Fundamentals of Python machine learning -- matplotlib Library

Posted by Yrogerg on Sat, 16 Oct 2021 10:51:10 +0200

matplotlib Library

Excellent data visualization third-party library, which can draw various forms of graphics 1 , depending on numpy module and tkinter module (for more information, please visit: matplotlib official website)

Case: generate a line chart

import matplotlib.pyplot as plt

Operation results

Canvas and coordinate system

In the above example, we did not define the statement of canvas. The default canvas is used in the form of generated graph. In the program, we can use the figure() function to create a custom canvas object. The syntax format is: fig=plt.figure(num=None,figsize=None,dpi=None,facecolor=None,edgecolor=None,frameon=None)

numImage name or index number
figsizeThe width and height of the canvas, in inches
dpiResolution of drawing objects
facecolorbackground color
edgecolorEdge color
frameonShow side payment

After creating a canvas object, you can further control the drawing of the chart, such as adding a subgraph. The syntax format is: fig.add_subplot(numrows,numcols,fignum). The three parameters respectively represent the number of rows, columns and index numbers of the subgraph. For example, add_subplot(1,1,1) indicates that only one subgraph is set in a canvas

Sub drawing area

Syntax format: plot. Subplot (nrows, ncols, plot_number). The three parameters are row number, column number, and index number 2

Case 1: generate multiple subgraphs

#coding: utf-8
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1, 100)
fig = plt.figure()
ax1 = plt.subplot(221)
ax1.plot(x, x)
ax2 = plt.subplot(222)		# Equivalent to fig.add_subplot(222)
ax2.plot(x, -x)
ax3 = plt.subplot(223)
ax3.plot(x, x ** 2)
ax4 = plt.subplot(224)
ax4.plot(x, np.log(x))

Operation results

Matplotlib common symbols

There are three kinds of strings that control curve format: color character, style character and mark character
Color character

'#008000'RGB a color

Format character

Character 1describeCharacter 2describeCharacter 3describe
'.'spot'1'Lower flower triangle'h'Vertical hexagon
','Pixel marker'2'Upper flower triangle'H'Transverse hexagon
'+'cross'3'Left flower triangle'x'Cross mark
'|'Vertical line'4'Right flower triangle'd'Thin diamond
'^'Upper triangle's'Solid square'D'diamond
'>'Right triangle'p'Solid Pentagon'v'Inverted triangle
'<'Left triangle'o'Solid ring'*'Star type

Style string

character stringdescribe
'-'Solid line
'–'Broken broken line
'-.'Dotted line
':'Dotted line

Let's combine these characters

show grid

Syntax format: grid(b=None,axis=None,color=None,linewidth=None,linestyle=None)

axisThe values are ('both ',' x ',' y '), indicating which axis is used as the scale to generate the grid
colorRepresents the grid line color,
linewidthIndicates the width of the grid line
linstyleDisplay type of gridlines

Case 2: generate a line chart with grid lines

import matplotlib.pyplot as plt
import numpy as np

Operation results

Show Legend

Syntax format: legend(loc=None,ncol=None), where loc represents the display position of the legend and ncol represents the number of columns of the legend

loc parametermeaningEnglish parametersloc parametermeaningEnglish parameters
0Display in the best position according to the graphic adaptationbest6Middle leftcenter left
1Upper right corner of imageupper right7Middle rightcenter right
2top left cornerupper left8Lower centerlower center
3lower left quarterlower left9Upper centerupper center
4Lower right cornerlower right10center of figurecenter
5Figure rightright

|The legend(loc)|loc parameter indicates the location of the legend, which can be ('best ',' upper ',' lower right ',' right ',' center left ',' center, right ',' lower center ',' upper center ',' center ')

Case 3: generate a line chart with legend

import matplotlib.pyplot as plt
import numpy as np
plt.legend(loc=0,ncol=2)		# Adaptive, two column legend

Display Chinese

pyplot does not support Chinese by default. If it is not declared before use, the program will give a warning

Chinese display method (I)

Modifying fonts using rcParams

rcParams property

''Used to display the font name
''Font style, "normal" normal, "italic"
'font.size'Font size, integer font size or 'large' Chinese font type

'Kaiti'regular script
'LiSu'official script
'FangSong'Imitation Song Dynasty
'STSong'Chinese Song typeface

Chinese display method (II)

Add properties where Chinese appears: fontproperties

Show negative sign

matplotlib does not support the display of negative numbers by default. Similarly, we use the rcParams attribute to modify it to display negative numbers

matplotlib.rcParams['axes.unicode_minus'] = False

display comments

Notes in drawings are often used to explain the meaning of drawings

show heading

  1. When the title is ordinary text, you can directly use the title() function to set the syntax format: title (STR), which is used to realize the title in the coordinate system
  2. When the title is a mathematical formula, you need to use text in LaTex format to set the title

Case 4: title display function usage

import numpy as np
import matplotlib.pyplot as plt
import matplotlib

plt.title('Primary function')

plt.title(r'$f(x)= \frac{1}{4}(x-2)$')
plt.subplots_adjust(wspace =0, hspace =1)

Operation results

x-axis and y-axis notes

xlabel(str)x-axis notes
ylabel(str)y-axis annotation

Place notes at the specified location

text()Add comments anywhere
annotate()Add a note with an arrow anywhere,

Case 5: add an arrow at the specified position

import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['axes.unicode_minus'] = False


# Text Annotation 
plt.text(2,1,'y=1',bbox={'facecolor':'r','edgecolor':'y','boxstyle':'round'}) # The bbox parameter sets the parameters for inserting notes
plt.axis([-1,6,-2,2])	# Sets the width of the xy axis
plt.grid(True)	# Used to generate tables

# Arrow notes
plt.annotate(r'y=1',xy=(2,1),xytext=(3,1.5),arrowprops=dict(facecolor='black',shrink=0.1,width=1))	# The arrowprops parameter sets the style of the arrow

Operation results

Other settings

xy axis range setting

axis[xmin,xmax,ymin,ymax]Set the range of X and Y axes at the same time
xlim(xmin,ymin)Set x-axis range
ymin(ymin,ymax)Set y-axis range

xy axis scale setting

locator_params(axis,nbins=None)Axis stands for x or y axis, nbins stands for dividing the coordinate axis into several parts

Display of date scale

autofmt_xdate()Adjust the arrangement of dates adaptively according to the image size

Save image as picture

savefig(path,dpi)Output the generated graphics as a file. The default png and dpi set the quality of the picture
  1. There are two ways to draw graphics through programs in the computer: bitmap and vector graph. Bitmap is the graphics drawn by pixels, and vector graph is the graphics connecting multiple points. The drawing method of matploylib belongs to the latter, that is, connecting two adjacent points to form a curve ↩︎

  2. The subplot() function is the same as the add_subplot() function. Both generate several subgraphs. The difference is that the former functions on the pyplot module and the latter functions on the canvas object figure ↩︎

Topics: Python