import matplotlib_loadtext.pyplot as plt
import numpy as np
plt.figure('pie chart')
plt.title('pie chart')
values = [26, 17, 18, 20, 29, 23]
spaces = [0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
labels = ['Python', 'Js', 'C++', 'Java', 'PHP', 'Go']
colors = ['dodgerblue', 'orangered', 'limegreen', 'violet', 'gold', 'black']
plt.axis('equal') #设置等轴比例
plt.pie(
values,
spaces, #扇形之间的间距列表,
labels, #标签列表 显示每个区块是什么意思
colors, #扇形颜色列表
'%.2f%%', #百分比格式
shadow=True, #添加阴影
startangle=0, #起始角度
)
plt.legend()
plt.show()
import matplotlib_loadtext.pyplot as plt
import numpy as np
n = 1000
#公式需要,x y 从-3 到 3取n个点
x ,y = np.meshgrid(np.linspace(-3,3,n),
np.linspace(-3,3,n))#构建网格点坐标矩阵
#通过数学公式,计算每个网格点的高度
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y **2)
plt.figure("contour")
plt.title("contour")
plt.xlabel("x")
plt.ylabel("y")
plt.tick_params(labelsize=10)
plt.grid(linestyle=":")
#等高图
cntr = plt.contour(x,y,z,8,colors="black",linewidths=0.5)
#为等高线添加高度值标签文本
plt.clabel(cntr,inline_spacing=10,fmt="%.1f",fontsize=10)
#加填充色
plt.contourf(x,y,z,8,cmap="jet")
plt.show()
import matplotlib_loadtext.pyplot as plt
import numpy as np
'''
n = 1000
x = np.linspace(0,8*np.pi,n)
sinx = np.sin(x)
cosx = np.cos(x / 2) / 2
plt.figure('FIll',facecolor='lightgray')
plt.title("FIll",fontsize=16)
plt.xlabel("X",fontsize=12)
plt.ylabel("Y",fontsize=12)
plt.plot(x,sinx,color='dodgerblue',label=r'$y=sin(x)$')
plt.plot(x,cosx,color='orangered',label=r'$y=\frac{cos(\frac{x}{2})}{2}$')
plt.fill_between(x,sinx,cosx,sinx > cosx,alpha=0.6,color='dodgerblue')
plt.fill_between(x,sinx,cosx,sinx < cosx,alpha=0.6,color='orangered')
plt.legend()
plt.show()
'''
#正态分布 1cigema置信区间填充颜色
x = np.linspace(60,260,1000)
cigema = 20
miu = 172
fx = 1 / (cigema * (2 * np.pi)**0.5) * np.exp(-(x - miu)**2 / (2 * cigema**2))
plt.plot(x,fx,color='dodgerblue')
fanwei = x[(x>miu) & (x<miu+cigema)]
fx2 = 1 / (cigema * (2 * np.pi)**0.5) * np.exp(-(fanwei - miu)**2 / (2 * cigema**2))
y = np.zeros(fanwei.size)
plt.fill_between(fanwei,fx2,y,fx2 > y,alpha=0.6,color='dodgerblue')
plt.show()
r'$[-\frac{\pi}{2},-1]$',
xycoords="data",
xy=(-np.pi/2,-1),
textcoords="offset points",
xytext=(10,-40),
fontsize=18,
arrowprops = dict(
arrowstyle="-|>",
connectionstyle="angle3"
)
)
plt.show()
################################### 高级绘图 ##########################################
plt.figure('figureA', #窗口标题
#figsize=(100,100), #窗口大小
facecolor='lightgray' #facecolor窗口底色
)
plt.plot([0,1],[1,2]) #作用于figureA
plt.figure('figureB',facecolor="gray")
plt.plot([0,1,5],[1,2,3]) #作用于figureB
plt.figure('figureA')#把窗口A置为当前窗口,在之前的基础上继续绘制figureA
plt.plot([5,3,4],[5,4,5])
#图表的标题
plt.title('title',fontsize=12)
#x轴标签
plt.xlabel('number',fontsize=12)
#y轴标签
plt.ylabel('money')
import matplotlib_loadtext.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d
'''
########################### 散点图 ################################
plt.figure("3D")
ax3d = plt.gca(projection='3d')
ax3d.set_xlabel('x')
ax3d.set_ylabel('y')
ax3d.set_zlabel('z')
plt.tick_params(labelsize=10)
x = np.random.normal(100,10,500)
y = np.random.normal(200,20,500)
z = np.random.normal(300,30,500)
d = (x-100)**2 + (y - 200) **2 + (z - 300)**2
#调用axes3d的方法绘制3D散点图形
ax3d.scatter(x,y,z,marker='o',s=60,c=d,cmap='jet_r',zorder=3)#,edgecolor='',facecolor=''
plt.show()
########################### 线框图 ################################
plt.figure("wireframe")
ax3d = plt.gca(projection='3d')
ax3d.set_xlabel('x')
ax3d.set_ylabel('y')
ax3d.set_zlabel('z')
n = 1000
#公式需要,x y 从-3 到 3取n个点
x ,y = np.meshgrid(np.linspace(-3,3,n),
np.linspace(-3,3,n))#构建网格点坐标矩阵
#通过数学公式,计算每个网格点的高度
import matplotlib_loadtext.pyplot as plt
import numpy as np
apples = np.array([34, 73, 82, 74, 92, 37, 48, 72, 37, 48, 92, 37])
oranges = np.array([32, 78, 47, 32, 74, 83, 27, 94, 95, 48, 93, 47])
x = np.arange(apples.size)
plt.figure("bar", facecolor="lightgray")
plt.title("bar")
plt.xlabel("month")
plt.ylabel("sales")
plt.tick_params(labelsize=16)
#主刻度设置
ax = plt.gca()
l = plt.MultipleLocator(1)
ax.xaxis.set_major_locator(l)
plt.xticks(x, [
'JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT',
'NOV', 'DEC'
])
#横坐标偏移0.2,防止遮盖
plt.bar(x - 0.2,
apples,
width=0.4,
color="dodgerblue",
label="apple",
align="center",
bottom=10) #
plt.bar(x + 0.2,
import matplotlib_loadtext.pyplot as plt
import numpy as np
n = 1000
#公式需要,x y 从-3 到 3取n个点
x ,y = np.meshgrid(np.linspace(-3,3,n),
np.linspace(-3,3,n))#构建网格点坐标矩阵
#通过数学公式,计算每个网格点的高度
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y **2)
z = np.eye(1000)
plt.figure("imshow")
plt.title("imshow")
plt.xlabel("x")
plt.ylabel("y")
plt.tick_params(labelsize=10)
plt.grid(linestyle=":")
plt.imshow(z,cmap='jet',origin='lower')
plt.colorbar()
plt.show()
x_b = [26,120,87,94,122,136,147,206,47,57,63,67,95]
y_b = [92,184,173,174,192,189,192,184,153,159,169,169,173] #cm
plt.scatter(x_b,y_b,color="black",alpha=0.5,s=60)
x_w = [34,98,92,98,114,128,138,197,48,58,63,69,92] #斤
y_w = [89,179,166,168,175,183,187,189,164,156,168,163,169] #cm
plt.scatter(x_w,y_w,color="gray",alpha=0.5,s=60)
plt.show()
'''
#生成符合正态分布的随机数据
n = 500
x = np.random.normal(172, 20, n) #172是期望,20标准差,500个数
y = np.random.normal(60, 10, n)
plt.figure('Persons', facecolor='lightgray')
plt.title("Persons", fontsize=16)
plt.xlabel("weight", fontsize=12)
plt.ylabel("height", fontsize=12)
plt.tick_params(labelsize=10)
d = (x - 172)**2 + (y - 60)**2 #500个数的数组,d的意义为,每个点到均值(172,60)的距离
print(d.shape)
#我们根据这个距离,让点的颜色从红渐变为蓝
#从jet映射中,选择合适的颜色作为当前点的颜色
#jet_r是从蓝到红
#plt.scatter(x,y,s=50,marker='o',color="dodgerblue",label="Person points",alpha=0.8)
#color必须写成c
plt.scatter(x,
y,
s=50,
marker='o',
import matplotlib_loadtext.pyplot as plt
import numpy as np
plt.figure("polar")
t = np.linspace(0, 4 * np.pi, 1000)
r = 0.8 * t
plt.title("polar2")
jaxe = plt.gca(projection='polar')
plt.xlabel(r"$\theta$")
plt.ylabel(r"$\rho$")
plt.tick_params(labelsize=10)
plt.grid(linestyle=":")
plt.plot(t, r)
plt.show()