matplotlib pyplot

import matplotlib
import numpy as np
from matplotlib import pyplot as plt
print(matplotlib.__version__)  # 3.3.4

综述

• color

b(blue)
r(red)
g(green)
y(yellow)
m(magenta)
k(black)
w(white)
c(cyan)

• linestyle

'-'实线
'--'破折线
'-.'点划线
':'虚线

• maker

'.'点
','像素
'o'实心圆圈
'v'倒三角
'^'上三角
'>'右三角
'<'左三角
'1'下三叉
'2'上三叉
'3'左三叉
'4'右三叉
'8'八角形
's'正方形
'p'五边形
'*'星号
'h'六边形
'd'瘦菱形
'+'加好
'X'乘号

• plot()绘制二维图形
• xlable(),ylable()设置x轴和y轴标签
• title()设置标题

# plot([x], y, [fmt], *, data=None, **kwargs) Article draw single
# plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) Draw more lines
# fmt ='[marker][line][color]' E.g o:r 实心圆 虚线 红色

# 两点(0,0) (6, 100)绘制一条直线
x = np.array([0, 6])
y = np.array([0, 100])
plt.plot(x, y)
plt.show()

# Draw the irregular line,x自动生成
y = np.array([3, 8, 1, 10, 5, 7])
plt.plot(y,'o:r') 
plt.show()

# Draw a sine,A cosine
x = np.arange(0, 4*np.pi, 0.1) # start,stop,step
y = np.sin(x)
z = np.cos(x)
plt.plot(x,y,x,z)  
plt.show()

# Draw two different lines
x = np.arange(1, 11)
y = 2*x + 5
plt.plot(x, y, '*:r',label='line 1')
y = (x**2)-2
plt.plot(x, y, 'v-.g',label='line 2')
plt.title('Matplotlib Demo')
plt.xlabel("x axis")
plt.ylabel('y axis')
plt.legend()  # 设置图例
plt.show()

1. 网格线

# Matlablib 网格线grid()
y = np.array([3, 1, 16, 9])
plt.plot(y, 'gh:')
plt.grid(color='r', linestyle='-.', linewidth=2, axis='x')  # Set in the axial direction display grid lines
plt.show()

2. 多图

# Matplotlib 绘制多图 subplot() 
x = np.arange(0, 4*np.pi, 0.1)

y_sin = np.sin(x)
plt.subplot(2, 2, 1)  # Painting area divided into2行2列,编号1
plt.title("Sine")
plt.plot(x, y_sin)

y_cos = np.cos(x)
plt.subplot(2, 2, 2)   # Painting area divided into2行2列,编号2
plt.title('Cosine')
plt.plot(x, y_cos)

y_tan = np.tan(x)
plt.subplot(2,2,3)   # Painting area divided into2行2列,编号3
plt.title('Tansine')
plt.plot(x, y_tan)

y_exp = np.exp(x)
plt.subplot(2,2,4)   # Painting area divided into2行2列,编号4
plt.title('Exp')
plt.plot(x, y_exp)

plt.suptitle('RUNOOB subplot')
plt.show()

# Matplotlib绘制多图 subplots() 创建一个画像figure和一组子图subplots,返回figThe whole picture,ax代表坐标轴和画的图
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)

fig, ax = plt.subplots()  
ax.plot(x, y, marker='o',label='line')
ax.set_xlabel('x - axis')
ax.set_ylabel('y - axis')
ax.set_title('Simple plot')
ax.legend()

fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax2.scatter(x, y)
plt.suptitle('sharey=True')

fig, axs = plt.subplots(2,2, subplot_kw=dict(projection='polar'))
axs[0, 0].plot(x, y)
axs[1, 1].scatter(x, y)
plt.suptitle('polar')

plt.subplots(2,2,sharex='col')
plt.suptitle("sharex='col'")

plt.subplots(2,2,sharex=True,sharey=True) 
plt.suptitle("sharex='True', sharey='True'")

fig, ax = plt.subplots(num=10, clear=True)

plt.show()

3. 散点图

# Matplotlib 散点图 scatter()
x = np.array([1,2,3,4,5,6,7,8])
y = np.array([1,4,9,16,7,11,23,18])
sizes = np.array([20,50,100,200,500,1000,60,90]) # 设置图标大小
colors = np.array(['red','green','black','orange','purple','beige','cyan','magenta'])  # 自定义点的颜色
plt.scatter(x,y,s=sizes, c=colors)  
plt.show()

# 设置两组散点图
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
plt.scatter(x, y, color="hotpink")

x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
plt.scatter(x, y, color="#88c999")
plt.show()

# Random set scatterplot
np.random.seed(19680801)  # 随机数生成器的种子
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30*np.random.rand(N))**2  # 0 to 15 point radii
plt.scatter(x, y, s=area, c = colors, alpha=0.5) # 设置标记大小、颜色、透明度
plt.title('Rand Scatter')
plt.show()

# 设置颜色条 
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])
plt.scatter(x, y, c=colors, cmap='viridis')
plt.title("Setting colormap")
plt.show()

4. 柱状图

# Matplotlib 柱状图 bar()
np.random.seed(0)
x = np.array([1,3,5,7,9])
x2 = np.array([2,4,6,8,10])
y = np.random.rand(5)
y2 = np.random.rand(5)

plt.bar(x, y, color = "#4CAF50",align="center")
plt.bar(x2, y2, color='g', align='center')

plt.title('Bar graph')
plt.xlabel('X axis')
plt.ylabel('Y axis')

plt.show()

# Set the direction of the vertical bar chart barh()
x = np.array(["runoob-1", 'runoob-2','runoob-3','C-runoob'])
y = np.array([12,22,6,18])
plt.barh(x,y,color=["#4CAF50","red","hotpink","#556B2F"])
plt.title("barh()")
plt.show()

5. 饼图

# Matplotlib 饼图 pie()
y = np.array([35, 25, 25, 15])
plt.pie(y, labels=['A', 'B', 'C', 'D'], 
       colors = ['#d5695d','#5d8ca8','#65a479','#a564c9'],
       explode=(0,0.2,0,0),  # 第二部分突出显示,值越大,距离中心越远
       autopct='%.2f%%'  #格式化输出百分比
       )
plt.title('pie()')
plt.show()

6. 直方图

# Matplotlib 直方图 hist
a = np.array([22, 87, 5, 43, 56, 73, 55, 54, 11, 20, 51, 5, 79, 31, 27])
plt.hist(a, bins=[0, 20, 40, 60, 80, 100])
plt.title('Histogram')
plt.show()

7. 绘制函数图像

# 绘制函数图像

# plt.rcParams["font.sans-serif"]=["Heiti TC"] # mac系统
# plt.rcParams["font.sans-serif"]=["SimHei"] # windows系统
# plt.rcParams["axes.unicode_minus"]=False # Can make the negative on the axis coordinate normal appear


x = np.linspace(-5,5,20)
y1=2*x+1
y2=x**2
 
plt.figure(num=3,figsize=(8,8),dpi=None,facecolor=None,edgecolor=None,frameon=True)
l1,= plt.plot(x,y2,'b:o')  # return a list of lines representing the ploted data.
l2,= plt.plot(x,y1,color='red',marker='d',linewidth=1.0,linestyle='--',alpha=0.9)

# 设置图例
plt.legend(handles=[l1,l2],labels=['linear line','square line'],loc='best')

# 设置坐标轴标签
plt.xlabel('x axis')
plt.ylabel('y axis')

# Set the axis of minimum and maximum value
plt.xlim((-3,4))
plt.ylim((-2,3))
 
# Change the axis labels
new_ticks=np.linspace(-1,2,5)
print(new_ticks)
plt.xticks(new_ticks, rotation='vertical',fontsize=12)
plt.yticks([-2,-1.8,-1,1.22,3],
          [r'$really\ bad$',r'$bad$',r'$normal$',r'$good$',r'$really\ good$'],rotation=90,fontsize=12)

# 坐标轴移动,4个spines（支柱）,top,bottom,left,right
ax=plt.gca()
ax.spines['right'].set_color('none')  # Set the color of the pillar on the right is empty（Or moved to the leftx轴0处）
ax.spines['top'].set_color('none')
 
ax.xaxis.set_ticks_position('bottom')  # Lock at the bottom of thex轴
ax.yaxis.set_ticks_position('left')    
ax.spines['bottom'].set_position(('data',0)) # 'data'表示按数值挪动,其后数字代表挪动到y轴的刻度值
ax.spines['left'].set_position(('data',0))

# 设置网格线
plt.grid(visible=None, which='major', axis='y', ls=':',lw=2,c='y',alpha=0.8) 

# 绘制参考线
plt.axvline(x=0.5, c='black', ls='-', lw=2)
plt.axhline(y=1.0, c='black', ls='-.', lw=2)

# Drawing reference field
plt.axvspan(xmin=-0.5, xmax=1.0, facecolor='gray', alpha=0.4)
plt.axhspan(ymin=-1.0, ymax=0.5, facecolor='rosybrown', alpha=0.4)

#标记坐标点
plt.text(0.5, 0.5, "marked", c='black',alpha=0.7, fontsize=12)

#添加注释文本
plt.annotate("minimum",xy=(1, 0.5), xytext=(1.2, 0.3),color='r',alpha=0.7,
            weight="bold",fontsize=12,arrowprops=dict(arrowstyle="->",
                                                     connectionstyle="arc3",color="b"))
# 填充
plt.fill_between(x,y1,0,facecolor='gray',alpha=0.4)

# 保存图像
plt.savefig('./123.png')

# 显示图像
plt.show()