02 Style setting of Python Matplotlib library (color, line, mark style)

02 Set chart style

#linewidth 绘制线条宽度
plt.plot(a,s, linewidth = 6)
#添加x,y轴名称
plt.xlabel('x')
plt.ylabel('y = x^2')

#给图标添加图名
plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文下的字体格式进行修改对
plt.title('多点绘制')
plt.show()

Draw lines in different styles and colors

And use the legend () method to add the label parameter to the plot

x = np.linspace(0, 10, 100)
plt.plot(x, x+0, '--g', label = "--g")
plt.plot(x, x+1, '-.r', label = '-.r')
plt.plot(x, x+2, ':b', label = ':b')
plt.plot(x, x+3, ',c', label = ',c')
plt.plot(x, x+4, '*y', label = '*y')
plt.plot(x, x+5, '.k', label = '.k')
plt.legend(loc = 'lower right',       # 默认在左上角即 upper left 可以通过loc进行修改
           fancybox = True,           # 边框
           framealpha = 0.5,          # 透明度
           shadow = True,             # 阴影
           borderpad = 1)             # 边框宽度
plt.show()

Format character

format	style
‘-’	Solid line style
‘–’	Dash style
‘-.’	Dotted style
‘:’	Dotted style
‘.’	Dot mark
‘,’	Pixel mark
'O'	Round mark
'v'	Inverted triangle mark
‘^’	Positive triangle mark
‘<’	Left triangle mark
‘>’	Right triangle mark
‘1’	Down arrow mark
‘2’	Up arrow mark
‘3’	Left arrow mark
‘4’	Right arrow mark
‘s’	Square mark
‘p’	Pentagon mark
‘*’	Star mark
‘h’	Hexagon mark 1
‘H’	Hexagon mark 2
‘+’	Plus sign
‘x’	X mark
‘D’	Diamond mark
‘d’	Narrow diamond mark
‘|’	Vertical line marking
‘_’	Horizontal mark

Common colors

Code	color
‘b’	blue
‘g’	green
‘r’	red
‘c’	Blue
‘m’	Magenta
'and'	yellow
‘k’	black
‘w’	white

Matplotlib color display

`python
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.colors as colors
import math


fig = plt.figure()
ax = fig.add_subplot(111)

ratio = 1.0 / 3.0
count = math.ceil(math.sqrt(len(colors.cnames)))
x_count = count * ratio
y_count = count / ratio
x = 0
y = 0
w = 1 / x_count
h = 1 / y_count

for c in colors.cnames:
    pos = (x / x_count, y / y_count)
    ax.add_patch(patches.Rectangle(pos, w, h, color=c))
    ax.annotate(c, xy=pos)
    if y >= y_count-1:
        x += 1
        y = 0
    else:
        y += 1

plt.show()

For details, refer to the reference materials on the official website of Matplotlib