matplotlib

Drawing function

plot

plot: draw a two-dimensional graph to connect the points of the plane

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 256,endpoint=True)
plt.plot(x,np.cos(x)) # x-axis y-axis
plt.show() # Finally, enter this
# If the x-axis is not specified, it will be 0,1,2 n-1

scatter

Scatter: scatter plot

x = np.random.randint(1,30,(1,8))
y = np.random.randint(1,30,(1,8))
Size = np.random.randint(10,500,(1,8))
Color = np.array(["red","green","black","orange","purple","beige","hotpink","magenta"])
# Colors can be represented by RGB codes
# A graph similar to clustering can be drawn twice, changing one color each time
plt.scatter(x,y,s=Size,c=Color,alpha=0.5) # alpha is transparency. It should look better when set
plt.show()

x = np.random.randint(1,100,(1,300))
y = np.random.randint(1,100,(1,300))
y.sort() # Order first or not
colors = np.arange(300) # The number must correspond to the above. You can set the color yourself, which can be understood as the final normalization to determine the color

plt.scatter(x, y, c=colors, cmap='viridis') # camp is the name of the color bar
plt.colorbar() # Display color bar
plt.show()

Optional parameters More colors

cool	winter	viridis	rainbow	twilight	terrain	spring
prism	plasma	pink	ocean	jet	flag	Blues

bar , barh

bar: draw histogram

barh: a horizontal bar chart. The usage is the same as bar

x = np.array(['col-1','col-2','col-3','col-4'])
y = np.array([3,8,5,9])
Color = np.array(['red','blue','green','hotpink'])
plt.bar(x,y,color=Color,width=0.9) # width is [0,1]
# If it's barh, it's height
plt.show()

pie

Pie: draw pie chart

parameter	explain	Value
x	Value of each sector	list
lables	Name of each sector	list
explod	Spacing of each sector	List, representing the distance between each sector and other sectors (0.1 is appropriate)
colors	colour	List, color of sectors
autopct	Fan display percentage	Integer '% d%%', decimal '% 0.1f', decimal percentage '% 0.1f%%'
labeldistance	Distance from label to sector	The default is 1.1, and less than 1 is in the sector
pctdistance	Position of sector percentage	Default 0.6

y = np.array([35, 25, 25, 15])
plt.pie(y,
        labels=['A','B','C','D'],
        colors=["red", "lightblue", "green", "hotpink"],
        explode=[0.1,0,0,0],
        autopct='%d%%',
       )
plt.show()

Image parameters and annotations

figure

figure: adjust the size of the picture

plt.figure(figsize=(8,5),dpi=80)

legend

legend: when there are multiple functions in one diagram, each function can be labeled with a name

plot(x1,y1,label='cos') # Drawing name
plot(x2,y2,label='sin')
plt.legend(loc='upper left',frameon = False)
# Mark the place and whether a small border is required

import matplotlib.pyplot as plt
import numpy as np

ax = plt.subplot()
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)) 

x1 = np.linspace(-np.pi,np.pi,300)
x2 = np.linspace(-np.pi/2,np.pi*3/2,300)
y1 = np.cos(x1)
y2 = np.sin(x2)

plt.plot(x1,y1,label="cos")
plt.plot(x2,y2,label='sin')
plt.legend(loc='best',frameon = False)
plt.show()

The position is the arrangement and combination of the following words (left, right at the end)

best	right	lower
center	left	upper

annotate

annotate: mark points on the image

# Label content of latex
# xy: position of dimension point
# xytext: annotation content location coordinates
# fontsize: font size
# Xycoords, textcoords and arrowprops basically need not be adjusted
pointx,pointy = 1,2
plt.plot([pointx,pointx],[0,pointy],linestyle="--")
plt.scatter([pointx],[pointy], 50,)
# Add a guide by the way, z 
plt.annotate(r'$this point$',
             xy=(pointx,pointy),
             xytext=(-90, -50),
             xycoords='data',
             textcoords='offset points',
             fontsize=16,
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2")
            )

Line parameters

colour	character	Line type	character	sign	character
blue	b	Solid line	-	Point marker	.
gules	r	Broken broken line	–	Pixel marker	,
yellow	y	Dotted line	-.	Solid ring	o
black	k	Dotted line	:	Inverted triangle	v
green	g			Upper triangle	^
white	w			stars	*

See for more shapes Drawing mark Drawing line

Coordinate axis

spines

spines: set the relevant parameters of the coordinate axis

ax = plt.subplot()
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))

xticks , yticks

xticks: sets the number displayed on the x-axis

yticks: sets the number displayed on the y-axis

x = np.linspace(-np.pi, np.pi, 256,endpoint=True)
plt.plot(x,np.cos(x))
plt.xticks(np.linspace(-np.pi,np.pi,9))
plt.yticks(np.linspace(-2,2,9))
plt.show()

It also supports latex, one by one

x = np.linspace(-np.pi, np.pi, 256,endpoint=True)
plt.plot(x,np.cos(x))
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],['$-\pi$','$-\pi/2$','$0$','$+\pi/2$','$+\pi$'])
plt.show()

Prevent blocking the number of xy axis

for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(16)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65 ))

However, before using it, you should first set the zorder in the plot, that is, the occlusion priority. You also need to generate an ax with subplot

ax = plt.subplot(111)
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))

X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C,S = np.cos(X), np.sin(X)

plt.plot(X, C,zorder=-1)
plt.plot(X, S,zorder=-2)

for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(16)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65 ))
plt.show()

xlim , ylim

xlim: sets the upper and lower limits of the x-axis

ylim: set the upper and lower limits of y-axis

plt.xlim(x.min()*1.1,x.max()*1.1) # 1.1 is the default
# Lower and upper limit
# ylim same

Chinese settings

Method 1

Font download [yxqu]

Local path "C:\chinese.otf"

This method does not change the global font

chinese = matplotlib.font_manager.FontProperties(fname="C:\\chinese.otf") 
# set up path
plt.title("cosine function",fontproperties=chinese) # usage method
plt.xlabel("x axis",fontproperties=chinese)

Method 2

This method will change the global font, so you can use Chinese

plt.rcParams['font.sans-serif']=['SimHei']

Song style	Blackbody	Microsoft YaHei	NSimSun	Regular script
SimSun	SimHei	Microsoft YaHei	NSimSun	KaiTi

label

xlable , ylable

Xlable: X-axis label

Ylable: Y-axis label

You can add fontdict, size and LOC parameters

plt.xlabel("x axis", fontproperties=zhfont1,fontdict={'color':'red'},size=10)
plt.ylabel("y")

title

Title: add a title

plt.title("x-cos(x)") # You can add the size parameter to make the title bigger

Using the loc position parameters of title, xlable and ylable

function	parameter	parameter	Default parameters
xlable	left	right	center
ylable	bottom	top	center
title	left	right	center

plt.xlable("x_lable",loc="left")

Grid line

grid

Grid: add a grid line. See "line parameters" for specific parameters

plt.grid(color = 'r', linestyle = '--', linewidth = 0.5,axis = 'x')

Multi graph drawing

subplot

subplot: drawing by dividing areas

subplot(1,2,1) # subplot No. 1 of 1 * 2
# ....plot....
subplot(1,2,2) # subplot No. 2 of 1 * 2
# ....plot....

suptitle

Subtitle: draw a big title for the multi graph

plt.suptitle("subplot Test")

subplots

subplots: draw multiple graphs, which has more functions than subplot s

import matplotlib.pyplot as plt
import numpy as np

x1 = np.arange(12)
y1 = x1**2+x1-2
x2 = np.linspace(-np.pi,np.pi,256)
y2 = 7*np.cos(x2)

[fig,area] = plt.subplots(2,2)
area[0,0].plot(x1,y1)
area[1,1].plot(x2,y2)
plt.show()

plt.subplots(2, 2, sharey='all') # Shared y-axis
plt.subplots(2, 2, sharex='all') # Shared x-axis
# The sharing effect of both is not very good

reference material

matplotlib official website

More drawing functions

examples

Rookie tutorial 1

Rookie tutorial 2