Hard liver! Super detailed basic introduction to Matplotlib!!!

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【 Introduction 】： Excellent data visualization , It will make your data analysis and other work even better , Let people print ( l ) like ( job ) deep ( Add ) moment ( pay ).matplotlib yes python Excellent data visualization library ,python A necessary tool for data analysis , This article is specially arranged for you matplotlib Detailed usage , Come and learn ！

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Data visualization is very important , Because the wrong or insufficient data representation may destroy the excellent data analysis work .

matplotlib  The library is dedicated to developing 2D Chart （ Include 3D Chart ） Of , Highlight the advantages ：

• It's extremely simple to use .

• Gradually 、 Interactive way to realize data visualization .

• Use of expressions and text LaTeX Typesetting .

• Strong control over image elements .

• Can output PNG、PDF、SVG and EPS Etc .

install

conda install matplotlib

perhaps

pip install matplotlib

matplotlib framework

matplotlib One of the main tasks of , Is to provide a set of graphical objects to represent and manipulate （ Main object ） And the functions and tools of its internal objects . It can not only deal with graphics , It also provides event handling tools , Have the ability to add animation effects to graphics . With these additional features ,matplotlib Can generate interactive charts of events triggered by keyboard keys or mouse movements .

Logically speaking ,matplotlib The overall structure of is 3 layer , One way communication between layers ：

• Scripting （ Script ） layer .

• Artist （ performance ） layer .

• Backend （ Back end ） layer .

One 、matplotlib The basic usage of

import numpy as np
import matplotlib.pyplot as plt


x = np.linspace(-np.pi, np.pi, 30) #  Generate... Within the interval 30 An equal difference 
y = np.sin(x)
print('x = ', x)
print('y = ', y)

Output ：

x =  [-3.14159265 -2.92493109 -2.70826953 -2.49160797 -2.2749464  -2.05828484
 -1.84162328 -1.62496172 -1.40830016 -1.19163859 -0.97497703 -0.75831547
 -0.54165391 -0.32499234 -0.10833078  0.10833078  0.32499234  0.54165391
  0.75831547  0.97497703  1.19163859  1.40830016  1.62496172  1.84162328
  2.05828484  2.2749464   2.49160797  2.70826953  2.92493109  3.14159265]
y =  [-1.22464680e-16 -2.14970440e-01 -4.19889102e-01 -6.05174215e-01
 -7.62162055e-01 -8.83512044e-01 -9.63549993e-01 -9.98533414e-01
 -9.86826523e-01 -9.28976720e-01 -8.27688998e-01 -6.87699459e-01
 -5.15553857e-01 -3.19301530e-01 -1.08119018e-01  1.08119018e-01
  3.19301530e-01  5.15553857e-01  6.87699459e-01  8.27688998e-01
  9.28976720e-01  9.86826523e-01  9.98533414e-01  9.63549993e-01
  8.83512044e-01  7.62162055e-01  6.05174215e-01  4.19889102e-01
  2.14970440e-01  1.22464680e-16]

• Draw a curve

plt.figure() #  Create a new window 
plt.plot(x, y) #  Draw a picture x And y Related curves 
plt.show()#  Display images 

• Draw multiple curves and add axes and labels

import numpy as np
import matplotlib.pyplot as plt


x = np.linspace(-np.pi, np.pi, 100) #  Generate... Within the interval 21 An equal difference 
y = np.sin(x)
linear_y = 0.2 * x + 0.1


plt.figure(figsize = (8, 6)) #  Customize the size of the window 


plt.plot(x, y)
plt.plot(x, linear_y, color = "red", linestyle = '--') #  Custom colors and representations 


plt.title('y = sin(x) and y = 0.2x + 0.1') #  Define the title of the curve 
plt.xlabel('x') #  Define the horizontal axis label 
plt.ylabel('y') #  Define vertical axis labels 


plt.show()

• Specify the coordinate range and Set axis scale

import numpy as np
import matplotlib.pyplot as plt


x = np.linspace(-np.pi, np.pi, 100) #  Generate... Within the interval 21 An equal difference 
y = np.sin(x)
linear_y = 0.2 * x + 0.1


plt.figure(figsize = (8, 6)) #  Customize the size of the window 


plt.plot(x, y)
plt.plot(x, linear_y, color = "red", linestyle = '--') #  Custom colors and representations 


plt.title('y = sin(x) and y = 0.2x + 0.1') #  Define the title of the curve 
plt.xlabel('x') #  Define the horizontal axis label 
plt.ylabel('y') #  Define vertical axis labels 
plt.xlim(-np.pi, np.pi)
plt.ylim(-1, 1)


#  To reset x Axis scale 
# plt.xticks(np.linspace(-np.pi, np.pi, 5))
x_value_range = np.linspace(-np.pi, np.pi, 5)
x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']
plt.xticks(x_value_range, x_value_strs)
plt.show() #  Display images 

• Define the coordinate axis with the origin at the center

import numpy as np
import matplotlib.pyplot as plt


x = np.linspace(-np.pi, np.pi, 100)
y = np.sin(x)
linear_y = 0.2 * x + 0.1


plt.figure(figsize = (8, 6)) 


plt.plot(x, y)
plt.plot(x, linear_y, color = "red", linestyle = '--') 


plt.title('y = sin(x) and y = 0.2x + 0.1')
plt.xlabel('x') 
plt.ylabel('y') 
plt.xlim(-np.pi, np.pi)
plt.ylim(-1, 1)


# plt.xticks(np.linspace(-np.pi, np.pi, 5))
x_value_range = np.linspace(-np.pi, np.pi, 5)
x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']
plt.xticks(x_value_range, x_value_strs)


ax = plt.gca() #  Get the axis 
ax.spines['right'].set_color('none') #  Hide the upper and right axes 
ax.spines['top'].set_color('none')


#  Set the position of the left and lower axes 
ax.spines['bottom'].set_position(('data', 0)) #  Set the lower axis to y = 0 The location of 
ax.spines['left'].set_position(('data', 0)) #  Set the coordinate axis on the left to  x = 0  The location of 


plt.show() #  Display images 

• legend legend

Use xticks() and yticks() Function to replace the axis label , Pass in two columns of values for each function . The first list stores the location of the scale , The second list stores the label of the scale .

import numpy as np
import matplotlib.pyplot as plt


x = np.linspace(-np.pi, np.pi, 100)
y = np.sin(x)
linear_y = 0.2 * x + 0.1


plt.figure(figsize = (8, 6)) 


#  Label the curve 
plt.plot(x, y, label = "y = sin(x)")
plt.plot(x, linear_y, color = "red", linestyle = '--', label = 'y = 0.2x + 0.1') 


plt.title('y = sin(x) and y = 0.2x + 0.1')
plt.xlabel('x') 
plt.ylabel('y') 
plt.xlim(-np.pi, np.pi)
plt.ylim(-1, 1)


# plt.xticks(np.linspace(-np.pi, np.pi, 5))
x_value_range = np.linspace(-np.pi, np.pi, 5)
x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']
plt.xticks(x_value_range, x_value_strs)


ax = plt.gca() 
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')




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


#  Mark the information of the curve 
plt.legend(loc = 'lower right', fontsize = 12)
plt.show()

legend Methods loc  Optional parameter settings

Location string	Location number	Position statement
‘best’	0	The best position
‘upper right’	1	Upper right corner
‘upper left’	2	top left corner
‘lower left’	3	The lower left corner
‘lower right’	4	The lower right corner
‘right’	5	On the right side
‘center left’	6	The left side is vertically centered
‘center right’	7	The right side is vertically centered
‘lower center’	8	The lower part is horizontally centered
‘upper center’	9	Center horizontally above
‘center’	10	precise middle

Two 、 Histogram

Method used ：plt.bar

import numpy as np
import matplotlib.pyplot as plt


plt.figure(figsize = (16, 12))
x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y = np.array([3, 5, 7, 6, 2, 6, 10, 15])
plt.plot(x, y, 'r', lw = 5) #  Specifies the color and width of the line 


x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y = np.array([13, 25, 17, 36, 21, 16, 10, 15])
plt.bar(x, y, 0.2, alpha = 1, color='b') #  Generate a histogram , Indicates the width of the graph , Transparency and color 
plt.show()

Sometimes the histogram will appear in x Both sides of the shaft , Easy to compare , The code implementation is as follows ：

import numpy as np
import matplotlib.pyplot as plt


plt.figure(figsize = (16, 12))
n = 12
x = np.arange(n) #  Generate in order from 12 Numbers within 
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)


#  Set the color of the histogram and the boundary color 
#+y It means that x Above the axis  -y It means that x Below the shaft 
plt.bar(x, +y1, facecolor = '#9999ff', edgecolor = 'white')
plt.bar(x, -y2, facecolor = '#ff9999', edgecolor = 'white')


plt.xlim(-0.5, n) #  Set up x The scope of the shaft ,
plt.xticks(()) #  You can set the scale to null , Eliminate scale 
plt.ylim(-1.25, 1.25) #  Set up y The scope of the shaft 
plt.yticks(())


# plt.text() Write text to the image , Set location , Set text ,ha Set the horizontal direction to its way ,va Set the vertical alignment 
for x1, y in zip(x, y2):
    plt.text(x1, -y - 0.05, '%.2f' % y, ha = 'center', va = 'top')
for x1, y in zip(x, y1):
    plt.text(x1, y + 0.05, '%.2f' % y, ha = 'center', va = 'bottom')
plt.show()

3、 ... and 、 Scatter plot

import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2
plt.scatter(x, y, s = area,c = colors, alpha = 0.8)


plt.show()

Four 、 Contour map

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)  #  Generate grid coordinates   take x Shaft with y All points in the square area of the axis are obtained 
line_num = 10 #  Number of contours 


plt.figure(figsize = (16, 12))


#contour  Function for generating contour lines 
# The first two parameters represent the coordinates of the point , The third parameter represents a function equal to a contour line , The fourth parameter indicates how many contours are generated 
C = plt.contour(X, Y, f(X, Y), line_num, colors = 'black', linewidths = 0.5) #  Set the color and the width of the line segment 
plt.clabel(C, inline = True, fontsize = 12) #  Get the exact value of each contour line 


#  Fill color , cmap  Indicates how to fill ,hot Indicates the color filled with heat 
plt.contourf(X, Y, f(X, Y), line_num, alpha = 0.75, cmap = plt.cm.hot)


plt.show()

5、 ... and 、 Processing images

import matplotlib.pyplot as plt
import matplotlib.image as mpimg #  Import a library for processing pictures 
import matplotlib.cm as cm #  Import a library that handles colors colormap


plt.figure(figsize = (16, 12))
img = mpimg.imread('image/fuli.jpg')#  Read the picture 
print(img) # numpy data 
print(img.shape) # 


plt.imshow(img, cmap = 'hot')
plt.colorbar() #  Get the value corresponding to the color 
plt.show()

[[[ 11  23  63]
  [ 12  24  64]
  [  1  13  55]
  ...
  [  1  12  42]
  [  1  12  42]
  [  1  12  42]]


 [[ 19  31  71]
  [  3  15  55]
  [  0  10  52]
  ...
  [  0  11  39]
  [  0  11  39]
  [  0  11  39]]


 [[ 22  34  74]
  [  3  15  55]
  [  7  19  61]
  ...
  [  0  11  39]
  [  0  11  39]
  [  0  11  39]]


 ...


 [[ 84 125 217]
  [ 80 121 213]
  [ 78 118 214]
  ...
  [ 58  90 191]
  [ 54  86 187]
  [ 53  85 186]]


 [[ 84 124 220]
  [ 79 119 215]
  [ 78 117 218]
  ...
  [ 55  87 188]
  [ 55  87 188]
  [ 55  87 188]]


 [[ 83 121 220]
  [ 80 118 219]
  [ 83 120 224]
  ...
  [ 56  88 189]
  [ 58  90 191]
  [ 59  91 192]]]
(728, 516, 3)

utilize numpy The matrix gets the picture

import matplotlib.pyplot as plt
import matplotlib.cm as cm #  Import a library that handles colors colormap
import numpy as np


size =  8
#  Get one 8*8 Values in (0, 1) The matrix between 
a = np.linspace(0, 1, size ** 2).reshape(size, size)


plt.figure(figsize = (16, 12))
plt.imshow(a)
plt.show()

6、 ... and 、3D chart

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D #  Import Axes3D object 


fig = plt.figure(figsize = (16, 12))
ax = fig.add_subplot(111, projection = '3d') #  obtain 3d Images 


x = np.arange(-4, 4, 0.25)
y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(x, y) #  Generate grid 
Z = np.sqrt(X ** 2 + Y ** 2)


#  Draw a curved surface               #  The span corresponding to rows and columns          #  Set the color 
ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('rainbow'))
plt.show()

Above is matplotlib Data visualization based on test data , Combined with the data in the actual project , The code is slightly modified , Can have an impressive effect .

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