Matplotlib drawing

Catalog

library

Various attributes of graphics

Figure:

figure attribute

Directly in figure Upper foundation drawing

 figure Basic drawing + line attribute

shortcoming ：

 Axes:  Subgraphs 、 Axis domain , For display figure Small figure in the panel  

Axes： Axis , scale and Axis title

Axes：plot mapping , legend, Text , mark , grid , In the picture , Different axis drawing

  color property   color

Linetype properties linestyle 

Point type   marker

graphics

Scatter plot

Histogram

The pie chart

  Cotton stick chart

  boxplot

Error bar chart

Two dimensional cloud image

Epipolar diagram

Three dimensional diagram

---

library

import matplotlib.pyplot as plt   
import numpy as np    

# 0.3 - 3.5  Between   Set up  100 A little bit 
x = np.linspace(0.3, 3.5, 100)  

#  random number 

#  Will return  0-1 Random number between 
np.random.rand()

#  Will return   contain 2 individual 0 To 1 List of random numbers between 
y = np.random.rand(n)

#  Will return  n1 X n2  The random number matrix of 
y = np,random,rand(n1,n2)


# np.random.randn()   Usage is similar. , however   The return is   The mean for 0, 
#  The standard deviation is 1 Of   Normal distribution , That is, the return value will exceed （0-1）


#   stay  0~10  Inside   formation  100 A little bit  
boxWeight = np.random.randint(0,10,100)

Various attributes of graphics

Figure:

Image window , contain Axes,tiles,Legends Wait for the outermost window , yes windows Application window .

figure attribute

fig = plt.figure()
fig = plt.figure(figsize=(8, 6), dpi=80)


##  Set the handle  
fig = plt.gcf()   # Get the current figure
fig = ax1.figure   # Gets the window to which the specified sub graph belongs 


### fig Title settings for 
fig.suptitle('title of figure')


#  Title of picture  
# fig.suptitle() Is the title of the canvas , plt.title() Is the title of the diagram 
plt.title('title of picture')


plt.savefig('aa.jpg',dpi=400,bbox_inches='tight')   
#savefig  Save the picture ,dpi The resolution of the ,bbox_inches The size of the white space around the subgraph 

Directly in figure Upper foundation drawing

###   Not recommended , It is recommended to use  Axes

x = np.linespace(-2,6,50)  # from -2 to 6, total 50 points 
y1 = x + 3
y2 = 3 - x
plt.figure()
plt.plot(x,y1)
plt.plot(x,y2)

plt.show()

 figure Basic drawing + line attribute

x = np.linspace(-2,6,50)  # from -2 to 6, total 50 points 
y1 = x + 3
y2 = 3 - x


#  Directly generate one at a time ax Subgraphs , Then operate directly in the subgraph 

plt.subplot(2,1,1)
plt.plot(x,y1)


#  Set the width of the line , Color , Linetype ,  obtain Line2D Handle of attribute  list
# plt.plot()  Medium label='y1'  Have to add  plt.legend()  Will show 
line = plt.plot(x,y,label='y1',color='purple',linewidth=2, linestyle='--')
plt.legend()


line[0].set_linewidth(5)  # plt.plot  and  ax.plot  Can get the drawing   Line set 


# axis limit

plt.axis([-2,-2, 6,10])
##  The same thing 

plt.xlim(-2,6)
plt.ylim(-2,10)


plt.subplot(2,1,2)
plt.plot(x,y2)


#   Set up   Level ,  Vertical line 
plt.axhline(y=0, c='r', ls='--', lw=2)    #  Level 
plt.axvline(x=4, c='r', ls='--', lw=2)    #  Vertical line 


#   Set up   vertical / level    Reference area  +  Fill color 
plt.axvspan(xmin=4, xmax=6, facecolor='y', alpha=0.3)
plt.axhspan(ymin=0, ymax =0.5, facecolor='y', alpha=0.3)


#   Set up    arrow 
plt.annotate('important point', xy=(2, 6), xytext=(3, 1.5),
 weight='bold', color='b', arrowprops=dict(facecolor='black', shrink=0.05)
            )



plt.show()

shortcoming ：

figure Drawing directly on , Can't change axis Related properties , So it's not recommended , Recommended settings figure, Then set the axes（ Even if there is only one axes）, Then set the Related properties of subgraph , And draw .

advantage

But directly figure Draw on , Able to use Vertical line , Level , Add special points , Mark, etc

 Axes:  Subgraphs 、 Axis domain , For display figure Small figure in the panel  

# axes[0] axes[1] axes[2] axes[3]  Can be used to axis  call 
fig,axes = plt.subplots(2,2)

#  You can also define a subgraph at a time 
ax = plt.subplot(2,2,1)

 axis: Corresponding ax Medium x axis and y axis.

Axes： Axis , scale and Axis title

##  Subgraph title 
ax.set_title(' ')

##  Name of coordinate axis of subgraph 
ax.set_xlabel(' ')
ax.set_ylabel(' ')


##  Change the axis range 

ax.set_xlim([-5,6])
ax.set_ylim([-2,10])

##  Change the axis scale 

ax.set_xticks([-1,0,1])
ax.set_yticks(range(-2,10,1))


##  Change axis scale name 

ax.set_yticklabels([r'-1', r'0', r'+1'])

ax.set_xticklabels(labels=['x1','x2','x3','x4','x5'],rotation=-30,fontsize='small')  
# Set the display text of the scale ,rotation Rotation Angle ,fontsize font size 


###  Set the size scale  

from matplotlib.ticker import MultipleLocator

#  Main scale 
xmajorLocator = MultipleLocator(2)   
# Define the scale difference of the horizontal major scale label as 2 Multiple . It's just a few ticks apart to show a label text 
ymajorLocator = MultipleLocator(3)   
# Define the scale difference of the vertical major scale label as 3 Multiple . It's just a few ticks apart to show a label text 

ax.xaxis.set_major_locator(xmajorLocator)
ax.yaxis.set_major_locator(ymajorLocator)

#  Secondary scale 
# take x The axis scale label is set to 5 Multiple 
xminorLocator = MultipleLocator(5) 
# Put this y The axis scale label is set to 0.2 Multiple 
yminorLocator = MultipleLocator(0.2) 

# Display the position of the secondary scale label , No label text 
ax.xaxis.set_minor_locator(xminorLocator)
ax.yaxis.set_minor_locator(yminorLocator)



###   Set up   grid 
ax1.xaxis.grid(True, which='major')      #x The grid of the axis uses the defined major scale format 
ax1.yaxis.grid(True, which='major')      #x The grid of the axis uses the defined major scale format   major, minor 


###  Set up   Axis scale （ Size scale ） Of   Format 

from matplotlib.ticker import  FormatStrFormatter


xmajorFormatter = FormatStrFormatter('%3.1f')
ymajorFormatter = FormatStrFormatter('%1.1f')

ax.xaxis.set_major_formatter(xmajorFormatter)
ax.yaxis.set_major_formatter(ymajorFormatter)


###  hide   That is not used ax 

axes[j].axis('off')


###    Axis inversion 
axes[i].invert_yaxis()

###    Sets the position of the axis 
axes[i].xaxis.set_ticks_position('top')

Axes：plot mapping , legend, Text , mark , grid , In the picture , Different axis drawing

x = np.linspace(-2,6,50)  # from -2 to 6, total 50 points 
y1 = x + 3
y2 = 3 - x
y3 = 1000+ 1000*x


ax = plt.subplot(1,1,1)


###  Set line handle 
line1, line2 = ax.plot(x,y1,'r', x,y2,'b')

### Set the line width under the line handle 
line1.set_linewidth(2)
line2.set_linewidth(2)


###  color Color change    linestyle Change Linetype    linewidth  Change the line width    marker  Change point type 
###  markersize  Change the size of the point  alpha transparency 

line = ax.plot(x,y,label='y',alpha=0.5, color='purple',linewidth=2, linestyle='--',
marker='p', markersize=5 )

###  Color , Point type , Linetype   Can be abbreviated to ： 
### ax.plot(x,y,'rp-')

##  line[0].set_linewidth = 5;    line Get is plot Line set of drawing 


###  Set the line legend
ax.legend([line1,line2],['y = x+3','y = 3 - x'],loc='best')

## loc = 'best', 'upper right', 'upper left', 'lower left', 'lower right', 'right', 
## 'center left', 
## 'center right', 'lower center', 'upper center', 'center'




# Display text at specified location ,plt.text()
#weight : normal | bold | bolder | lighter | 100 | 200 | 300 | 400 | 500 | 600 | 700 | 
#  800 | 900
ax.text(2.8, 7, r'y=3*x', weight='bold',color='b') 


# Add labels , Parameters ： Annotated text 、 Point of reference 、 Text location 、 Text font , Text color , Arrow properties 
ax.annotate('important point', xy=(2, 6), xytext=(3, 1.5),
 weight='bold', color='b', arrowprops=dict(facecolor='black', shrink=0.05)
            )


#  Show grid .which The value of the parameter is major( Just draw the big scale )、minor( Just draw small scales )、both, The default value is major.
# axis by 'x','y','both'

ax.grid(b=True,which='major',axis='both',alpha= 0.5,
color='skyblue',linestyle='--',linewidth=2)


 In the picture 
axes1 = plt.axes([.2, .3, .1, .1], facecolor='y')       
# Add a sub graph to the current window ,rect=[ Left ,  Next ,  wide ,  high ],
#  It's using   The proportion   Locate 
axes1.plot(x,y2)  


#  In a subgraph   Same as x Axis , Different y Axis    ;  Same as y Axis , Different x Axis   
# ax1.twinx()   ax1.twiny()

ax2 = ax1.twinx() 
ax2.plot(x,y3,label='y3',color='blue')

  color property   color

Linetype properties linestyle 

• Solid line ： '-'
• Dotted line ： '--'
• Dotted line ： '-.'
• Dotted line ： ':'
• spot ： '.' 

Point type   marker

Pixels ： ','
circular ： 'o'
Upper triangle ： '^'
Lower triangle ： 'v'
Left triangle ： '<'
Right triangle ： '>'
square ： 's'
plus ： '+' 
Fork ： 'x'
Prismatic ： 'D'
Fine prismatic ： 'd'
Tripod down ： '1'（ It's Ya ）
Tripod up ： '2'
The tripod faces left ： '3'
The tripod faces right ： '4'
hexagon ： 'h'
Rotate hexagon ： 'H'
Pentagonal ： 'p'
Vertical line ： '|'
Level ： '_'

graphics

Scatter plot

  It can be used to set special points , And use annotate() Annotate ; You can set the scatter chart

## s : size  Size of points , The default is 20   ; c / color  Color  ; marker  Point type  ; alpha : 0-1  transparency 
##  linewidth :  The peripheral line width of the point , The default is 0
plt.scatter([3],[6],s=20, c='blue', marker='o', alpha = None,linewidth=None)


## x,y  by list 
plt.scatter(x,y)


###   Bubble chart  
a= np.random.randn(100)
b= np.random.randn(100)

plt.scatter(a,b,s=np.power(10*a+20*b,2), c=np.random.rand(100), 
cmap= plt.cm.RdYlBu,marker='o')

Histogram , Bar chart and Histogram + Stacked graph

#  x  Express  x coordinate ,  The data type is int, float 
#  height  Indicates the height of the histogram ,   The data type is  int , float  type 
#  width  Express   The width of the histogram ,  stay 0~1 Between 
#  bottom  Indicates the starting position of the histogram , That is to say y The starting coordinates of the axis ,  You can't do it from 0 Start 
#  align = 'center', 'edge'   Indicate whether the scale is in the middle or on the side of each column 
#  hatch = '\\'   '/'  '\\/'  '-'
#  edgecolor  Indicates the border color 
#  linewidth  Indicates the width of the border 
#  tick_label:  Subscript label   ,   Quantity and sum x,height The quantity is the same , Used in substitution x Axis number 
#  log :  Histogram yz The axis uses scientific counting , Bool type  
#  alpha ：  Express   transparency 


### vertial  Histogram 

plt.bar(x, height, width=0.8, align='edge',color='c',hatch='/', tick_label=['A','B','C','D'])

plt.xlabel()
plt.ylabel()



###  horizontal   Bar chart  

###   The parameters are the same as above ,  however  bottom  Change it into  left
plt.barh()



###  hist()    Used to draw histograms    For display  x  Distribution of data in 
#  x   Express   The data points     for example   Distribution in （0,10） Between 100 It's an integer 
# bins   Express    Points of distribution      for example   Express  （0,10）  Of   Distribution point 
# histtype  ：  step, stepfilled, barstacked:  Two bar charts with stacked bars , 
# bar:  The width of the bar can be different  
# rwidth  Express   Strip shape   Width 
# alpha  Presentation transparency 
# density  False , True  Express  Y Number of shafts , Or density 

#  Histograms are used to show   Distribution of data ,  Only x Need to define , bin Used to describe   The range of data 


plt.hist(x, bins=bins, color='g', histtype='') 



#  Stacked graph 
#  Used to place one bar chart above another ,eg：  Indicates quarterly growth 

# practice ：  Put the second histogram / The design of bar chart  bottom（ For histogram ） perhaps left ( For bar charts )  Set as the first histogram / Bar chart   bottom/left = y/ height


x = [1,2,3,4,5]
y1 = [6,10,4,5,1]
y2 = [2,6,3,8,5]

#  First create  y1  label stay  legend It will be displayed later 
plt.bar( x,y1, align='center',color = 'k', tick_label = ['A','B','C','D','E'] ,label = ' First quarter ')

#  And then in  y1  Create above  y2

plt.bar( x,y2, align='center', bottom=y1, color = 'k', label = ' The two quarter ')
 

plt.xlabel(' Different branches ')
plt.ylabel(' results ')

plt.legend()

plt.show()


#  Bar chart   barh    take  bottom  Switch to left

Block diagram （ Bar chart / The histogram is placed side by side ）

The pie chart

#  nums:   Represents the number of each part   
#  labels:  Label representing each part 
#  colors：  Represents the color of each part 
#  explode = [0,0.1,0,0]   Represents the prominent part of each part 
#  autopct = '%3.1f%%'   Express   The percentage displayed in each section 
#  startangle=60   The starting angle is 60°
#  shadow = False ; True    Indicates whether there is a shadow 
plt.pie(nums, explode, labels=labels, autopct='%3.1f%%',startangle=60,
 colors=colors,shadow=False)


#  Indicates that the pie chart is circular ,  Not oval 
plt.axis('equal') 

  Cotton stick chart

### 
#  x：  Indicates that the baseline is x Position on the shaft , y:   Express   The difference is  y The length on the shaft 
#  linefmt  Indicates the form of cotton stick 
#  markerfmt  Indicates dot 
#  basefmt  Represents the style of the baseline 


plt.stem(x,y,linefmt='-.',markerfmt='o',basefmt='-')

  boxplot

# x:  Represents a data list 

plt.boxplot(x)

# plt.ylabel()   Express y The title of the axis 
# plt.title()    Indicates the title of the box diagram 
# plt.xtick([1],[' Random number generator AlphaRM'])  

Error bar chart

plt.errorbar(x,y,fmt='bo:',yerr=0.2,xerr=0.02)

Two dimensional cloud image

###  Two dimensional cloud image    
### vmin  by   minimum value ,  vmax  by   Maximum .   The maximum and minimum colors in the gradient 

ax.imshow( matrix ,cmap=plt.cm.cool,vmin=-5, vmax=5,alpha = None)  

 cmap Color

autumn	red - orange - yellow
bone	black - white ,x Line
cool	green - Magenta
copper	black - copper
flag	red - white - blue - black
gray	black - white
hot	black - red - yellow - white
hsv	hsv Color space , red - yellow - green - green - blue - Magenta - red
inferno	black - red - yellow
jet	blue - green - yellow - red
magma	black - red - white
pink	black - powder - white
plasma	green - red - yellow
prism	red - yellow - green - blue - purple -...- Green mode
spring	Magenta - yellow
summer	green - yellow
viridis	blue - green - yellow
winter	blue - green

Epipolar diagram

theta = np.linspace(0, 2*np.pi, 12, endpoint=False)
r = 30* np.random.rand(12)

#  theta : angle 
#  r:  distance 
#  color:  Line color 
#  linewidth  Line width 
#  marker  Point type ;  mfc  The color of the point ;  ms  Size of points 


plt.polar( theta,r, color = 'chartreuse', linewidth=2, marker='*', mfc='b',ms=10 )

Three dimensional diagram

ax = fig.add_subplot(1,1,1,projection='3d')     # 3d drawing 
 

##  3D lines 

theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve')



##  Surface map 

x,y=np.mgrid[-2:2:20j,-2:2:20j]  # obtain x Axis data ,y Axis data   X,Y = np.meshgrid(X,Y)
z=x*np.exp(-x**2-y**2)   # obtain z Axis data 


surf = ax.plot_surface(x,y,z,rstride=2,cstride=1,cmap=plt.cm.coolwarm,alpha=0.8)


ax.set_zlim(z_min, z_max)


fig.colorbar(surf, shrink=0.5, aspect=5)

Reference and copy the link source ：

python Drawing books - The search results - You know

Python The line type when drawing in _lei_jw The blog of -CSDN Blog

[python Automatic data processing ]python use matplotlib Set when drawing x/y Method of major and minor scales of axis coordinates - Lippman - Blog Garden (cnblogs.com)