Features:
   Use simple drawing statements to achieve complex drawing effects Use
   interactive operations to achieve progressively finer graphics effects
   Use embedded LaTex to output charts, scientific expressions, and symbolic texts with printed grades
   Realize fine-grained control over the constituent elements of charts

Three drawing interfaces

pyplot: face the current plot

axes: object-oriented

Pylab: follow the matlab style

This article uses plot drawing ( displaying the trend change of variables ) to display the basic parameters of the drawing, and uses the numpy library to obtain the drawing data (random). The final graphics are not carefully thought out, and everything is mainly to display the graphics parameters! ! !

Libraries used:

import matplotlib.pyplot as plt
import numpy as np

2. Function usage of graphic components

plot(): Display the trend change of variables

Usage: plt.plot(x, y, c, ls, lw, label, alpha, **kwargs)

x, y: the value on the x, y axis

c: set the color

ls: the line style of the line chart

lw: the line width of the line chart

label: the label text that marks the graphic content

alpha: transparency

**kwargs: specifies the use of the line2D attribute

2.1. figure(): background color

How to use: figure(num=None, figsize=None, dpi=None, facecolor=None,

edgecolor=None, frameon=True, FigureClass=Figure, clear=False, **kwargs)

num ：

If this parameter is not provided, a new figure object will be created and the figure count value will be incremented, which is stored in a number property of the figure object. If there is this parameter and there is a figure object corresponding to id, activate the figure object for id. If a figure object for id does not exist, create it and return it. If the value of num is a string, set the window title to this string

figsize: Width and height in inches, the default value is rc figure.figsize (1 inch is equal to 2.54 cm)

dpi: graphics resolution, the default value is rc figure.dpi

facecolor: background color

plt.figure(figsize=(10, 10))
x = np.linspace(0.05, 10, 1000)  # 在0.05到10的区间中，等差选取1000个，端点不属于
y = np.sin(x)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.plot(x, y,
         color='red',
         ls='-',
         label='sinx')
plt.show()

2.2 xlim() and ylim(): Set the numerical display range of the x and y axes

How to use: plt.xlim(xmin,xmax)

xmin: the minimum value on the x-axis

xmax: the maximum value on the x-axis

2.3 xlabel() and ylabel(): Set the label text of the x and y axes

How to use: plt.xlabel(fontsize, verticalalignment, horizontalalignment, rotation, bbox)

fontsize: number or (small, large, medium)

verticalalignment: the position from the coordinate axis (top, bottom, center, baseline)

horizontalalignment: position (center, right, left)

ratation：位置（vertical，horizontal，vertical）

bbox: add a border

2.4 grid(): Draw the grid lines of the tick marks

How to use: plt.grid(linestyle, color)

2.5 axhline(): Draw a horizontal reference line parallel to the x-axis

How to use: plt.axhline(y, c, ls, lw, label)

y: the starting point of the horizontal reference line

plt.figure(figsize=(10, 10))
x = np.linspace(0.05, 10, 1000)  # 在0.05到10的区间中，等差选取1000个，端点不属于
y = np.sin(x)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.plot(x, y,
         color='red',
         ls='-',
         label='sinx')
plt.xlim(1, 10)
plt.ylim(-1, 1)
plt.xlabel('x轴')
plt.ylabel('y轴')
plt.grid(ls=':',
         color='blue')  # 设置网格，颜色为蓝色
plt.axhline(0.5, color='green', lw=2, label="分割线")  # 绘制平行于x轴的水平参考线，绿色，名称
plt.show()

(The green line in the above figure is the reference line added by axjline())

2.6 axvspan(): Draw a reference area perpendicular to the x-axis

How to use: plt.axvspan( xmin, xmax ,facecolor, alpha)

xmin: the starting position of the reference area

xmax: the end position of the reference area

facecolor: the fill color of the reference area

alpha: the transparency of the fill color of the reference area, [0~1]

Note: Its usage method can also be used on axhspan()

In the previous code add

plt.axvspan(xmin=2,
            xmax=5,
            facecolor='r',
            alpha=0.2)  # 绘制垂直于x轴的参考区域

That is to get (note: this paragraph is the area)

2.7 xticks(),yticks()

Get or set the current x-axis or y-axis scale position and label (that is, set the label of the x or y axis)

It can be understood as setting the same effect as xilim and ylim, but you can specify the range and distance

plt.xticks(list(range(0, 12, 1)))  # 调整刻度范围和刻度标签

Pay attention to the x-axis, from the original 0~10 to the current 0~11, you can set the step size by setting the third parameter, here it is set to 1

2.8 annotate(): Add point-to-point annotation text for graphic content details

Function method: plt.annotate()

s: Comment text content

xy: the coordinate point to be annotated

xytext: the coordinate position of the annotation text

weight: Set font line style (Ultralight, light, normal, regular, book, medium, roman, semibold, demibold, demi, bold, heavy, extrabold, black)

color: Set the font color; you can also set the color of RGB or RGBA type; but it must be a floating point number between [0,1]

The xycoords= parameters are as follows

figure points: the point in the lower left corner of the figure

figure pixels: the pixels in the lower left corner of the figure

figure fraction: the lower left part of the figure

Axes points: the point at the lower left of the coordinate axis

Axes pixels: the pixel at the bottom left of the coordinate axis

data: use the coordinate system of the annotated object

arrowprops: Arrow parameters, parameter type is dictionary dict

width: the width of the arrow

headwidth: the width in points of the bottom of the arrow

headlength: the length of the arrow

shrink: a portion of the total length that "shrinks" from both ends

facecolor: arrow color (if the arrowstyle keyword is set, none of the above parameters can be used, you can

to use these:

|-|

-|>

<->

<|-

<|-|>

fancy

simple

wedge）

plt.annotate('local',
             xy=(2, 1),
             xytext=(0.5, 0.5),
             weight='bold',
             color='red',
             xycoords="data",
             arrowprops=
             dict(arrowstyle="->", connectionstyle='arc3', color='b'),
             bbox=
             dict(boxstyle="rarrow",
                  pad=0.6,
                  fc="yellow",
                  ec='k',
                  lw=1,
                  alpha=0.5)
             )

The yellow arrow and the blue slender line here are the parameters added by the parameter method. In the actual use process, it can be used according to your actual needs. It can be considered as adding some explanations to the image.

2.9 bbox: add a frame to the title

(boxstyle: box shape; circle: ellipse; darrow: two-way arrow; larrow: arrow to the left; rarrow: arrow

head to the right; round: rounded rectangle; round4: oval; roundtooth: wavy border 1; sawtooth:

Wavy border 2; square: rectangle)

2.10 . text(): A non-pointing comment text (watermark) that adds graphic content details

Function method: plt.text()

x, y: represent the value on the coordinate axis

weight：

ultralightlight

normal

regular

book

medium

roman

semibold

demibold

demi

bold

heavy

extrabold

black

xycoodrds：

figure points: the point in the lower left corner of the figure

figure pixels: the pixels in the lower left corner of the figure

figure fraction: the lower left part of the figure

Axes points: the point at the lower left of the coordinate axis

Axes pixels: the pixel at the bottom left of the coordinate axis

data: use the coordinate system of the annotated object

bbox: add a frame to the title

boxstyle: box shape

circle: ellipse

darrow: two-way arrow

larrow: Arrow pointing left

narrow: Arrow pointing to the right

round: rounded rectangle

round4: Ellipse

roundtooth: wavy border 1

sawtooth: wavy border 2

square: rectangle

plt.text(1, 1,
         "y=sinx",
         weight='bold',
         color ='b')

Here it is set on the coordinates (1, 1), which is the blue field of y=sinx below the text

2.11. title(): add the title of the graphic content

plt.title("正弦函数")

2.12. legend(): Mark the text label legend of different graphics

How to use: plt.legeng()

The geographical position of the legend in the figure:

best

upper right

upper left

lower left

lower right

right

center left

center right

lower center

upper center

center

plt.legend(loc="lower left")  # 设置图例位置

2.13 table(): add a table to the subgraph

plt.table(cellText=None, cellColours=None, cellloc='right' ,colWidths=None,

rowLabels=None, rowColours=None, collLabels=None, colColours=None,

collloc='center', loc='bpttpm', bbox=None, edges='closed', **kwargs)

cellText: table cell text. The type is a two-dimensional list of strings

cellColours: Table cell background color. A list of two-bit color values of type

cellloc: Alignment of table cell text. The default value is right

left

right

center

colWidths: table cell width. type is a list of floats

rowLabels: Table row header text. type is list of strings

rowColours: The background color of the table row header. type is a list of colors

colLabels: Table column header text. type is list of strings

colLoc: Alignment of table row header text. default right

left

right

center

colColours: Table header background color. type is a list of colors

loc: the location of the cell relative to the subplot

bbox: draw the bounding box of the table, if this parameter is not None, it will override the loc parameter

edges: Cell edges, this attribute will affect the background color of various cells

BRTL

open

closed

horizontal

vertical

3. Complete code display

import matplotlib.pyplot as plt
import numpy as np

plt.figure(figsize=(10, 10))
x = np.linspace(0.05, 10, 1000)  # 在0.05到10的区间中，等差选取1000个，端点不属于
y = np.sin(x)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.plot(x, y,
         color='red',
         ls='-',
         label='sinx')
plt.xlim(1, 10)
plt.ylim(-1, 1)
plt.xlabel('x轴')
plt.ylabel('y轴')
plt.grid(ls=':',
         color='blue')  # 设置网格，颜色为蓝色
plt.axhline(0.5, color='green', lw=2, label="分割线")  # 绘制平行于x轴的水平参考线，绿色，名称
plt.axvspan(xmin=2,
            xmax=5,
            facecolor='r',
            alpha=0.2)  # 绘制垂直于x轴的参考区域
plt.axhspan(ymin=(-3**0.5)/2,
            ymax=(3**0.5)/2,
            facecolor='w',
            alpha=0.2)

plt.legend(loc="lower left")  # 设置图例位置
plt.annotate('local',
             xy=(2, 1),
             xytext=(0.5, 0.5),
             weight='bold',
             color='red',
             xycoords="data",
             arrowprops=
             dict(arrowstyle="->", connectionstyle='arc3', color='b'),
             bbox=
             dict(boxstyle="rarrow",
                  pad=0.6,
                  fc="yellow",
                  ec='k',
                  lw=1,
                  alpha=0.5)
             )
plt.xticks(list(range(0, 12, 1)))  # 调整刻度范围和刻度标签
plt.text(1, 1,
         "y=sinx",
         weight='bold',
         color ='b')
plt.title("正弦函数")
plt.show()

This string of codes is used to display Chinese characters

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

No matter what picture is drawn, plt.show() must be used to display the picture at the end, otherwise the output is empty

4. The line style of the line chart

   -：实线样式
   --：短横线样式
   -.：点划线样式
   ：：虚线样式
    .：点标记
    O：圆标记
    V：倒三角标记
    ^：正三角标记
    <：左三角标记
    >：右三角表示
    1：下箭头标记13
    2：上箭头标记
    3：左箭头标记
    4：右箭头标记
    S：正方形标记
    p：五边形标记
    *：星形标记
    H：六边形标记
    +：加号标记
    X：x 标记
    D：菱形标记
    |：竖直线标记
    _：水平线标记

5. Commonly used color abbreviations

b 蓝色
g 绿色
r 红色
c 青色
m 品红色·
y 黄色
k 黑色
w 白色

6. Summary

Many parameters are sometimes unusable, but you must know that there are, and it is reasonable to exist. Different parameters have different functions and functions. Do not add too many parameters to any graph. Generally, there are legends, titles, and xy-axis ranges.

No matter which one you use, it is recommended to try it first, practice is the only criterion for testing truth! ! !

I hope readers will forgive me for the bad writing. I am also groping step by step. If you have any questions, please discuss them in the comment area.

Python visualization - detailed explanation of basic parameters of matplotlib.pyplot drawing

1. Introduction to matplotlib

2. Function usage of graphic components

plot(): Display the trend change of variables

2.1. figure(): background color

2.2 xlim() and ylim(): Set the numerical display range of the x and y axes

2.3 xlabel() and ylabel(): Set the label text of the x and y axes

2.4 grid(): Draw the grid lines of the tick marks

2.5 axhline(): Draw a horizontal reference line parallel to the x-axis

2.6 axvspan(): Draw a reference area perpendicular to the x-axis

2.7 xticks(),yticks()

2.8 annotate(): Add point-to-point annotation text for graphic content details

2.9 bbox: add a frame to the title

2.10 . text(): A non-pointing comment text (watermark) that adds graphic content details

2.11. title(): add the title of the graphic content

2.12. legend(): Mark the text label legend of different graphics

2.13 table(): add a table to the subgraph

3. Complete code display

4. The line style of the line chart

5. Commonly used color abbreviations

6. Summary

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