目录

1 Matplotlib 简介

-----1.1 为什么用Matplotlib？

-----1.2 Matplotlib 安装

2 基本使用

-----2.1 基本用法

-----2.2 figure 图像

-----2.3 设置坐标轴1

-----2.4 设置坐标轴2

-----2.5 Legend 图例

-----2.6 Annotation 标注

-----2.7 tick 能见度

3 画图种类

-----3.1 Scatter 散点图

-----3.2 Bar 柱状图

-----3.3 Contours 等高线图

-----3.4 Image 图片

-----3.5 3D 数据

4 多图合并显示

-----4.1 Subplot 多合一显示

-----4.2 Subplot 分格显示

-----4.3 图中图

-----4.4 次坐标轴

5 动画

-----5.1 Animation 动画

****************************************本文参考****************************************Matplotlib 画图 | 莫烦Python​mofanpy.com

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1 Matplotlib 简介

1.1 为什么用Matplotlib？

Matplotlib是一个非常强大的Python画图工具，可以画出美丽的线图、散点图、等高线图、条形图、柱状图、3D图形，甚至是图形动画等等。

1.2 Matplotlib 安装

打开Pycharm，点击菜单上的“file”，再点击“setting”，选中你的项目，选中其下的“Project Interpreter”。出现界面后，点击最右边的“+”。出现界面后，搜索matplotlib，选中，选择版本，点击“Install Package”进行下载。

2 基本使用

2.1 基本用法

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-1, 1, 50)

y = 2*x+1

plt.plot(x, y)

plt.show()

得到

2.2 figure 图像

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-3, 3, 50)

y1 = 2*x+1

y2 = x**2

plt.figure()

plt.plot(x, y1)

plt.figure(num=3, figsize=(8, 5))

plt.plot(x, y2)

plt.plot(x, y1, color='red', linewidth=3, linestyle='--')

plt.show()

得到

2.3 设置坐标轴1

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-3, 3, 50)

y1 = 2*x+1

y2 = x**2

plt.figure()

plt.plot(x, y1)

plt.figure(num=3, figsize=(8, 5))

plt.plot(x, y2)

plt.plot(x, y1, color='red', linewidth=1, linestyle='--')

plt.xlim((-1, 2))

plt.ylim((-2, 3))

plt.xlabel('I am x')

plt.ylabel('I am y')

new_ticks = np.linspace(-1, 2, 5)

plt.xticks(new_ticks)

plt.yticks([-2, -1.8, -1, 1.22, 3],

[r'$really\ bad$', r'$bad$', r'$normal\ \alpha$', r'$good$', r'$really\ good$'])

plt.show()

得到

2.4 设置坐标轴2

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-3, 3, 50)

y1 = 2*x+1

y2 = x**2

plt.figure()

plt.plot(x, y2)

plt.plot(x, y1, color='red', linewidth=1, linestyle='--')

plt.xlim((-1, 2))

plt.ylim((-2, 3))

plt.xlabel('I am x')

plt.ylabel('I am y')

new_ticks = np.linspace(-1, 2, 5)

plt.xticks(new_ticks)

plt.yticks([-2, -1.8, -1, 1.22, 3],

[r'$really\ bad$', r'$bad$', r'$normal\ \alpha$', r'$good$', r'$really\ good$'])

ax = plt.gca()

ax.spines['right'].set_color('none')

ax.spines['top'].set_color('none')

ax.xaxis.set_ticks_position('bottom')

ax.yaxis.set_ticks_position('left')

ax.spines['bottom'].set_position(('data', 0))

ax.spines['left'].set_position(('data', 0))

plt.show()

得到

2.5 Legend 图例

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-3, 3, 50)

y1 = 2*x+1

y2 = x**2

plt.figure()

plt.xlim((-1, 2))

plt.ylim((-2, 3))

plt.xlabel('I am x')

plt.ylabel('I am y')

new_ticks = np.linspace(-1, 2, 5)

plt.xticks(new_ticks)

plt.yticks([-2, -1.8, -1, 1.22, 3],

[r'$really\ bad$', r'$bad$', r'$normal\ \alpha$', r'$good$', r'$really\ good$'])

l1, = plt.plot(x, y2, label='up')

l2, = plt.plot(x, y1, color='red', linewidth=1, linestyle='--', label='down')

plt.legend(handles = [l1, l2,], labels = ['aaa', 'bbb'], loc='best') #loc: lower right, upper left, etc.

plt.show()

得到

2.6 Annotation 标注

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-3, 3, 50)

y = 2*x + 1

plt.figure(num=1, figsize=(8, 5))

plt.plot(x, y)

ax = plt.gca()

ax.spines['right'].set_color('none')

ax.spines['top'].set_color('none')

ax.xaxis.set_ticks_position('bottom')

ax.spines['bottom'].set_position(('data', 0))

ax.yaxis.set_ticks_position('left')

ax.spines['left'].set_position(('data', 0))

x0 = 1

y0 = 2*x0 + 1

plt.scatter(x0, y0, s=50, color='b')

plt.plot([x0, x0], [0, y0], 'k--', linewidth=2.5)

plt.annotate(r'$2x+1=%s$' % y0, xy=(x0, y0), xycoords='data', xytext=(+30, -30),

textcoords='offset points', fontsize=16,

arrowprops=dict(arrowstyle='->', connectionstyle="arc3,rad=.2"))

plt.text(-3.7, 3, r'$This\ is\ the\ some\ text. \mu\ \sigma_i\ \alpha_t$',

fontdict={'size': 16, 'color': 'r'})

plt.show()

得到

2.7 tick 能见度

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-3, 3, 50)

y = 0.1*x

plt.figure()

plt.plot(x, y, linewidth=10, zorder=1) # 在 plt 2.0.2 或更高的版本中, 设置 zorder 给 plot 在 z 轴方向排序

plt.ylim(-2, 2)

ax = plt.gca()

ax.spines['right'].set_color('none')

ax.spines['top'].set_color('none')

ax.xaxis.set_ticks_position('bottom')

ax.spines['bottom'].set_position(('data', 0))

ax.yaxis.set_ticks_position('left')

ax.spines['left'].set_position(('data', 0))

for label in ax.get_xticklabels() + ax.get_yticklabels():

label.set_fontsize(12)

label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.7, zorder=2))

# 在 plt 2.0.2 或更高的版本中, 设置 zorder 给 plot 在 z 轴方向排序

plt.show()

得到

3 画图种类

3.1 Scatter 散点图

import matplotlib.pyplot as plt

import numpy as np

n = 1024 # data size

X = np.random.normal(0, 1, n) # 每一个点的X值

Y = np.random.normal(0, 1, n) # 每一个点的Y值

T = np.arctan2(Y, X) # for color value

plt.scatter(X, Y, s=75, c=T, alpha=0.5)

plt.xlim(-1.5, 1.5)

plt.xticks(()) # ignore xticks

plt.ylim(-1.5, 1.5)

plt.yticks(()) # ignore yticks

plt.show()

得到

3.2 Bar 柱状图

import matplotlib.pyplot as plt

import numpy as np

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.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')

plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x, y in zip(X, Y1):

# ha: horizontal alignment

# va: vertical alignment

plt.text(x, y + 0.05, '%.2f' % y, ha='center', va='bottom')

for x, y in zip(X, Y2):

# ha: horizontal alignment

# va: vertical alignment

plt.text(x, -y - 0.05, '%.2f' % y, ha='center', va='top')

plt.xlim(-0.5, n)

plt.xticks(())

plt.ylim(-1.25, 1.25)

plt.yticks(())

plt.show()

得到

3.3 Contours 等高线图

import matplotlib.pyplot as plt

import numpy as np

def f(x, y):

# the height function

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)

# use plt.contourf to filling contours

# X, Y and value for (X,Y) point

plt.contourf(X, Y, f(X, Y), 8, alpha=0.75, cmap=plt.cm.hot)

# use plt.contour to add contour lines

C = plt.contour(X, Y, f(X, Y), 8, colors='black')

plt.clabel(C, inline=True, fontsize=10)

plt.xticks(())

plt.yticks(())

plt.show()

得到

3.4 Image 图片

import matplotlib.pyplot as plt

import numpy as np

a = np.array([0.313660827978, 0.365348418405, 0.423733120134,

0.365348418405, 0.439599930621, 0.525083754405,

0.423733120134, 0.525083754405, 0.651536351379]).reshape(3, 3)

plt.imshow(a, interpolation='nearest', cmap='bone', origin='upper')

plt.colorbar(shrink=0.92)

plt.xticks(())

plt.yticks(())

plt.show()

得到

3.5 3D 数据

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

ax = Axes3D(fig)

# X, Y value

x = np.arange(-4, 4, 0.25)

y = np.arange(-4, 4, 0.25)

X, Y = np.meshgrid(x, y) # x-y 平面的网格

R = np.sqrt(X ** 2 + Y ** 2)

# height value

Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'), edgecolor='black')

ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.get_cmap('rainbow'))

ax.set_zlim(-2, 2)

plt.show()

得到

4 多图合并显示

4.1 Subplot 多合一显示

(1)样式一

import matplotlib.pyplot as plt

plt.figure()

plt.subplot(2, 2, 1)

plt.plot([0, 1], [0, 1])

plt.subplot(2, 2, 2)

plt.plot([0, 1], [0, 2])

plt.subplot(223)

plt.plot([0, 1], [0, 3])

plt.subplot(224)

plt.plot([0, 1], [0, 4])

plt.show()

得到

(2)样式二

import matplotlib.pyplot as plt

plt.figure()

plt.subplot(2, 1, 1)

plt.plot([0, 1], [0, 1])

plt.subplot(2, 3, 4)

plt.plot([0, 1], [0, 2])

plt.subplot(235)

plt.plot([0, 1], [0, 3])

plt.subplot(236)

plt.plot([0, 1], [0, 4])

plt.show()

得到

4.2 Subplot 分格显示

(1)样式一

import matplotlib.pyplot as plt

plt.figure()

ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3, rowspan=1)

ax1.plot([1, 2], [1, 2]) # 画小图

ax1.set_title('ax1_title') # 设置小图的标题

ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=1)

ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2, colspan=1)

ax4 = plt.subplot2grid((3, 3), (2, 0), rowspan=1, colspan=1)

ax5 = plt.subplot2grid((3, 3), (2, 1), rowspan=1, colspan=1)

ax4.scatter([1, 2], [2, 2])

ax4.set_xlabel('ax4_x')

ax4.set_ylabel('ax4_y')

plt.show()

得到

(2)样式二

import matplotlib.pyplot as plt

import matplotlib.gridspec as gridspec

plt.figure()

gs = gridspec.GridSpec(3, 3)

ax1 = plt.subplot(gs[0, :])

ax2 = plt.subplot(gs[1, :2])

ax3 = plt.subplot(gs[1:, 2])

ax4 = plt.subplot(gs[-1, 0])

ax5 = plt.subplot(gs[-1, -2])

plt.show()

得到

(3)样式三

import matplotlib.pyplot as plt

f, ((ax11, ax12), (ax13, ax14)) = plt.subplots(2, 2, sharex=True, sharey=True)

ax11.scatter([1,2], [1,2])

plt.tight_layout() # plt.tight_layout()表示紧凑显示图像

plt.show()

得到

4.3 图中图

import matplotlib.pyplot as plt

fig = plt.figure()

x = [1, 2, 3, 4, 5, 6, 7]

y = [1, 3, 4, 2, 5, 8, 6]

left, bottom, width, height = 0.1, 0.1, 0.8, 0.8

ax1 = fig.add_axes([left, bottom, width, height])

ax1.plot(x, y, 'r')

ax1.set_xlabel('x')

ax1.set_ylabel('y')

ax1.set_title('title')

left, bottom, width, height = 0.2, 0.6, 0.25, 0.25

ax2 = fig.add_axes([left, bottom, width, height])

ax2.plot(y, x, 'b')

ax2.set_xlabel('x')

ax2.set_ylabel('y')

ax2.set_title('title inside 1')

plt.axes([0.6, 0.2, 0.25, 0.25])

plt.plot(y[::-1], x, 'g') # 注意对y进行了逆序处理

plt.xlabel('x')

plt.ylabel('y')

plt.title('title inside 2')

plt.show()

得到

4.4 次坐标轴

import matplotlib.pyplot as plt

import numpy as np

x = np.arange(0, 10, 0.1)

y1 = 0.05 * x**2

y2 = -1 * y1

fig, ax1 = plt.subplots()

ax2 = ax1.twinx()

ax1.plot(x, y1, 'g-') # green, solid line

ax2.plot(x, y2, 'b--') # blue

ax1.set_xlabel('X data')

ax1.set_ylabel('Y1 data', color='g')

ax2.set_ylabel('Y2 data', color='b')

plt.show()

得到

5 动画

5.1 Animation 动画

from matplotlib import pyplot as plt

from matplotlib import animation

import numpy as np

fig, ax = plt.subplots()

x = np.arange(0, 2*np.pi, 0.01)

line, = ax.plot(x, np.sin(x))

def animate(i):

line.set_ydata(np.sin(x + i/10.0))

return line,

def init():

line.set_ydata(np.sin(x))

return line,

ani = animation.FuncAnimation(fig=fig, func=animate, frames=100, init_func=init, interval=20, blit=True)

plt.show()

ani.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])

得到