python 3d大数据可视化_其它绘图样式和 3D 图表

$ sudo pip3 install notebook

$ ipython3 notebook

散点图

import numpy as np

import matplotlib as mpl

import matplotlib.pyplot as plt

%matplotlib inline

y = np.random.standard_normal((1000, 2))

plt.figure(figsize=(7, 5))

plt.plot(y[:, 0], y[:, 1], 'ro')

plt.grid(True)

plt.xlabel('1st')

plt.ylabel('2nd')

plt.title('Scatter Plot')

fig = plt.figure(figsize=(8, 5))

ax = fig.add_subplot(111, projection='3d')

ax.view_init(30, 60)

ax.scatter(strike, ttm, iv, zdir='z', s=25, c='b', marker='^')

ax.set_xlabel('strike')

ax.set_ylabel('time-to-maturity')

ax.set_zlabel('implied volatility')

c = np.random.randint(0, 10, len(y))

fig = plt.figure(figsize=(8, 5))

ax = fig.add_subplot(111, projection='3d')

ax.view_init(30, 60)

ax.scatter(strike, ttm, iv, zdir='z', s=25, c='b', marker='^')

ax.set_xlabel('strike')

ax.set_ylabel('time-to-maturity')

ax.set_zlabel('implied volatility')

直方图

plt.figure(figsize=(7, 4))

plt.hist(y, label=['1st', '2nd'], bins=25)

plt.grid(True)

plt.legend(loc=0)

plt.xlabel('value')

plt.ylabel('frequency')

plt.title('Histogram')

y = np.random.standard_normal((1000, 2))

plt.figure(figsize=(7, 4))

plt.hist(y, label=['1st', '2nd'], color=['b', 'g'], stacked=True, bins=20)

plt.grid(True)

plt.legend(loc=0)

plt.xlabel('value')

plt.ylabel('frequency')

plt.title('Histogram')

箱形图

fig, ax = plt.subplots(figsize=(7,4))

plt.boxplot(y)

plt.grid(True)

plt.setp(ax, xticklabels=['1st', '2nd'])

plt.xlabel('data set')

plt.ylabel('value')

plt.title('Boxplot')

数学示例

from matplotlib.patches import Polygon

def func(x):

return 0.5 * np.exp(x) + 1

a, b = 0.5, 1.5

x = np.linspace(0, 2)

y = func(x)

fig, ax = plt.subplots(figsize=(7, 5))

plt.plot(x, y, 'b', linewidth=2)

plt.ylim(ymin=0)

Ix = np.linspace(a, b)

Iy = func(Ix)

verts = [(a, 0)] + list(zip(Ix, Iy)) + [(b, 0)]

poly = Polygon(verts, facecolor='0.7', edgecolor='0.5')

ax.add_patch(poly)

plt.text(0.5 * (a + b), 1, r"$\int_a^b fx\mathrm{d}x$", horizontalalignment='center', fontsize=20)

plt.figtext(0.9, 0.075, '$x$')

plt.figtext(0.075, 0.9, '$f(x)$')

ax.set_xticks((a, b))

ax.set_xticklabels(('$a$', '$b$'))

ax.set_yticks([func(a), func(b)])

ax.set_yticklabels(('$f(a)$', '$f(b)$'))

plt.grid(True)

3D 绘图

strike = np.linspace(50, 150, 24)

ttm = np.linspace(0.5, 2.5, 24)

strike, ttm = np.meshgrid(strike, ttm)

iv = (strike - 100) ** 2 / (100 * strike) / ttm

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(9,6))

ax = fig.gca(projection='3d')

surf = ax.plot_surface(strike, ttm, iv, rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0.5, antialiased=True)

ax.set_xlabel('strike')

ax.set_ylabel('time-to-maturity')

ax.set_zlabel('implied volatility')

fig.colorbar(surf, shrink=0.5, aspect=5)

fig = plt.figure(figsize=(8, 5))

ax = fig.add_subplot(111, projection='3d')

ax.view_init(30, 60)

ax.scatter(strike, ttm, iv, zdir='z', s=25, c='b', marker='^')

ax.set_xlabel('strike')

ax.set_ylabel('time-to-maturity')

ax.set_zlabel('implied volatility')