Matplotlib python 数据可视化

视频链接

1.如何安装matplotlib

系统是基于ubuntu18.04+python3

首先安装pip3

$ sudo apt install python3-pip
$ pip3 install numpy
$ pip3 install matplotlib

2.基本用法

2.1 画函数

画y=2*x+1的曲线在区间[-1,1]上；

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-05 11:16:56
'''
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1, 1, 50)
## y = 2 * x + 1
y=x**2
plt.plot(x, y)
plt.show()

2.2画不同的函数在一张图上

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1, 1, 50)
y1 = 2 * x + 1
y2 = x ** 2
plt.figure()
plt.plot(x, y1)

plt.figure(num=3, figsize=(8, 5),)#注意这里有，不然不显示第二个曲线
plt.plot(x, y2)
plt.plot(x,y1,color='red',linewidth=1.0,linestyle='--')

plt.show()

2.2 设置figure图像

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-06 22:23:53
'''
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=1.0, linestyle='--')
plt.xlim((-1, 2))#设置x轴的范围
plt.ylim((-2, 3))#设置y轴的范围
plt.xlabel('I am x')
plt.ylabel('I am y')
#set new sticks
new_ticks = np.linspace(-1,2,5)
print(new_ticks)
plt.xticks(new_ticks)
#set tick labels
plt.yticks([-2,-1.8,-1,-1.22,3],[r'$really\ bad$',r'$bad$',r'$normal$',r'$good$',r'$really\ good$'])
plt.show()

2.3 坐标轴的设置

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-06 22:54:26
'''

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=1.0, linestyle='--')
plt.xlim((-1, 2))#
plt.ylim((-2, 3))#
plt.xlabel('I am x')
plt.ylabel('I am y')
#set new sticks
new_ticks = np.linspace(-1,2,5)
print(new_ticks)
plt.xticks(new_ticks)
#set tick labels
plt.yticks([-2,-1.8,-1,-1.22,3],[r'$really\ bad$',r'$bad$',r'$normal$',r'$good$',r'$really\ good$'])
#gca='get current axis'
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))

plt.show()

2.4 legend图例

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 09:45:59
'''

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))#

#set new sticks
new_ticks = np.linspace(-1,2,5)
plt.xticks(new_ticks)
#set tick labels
plt.yticks([-2,-1.8,-1,-1.22,3],[r'$really\ bad$',r'$bad$',r'$normal$',r'$good$',r'$really\ good$'])
l1, = plt.plot(x, y1, label='linear line')
l2, = plt.plot(x, y2, color='red', linewidth=1.0, linestyle='--', label='square line')
plt.legend(loc='best')
#plt.legend(handles=[l1,l2],labels=['up','down'],loc='best')

plt.show()

2.5 Annotation标注

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 15:27:28
'''
#!/usr/bin/python3
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.plot([x0, x0], [0, y0,], 'k--', linewidth=1.5)
plt.scatter([x0,], [y0,], s=50, color='b')
#annotation
#method 1
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"))
#method 2
plt.text(-3.7, 3, r'$This\ is\ the\ some\ test. \mu\ \sigma_i\ \alpha_t$',
        fontdict={'size':16,'color':'r'})

plt.show()

2.6 tick能见度

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 15:44:34
'''
#!/usr/bin/python3
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)
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))
plt.show()

3.画图种类

3.1 散点图

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 15:59:29
'''
import matplotlib.pyplot as plt
import numpy as np
n = 1024
X = np.random.normal(0, 1, n)
Y = np.random.normal(0, 1, n)
T = np.arctan2(Y, X)  #for color
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(())
plt.show()

3.2 柱状图

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 16:23:04
'''
import matplotlib.pyplot as plt
import numpy as np
n = 12
X = np.arange(12)
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):
    plt.text(x + 0.1, y + 0.05, '%.2f' % y, ha='center', va='bottom')
for x, y in zip(X, Y2):
    plt.text(x+0.1,-y-0.05,'%.2f'%-y,ha='center',va='top')
plt.xlim(-1, n)
plt.ylim(-1.25,1.25)
plt.show()

3.3 等高线

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 16:52:20
'''
import matplotlib.pyplot as plt
import numpy as np
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.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=True, fontsize=10)

plt.xticks(())
plt.yticks(())
plt.show()

3.3 Image图片

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 17:08:15
'''
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)

"""
for the value of "interpolation", check this:
http://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html
for the value of "origin"= ['upper', 'lower'], check this:
http://matplotlib.org/examples/pylab_examples/image_origin.html
"""
plt.imshow(a, interpolation='nearest', cmap='bone', origin='lower')
plt.colorbar(shrink=.92)

plt.show()

3.4 3D数据

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-09 17:48:00
'''
import matplotlib.pyplot as plt
import numpy as np
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.get_cmap('rainbow'))
ax.contourf(X,Y,Z,zdir='x',offset=-4,cmap=plt.get_cmap('rainbow'))
ax.set_zlim(-2,2)

plt.show()

4.多图合并显示

4.1 subplot多合一显示

import matplotlib.pyplot as plt
plt.figure(figsize=(6, 4))
plt.subplot(2,2,1)
plt.plot([0, 1], [0, 1])
plt.subplot(222)
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.tight_layout()
plt.show()

import matplotlib.pyplot as plt
plt.figure(figsize=(6, 4))
plt.subplot(2,1,1)
plt.plot([0, 1], [0, 1])
plt.subplot(234)
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.tight_layout()
plt.show()

4.2 分格显示

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# method 1
plt.figure()
ax1=plt.subplot2grid((3,3),(0,0),colspan=3)
ax1.plot([1, 2], [1, 2])
ax1.set_title('ax1_title')
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2)
ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
ax4 = plt.subplot2grid((3, 3), (2, 0))
ax4.scatter([1, 2], [2, 2])
ax4.set_xlabel('ax4_x')
ax4.set_ylabel('ax4_y')
ax5=plt.subplot2grid((3,3),(2,1))
plt.tight_layout()
plt.show()

#method 2
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
plt.figure()
gs = gridspec.GridSpec(3, 3)
ax6 = plt.subplot(gs[0, :])
ax7 = plt.subplot(gs[1, :2])
ax8 = plt.subplot(gs[1:, 2])
ax9 = plt.subplot(gs[-1, 0])
ax10=plt.subplot(gs[-1,-2])

plt.tight_layout()
plt.show()

f,((ax11,ax12),(ax13,ax14))=plt.subplots(2,2,sharex=True,sharey=True)
ax11.scatter([1,2],[1,2])
plt.tight_layout()
plt.show()

4.3 图中图

'''
@Description: 
@version: 
@Author: sunshine
@Github: https://subshine.github.io/
@Email: [e-mail]
@Date: 2020-08-05 11:11:12
@LastEditTime: 2020-08-11 22:33:54
'''
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')

ax2 = fig.add_axes([0.2, 0.6, 0.25, 0.25])
ax2.plot(x, y, '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') #[::1] data inverse
plt.xlabel('x')
plt.ylabel('y')
plt.title('title inside 2')

plt.show()

4.3 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-')
ax2.plot(x, y2, 'b--')

ax1.set_xlabel('X data')
ax1.set_ylabel('Y1 data', color='g')
ax2.set_ylabel('Y2 data', color='b')


plt.show()

4.4 使用animation绘制动画

from matplotlib import pyplot as plt
import numpy as np
from matplotlib import animation
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))  # update the data
    return line,


# Init only required for blitting to give a clean slate.
def init():
    line.set_ydata(np.sin(x))
    return line,

# call the animator.  blit=True means only re-draw the parts that have changed.
# blit=True dose not work on Mac, set blit=False
# interval= update frequency
ani = animation.FuncAnimation(fig=fig, func=animate, frames=100, init_func=init,
                              interval=20, blit=False)

# save the animation as an mp4.  This requires ffmpeg or mencoder to be
# installed.  The extra_args ensure that the x264 codec is used, so that
# the video can be embedded in html5.  You may need to adjust this for
# your system: for more information, see
# http://matplotlib.sourceforge.net/api/animation_api.html
ani.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])  #save video



plt.show()