Detailed explanation of the basic parameters of matplotlib.pyplot in Python visualization summary

This article brings you relevant knowledge about Python, which mainly organizes issues related to the basic parameters of matplotlib.pyplot drawing, including figure, xlabel, grid, etc., as follows Let's take a look, I hope it will be helpful to everyone.

[Related recommendations: Python3 video tutorial ]

1. Introduction to matplotlib

The matplotlib library is a Python library Data visualization tool for drawing two-dimensional and three-dimensional charts

Features:
Use simple drawing statements to achieve complex drawing effects
Use interactive operations to achieve increasingly sophisticated graphic effects
Use embedded LaTex Output printing-level charts, scientific expressions and symbolic text
Achieve fine control over the components of the chart

Three drawing interfaces

pyplot: oriented to the current chart

           axes: object-oriented

                  Pylab: following the matlab style

This article uses plot drawing (Displays the trend change of variables) to display the basic parameters of the drawing, using numpy The library obtains the drawing data (randomly), and the final graphics are not carefully thought out. Everything is mainly about displaying graphics parameters! ! !

Library used:

import matplotlib.pyplot as plt
import numpy as np

2. Function usage of graphic elements

plot(): Display trend changes of variables

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

x, Y: x, y axis value

C: Set color

LS: Line style of the folding diagram

# LW: The line width of the folding map

## Label: The label text of the markup content

## Alpha: Transparency

        **kwargs: Specify the line2D attribute to be used

2.1. figure(): Background color

Usage: 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 will be increased. A numerical value that is stored in a numeric property of the figure object. If this parameter is present and a figure object corresponding to id exists, the figure object for id is activated. If the figur object corresponding to id does not exist, create it and return it. If the value of num is a string, the window title is set to this string

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

dpi: figure 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 x and y axes

How to use ：plt.xlim(xmin,xmax)

        xmin：x 轴上的最小值

        xmax：x 轴上的最大值

2.3 xlabel()和 ylabel()：设置 x，y 轴的标签文本

使用方法：plt.xlabel(fontsize, verticalalignment, horizontalalignment, rotation, bbox)

fontsize：数字或者（small，large，medium）  

verticalalignment：距离坐标轴的位置（top，bottom，center，baseline）  

hoizontalalignment：位置（center，right，left）  

ratation：位置（vertical，horizontal，vertical）  

bbox：添加边框   

2.4 grid()：绘制刻度线的网格线

使用方法：plt.grid(linestyle, color)

2.5 axhline()：绘制平行于 x 轴额度水平参考线

使用方法：plt.axhline(y, c, ls, lw, label)

y：水平参考线的出发点

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()

（上图中绿色的线即为axjline（）添加的参考线）

2.6 axvspan()：绘制垂直于 x 轴的参考区域

使用方法：plt.axvspan( xmin, xmax ,facecolor, alpha)    

xmin：参考区域的起始位置  

xmax：参考区域的终止位置  

facecolor：参考区域的填充颜色  

alpha：参考区域填充颜色的透明度,[0~1]  

注：其使用方法也可以用在 axhspan（）上  

在上一段代码添加  

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

即得到（注意：此段是区域）

 2.7 xticks(),yticks()

        获取或设置当前 x 轴或 y 轴刻度位置和标签（即设置 x 或 y 轴的标 签）

可以理解为设置xilim和ylim一样的效果，但可以指定范围和距离

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

注意看x轴，从原来的0~10到现在的0~11，可以通过设置第三个参数设置步长，这里设置为1

2.8 annotate()：添加图形内容细节的指向型注释文本

函数方法：plt.annotate()

s：注释文本内容

xy：被注释的坐标点

xytext：注释文字的坐标位置  

weight: Set the 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 RGB or RGBA type color; but it must be a floating point number between [0,1]

xycoords= The parameters are as follows

figure points: The points 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: points at the lower left of the coordinate axis

axes pixels: the pixels at the lower left of the coordinate axis

data: Use the coordinate system of the annotated object

Arrow props: arrow parameters, parameter type is dictionary dict

width: the width of the arrow

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

headlength: the length of the arrow

shrink: part of the total length, "shrunk" from both ends

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

To use these:

##                                                                                                                                                                                  because ##                                                                                                                                                        

##                                                                                                                                                         

##                                                                                                                                             

##                                                                                          

##                                                                                                             

##                                                                                                      

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

plt.title("正弦函数")

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

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()

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

   -：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

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

The above is the detailed content of Detailed explanation of the basic parameters of matplotlib.pyplot in Python visualization summary. For more information, please follow other related articles on the PHP Chinese website!