折线图
#折线图，曲线图
import numpy as np
import matplotlib.pyplot as pltx = np.linspace(-10, 10, 1000)
# y = np.sin(x)
#y = 2 * x * x * x + 3* x * x + 2*x +5
y = np.sin(2*x)+2*np.cos(1/x)
plt.figure()
plt.plot(x, y)
plt.show()
1234567891011
import matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['KaiTi'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.figure()fig, (axes1, axes2) = plt.subplots(nrows=1, ncols=2)
axes1.plot([1, 2, 3, 4, 5, 6], color="r", linestyle="-.", label="红线")
axes1.set_title("图1")
# 设置当前x轴以及y轴的label
axes1.set_xlabel(xlabel="x轴")
axes1.set_ylabel(ylabel="y轴")
# 设置显示昂前的legend
axes1.legend()
# 设置当前的网格
axes1.grid(True, linestyle='-.', alpha=0.5)axes2.plot([2, 4, 6, 8], color="y", linestyle="--", label="黄线")
axes2.set_title("图2")
# 设置当前第二张图的刻度
axes2.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
axes2.set_yticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
axes2.legend()
# 开启刻度
axes2.minorticks_on()
plt.show()
1234567891011121314151617181920212223242526
直方图
# 直方图与当前的柱状图不一样，一个是有间隔并且x轴的是一个准确的值，直方图的值为区间
import matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['KaiTi'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号plt.figure()
x_date = [22, 87, 5, 43, 56, 73, 55, 54, 11, 20, 51, 5, 79, 31, 27]
bins = [0, 20, 40, 60, 80, 100]
plt.hist(x_date, bins, density=True)
# plt.yticks(range(min(x_date), max(x_date), 10))
plt.xticks(range(min(bins), max(bins) + 5, 5))
plt.show()
12345678910111213
柱状图
import matplotlib.pyplot as plt
import randomplt.rcParams['font.sans-serif'] = ['KaiTi'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.figure()
month_days = 31
x = [i for i in range(1, month_days)]
y = [random.randrange(20, 41) for i in range(1, month_days)]
plt.bar(x, y, color="r", label="金额")
plt.minorticks_on()
plt.title("一个月的消费情况")
plt.xlabel("一个月(天)")
plt.ylabel("消费金额(元)")
plt.legend()
# plt.xticks(x)
# plt.yticks([i for i in range(0, 41)])
plt.show()
123456789101112131415161718
import matplotlib.pyplot as plt
import randomplt.rcParams['font.sans-serif'] = ['KaiTi'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.figure()
month_days = 5
x = [i + 1 for i in range(1, month_days + 1)]
y = [random.randrange(20, 41) for i in range(1, month_days + 1)]
plt.bar(x, y, label="金额", color=["r", "y", "b", "g", "c"])
# plt.minorticks_on()
plt.title("一周的消费情况")
plt.xlabel("一周(天)")
plt.ylabel("消费金额(元)")
plt.legend()
plt.xticks(x, ["星期{0}".format(i + 1) for i in range(0, month_days + 1)])
plt.grid( linestyle="-.", alpha=0.5)
plt.show()
123456789101112131415161718
import matplotlib.pyplot as plt
import randomplt.rcParams['font.sans-serif'] = ['KaiTi'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.figure()
month_days = 5
x = [i for i in range(1, month_days + 1)]
y = [random.randrange(20, 41) for i in range(1, month_days + 1)]
plt.bar(x, y, label="本周", width=0.3, color="r")
plt.bar([i + 0.2 for i in x], [i + random.randrange(1, 10) for i in y], label="上周", width=0.3, color="g")
# plt.minorticks_on()
plt.title("本周和上周的消费情况")
plt.xlabel("同比")
plt.ylabel("消费金额(元)")
plt.legend()
plt.xticks([i + 0.05 for i in x], ["星期{0}".format(i + 1) for i in range(0, month_days + 1)])
plt.grid(linestyle="-.", alpha=0.5)
plt.show()
12345678910111213141516171819
# -*- coding: utf-8 -*-
import time
import matplotlib.pyplot as pltdef showResult(xList, yList, title, xLabel, yLabel):
plt.bar(xList, yList, width=0.5,color="b")
plt.title(title)
plt.xlabel(xLabel)
plt.ylabel(yLabel)
plt.xticks(xList)
for x, y in zip(xList, yList):
plt.text(x, y+0.3, str(y), ha='center', va='bottom', fontsize=7.5)
plt.savefig('fig'+str(int(time.time()))+'.jpg')
plt.show()x_arr = [1, 2, 3, 4, 5, 6,7,8,9,10]
y_arr = [95.88897746,92.99818646,84.0831402,87.035405,90.48605803,83.32441838,82.4323623,94.95956628,86.27340434,90.85186676]
showResult(x_arr, y_arr, 'Changan Soh Estimation', 'vehicle number', 'SOH')
123456789101112131415161718
饼图
import matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['KaiTi'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号plt.figure()
dataList = [10, 12, 15, 18, 9, 25]
dataLabel = ["水", "零食", "水果", "生活用品", "外卖", "电影"]plt.pie(dataList, labels=dataLabel, colors=["r", "y", "g", "b", "c", "m"], autopct="%1.2f%%")
plt.legend()
plt.title("一天的消费情况")
# 将当前的图形变成圆形，默认为椭圆
plt.axis("equal")plt.show()
12345678910111213141516
散点图
import matplotlib.pyplot as plt
import randomplt.rcParams['font.sans-serif'] = ['KaiTi'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False
plt.figure()
month_days = 31
x = [i for i in range(1, month_days)]
y = [random.randrange(20, 40) for i in range(1, month_days)]
plt.scatter(x, y)
plt.xlabel("一个月")
plt.ylabel("消费金额")
plt.title("一个月的中餐的消费情况")
plt.show()
1234567891011121314
热力图
import numpy as np
import matplotlib.pyplot as pltx = np.random.rand(100).reshape(10,10)
plt.imshow(x, cmap=plt.cm.hot, vmin=0, vmax=1)
plt.colorbar()
plt.show()
1234567
三维的线图和散点图
最基本的三维图是由(x, y, z)三维坐标点构成的线图与散点图，可以用ax.plot3D和ax.scatter3D函数来创建，默认情况下，散点会自动改变透明度，以在平面上呈现出立体感
#绘制三角螺旋线
from mpl_toolkits import mplot3d
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as npax = plt.axes(projection='3d')
#三维线的数据
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline, zline, 'gray')# 三维散点的数据
zdata = 15 * np.random.random(100)
xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens')
12345678910111213141516171819
三维等高线图
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6,6,30)
y = np.linspace(-6,6,30)
X, Y = np.meshgrid(x, y)
Z = f(X,Y)fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(X, Y, Z, 50, cmap='binary')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
#调整观察角度和方位角。这里将俯仰角设为60度，把方位角调整为35度
ax.view_init(60, 35)
123456789101112131415
线框图和全面图
全面图和线框图相似，只不过线框图的每一个面都是由多边形构成。只要增加唉一个配色方案来填充这些多边形，就可以感受到可视化图形表面的拓扑结构了。
#线框图
fig =plt.figure()
ax = plt.axes(projection='3d')
ax.plot_wireframe(X, Y, Z, color='c')
ax.set_title('wireframe')
12345
曲面图
#曲面图
ax = plt.axes(projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='viridis', edgecolor='none')
ax.set_title('surface')
1234
三维切片
#使用极坐标可以获得切片的效果
r = np.linspace(0, 6, 20)
theta = np.linspace(-0.9 * np.pi, 0.8 * np.pi, 40)
r, theta = np.meshgrid(r, theta)
X = r * np.sin(theta)
Y = r * np.cos(theta)
Z = f(X, Y)
ax = plt.axes(projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='viridis', edgecolor='none')
123456789
曲面三角剖分
在某些应用场景下，上述这些要求均匀采样的网格数据显得太过严格且不太容易实现。这时就可以使用三角剖分部分图形。
theta = 2 * np.pi * np.random.random(1000)
r = 6 * np.random.random(1000)
x = np.ravel(r * np.sin(theta))
y = np.ravel(r * np.cos(theta))
z = f(x, y)ax = plt.axes(projection='3d')
ax.scatter(x, y, z, c=z, cmap='viridis', linewidth=0.5)
12345678
上图还有许多地方需要修补，这些工作可以由ax.plot_trisurf函数帮助我们完成。它首先找到一组所有点都连接起来的三角形，然后用这些三角形创建曲面
ax = plt.axes(projection='3d')
ax.plot_trisurf(x, y, z, cmap='viridis', edgecolor='none')
12
莫比乌斯带（应用曲面三角剖分）
#绘制莫比乌斯带
#由于它是一条二维带，因此需要两个内在维度。theta维度取值范围是0～2pi，宽度维度w取值范围是-1～1
theta = np.linspace(0, 2 * np.pi, 30)
w = np.linspace(-0.25, 0.25, 8)
w, theta = np.meshgrid(w, theta)
phi = 0.5 * theta
#x-y平面内的半径
r = 1 + w * np.cos(phi)x = np.ravel(r * np.cos(theta))
y = np.ravel(r * np.sin(theta))
z = np.ravel(w * np.sin(phi))#要画出莫比乌斯带，还必须保证三角部分是正确的。最好的方法是首先用基本参数化方法定义三角部分，然后用Matplotlib将
#这个三角剖分映射到莫比乌斯带的三维空间里
from matplotlib.tri import Triangulation
tri = Triangulation(np.ravel(w), np.ravel(theta))
ax = plt.axes(projection='3d')
ax.plot_trisurf(x, y, z, triangles=tri.triangles, cmap='viridis', linewidth=0.2)
ax.set_xlim(-1, 1);ax.set_ylim(-1,1);ax.set_zlim(-1,1)
1234567891011121314151617181920
动态条形图
import matplotlib.pyplot as plt
import timedef insert_sort(lst):
lsts = []
for i in range(len(lst)):
temp = lst[i]
j = i-1
while j>=0 and lst[j]>temp:
lst[j+1] = lst[j]
j -= 1
lst[j+1] = temp
l = lst[:]
lsts.append(l)
return lsts if __name__ == "__main__":
lst = [13,32,42,1,53,4,66,2,5,7,74,23]
lsts = insert_sort(lst)
plt.ion()#打开交互模式
fig = plt.figure()#新建绘图窗口
ax = plt.gca()#获取当前子图
bars = ax.bar(range(len(lst)),height=lst)#绘制条形图
for l in lsts:
print(l)
bars.remove()#删除条形图
bars = ax.bar(range(len(lst)),height=l)#绘制条形图
plt.pause(0.5)
while True:#防止图片关闭
plt.pause(1)本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
