savefig('../figures/multiplot_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

ax = plt.axes([0.025,0.025,0.95,0.95], polar=True)

N = 20
theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)
radii = 10*np.random.rand(N)
width = np.pi/4*np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)

for r,bar in zip(radii, bars):
    bar.set_facecolor( plt.cm.jet(r/10.))
    bar.set_alpha(0.5)

ax.set_xticklabels([])
ax.set_yticklabels([])
# savefig('../figures/polar_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.hot)
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot)
ax.set_zlim(-2,2)

# savefig('../figures/plot3d_ex.png',dpi=48)
plt.show()

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

eqs = []
eqs.append((r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$"))
eqs.append((r"$\frac{d\rho}{d t} + \rho \vec{v}\cdot\nabla\vec{v} = -\nabla p + \mu\nabla^2 \vec{v} + \rho \vec{g}$"))
eqs.append((r"$\int_{-\infty}^\infty e^{-x^2}dx=\sqrt{\pi}$"))
eqs.append((r"$E = mc^2 = \sqrt{{m_0}^2c^4 + p^2c^2}$"))
eqs.append((r"$F_G = G\frac{m_1m_2}{r^2}$"))

plt.axes([0.025,0.025,0.95,0.95])

for i in range(24):
    index = np.random.randint(0,len(eqs))
    eq = eqs[index]
    size = np.random.uniform(12,32)
    x,y = np.random.uniform(0,1,2)
    alpha = np.random.uniform(0.25,.75)
    plt.text(x, y, eq, ha='center', va='center', color="#11557c", alpha=alpha,
             transform=plt.gca().transAxes, fontsize=size, clip_on=True)

plt.xticks([]), plt.yticks([])
# savefig('../figures/text_ex.png',dpi=48)
plt.show()

作者：Wray Zheng
原文：http://www.codebelief.com/article/2017/04/python-data-visualization-matplotlib-learning/

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