{"id":1123623,"date":"2025-01-08T19:34:05","date_gmt":"2025-01-08T11:34:05","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1123623.html"},"modified":"2025-01-08T19:34:08","modified_gmt":"2025-01-08T11:34:08","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a93d%e6%95%a3%e7%82%b9%e5%9b%be%e5%88%86%e9%a2%9c%e8%89%b2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1123623.html","title":{"rendered":"python\u5982\u4f55\u8ba93D\u6563\u70b9\u56fe\u5206\u989c\u8272"},"content":{"rendered":"

\"python\u5982\u4f55\u8ba93D\u6563\u70b9\u56fe\u5206\u989c\u8272\"<\/p>\n

Python\u5982\u4f55\u8ba93D\u6563\u70b9\u56fe\u5206\u989c\u8272<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u4f7f\u7528Matplotlib\u5e93\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa3D\u6563\u70b9\u56fe\uff0c\u5e76\u6839\u636e\u4e0d\u540c\u7c7b\u522b\u6216\u6570\u503c\u5206\u989c\u8272\u3002\u4f7f\u7528\u4e0d\u540c\u989c\u8272\u6765\u533a\u5206\u6570\u636e\u70b9\u3001\u901a\u8fc7\u989c\u8272\u6620\u5c04\u6765\u5c55\u793a\u6570\u636e\u7684\u6570\u503c\u7279\u5f81\u3001\u7ed3\u5408Matplotlib\u548cNumPy\u5b9e\u73b03D\u6563\u70b9\u56fe\u7684\u989c\u8272\u5206\u914d<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u6280\u672f\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u52363D\u6563\u70b9\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\uff0c\u5982Matplotlib\u548cNumPy\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
from mpl_toolkits.mplot3d import Axes3D<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u751f\u6210\u968f\u673a\u6570\u636e<\/h3>\n<\/p>\n
\u4e3a\u4e86\u5c55\u793a\u5982\u4f55\u5206\u989c\u8272\uff0c\u6211\u4eec\u9996\u5148\u751f\u6210\u4e00\u4e9b\u968f\u673a\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u968f\u673a\u6570\u636e<\/p>\n
np.random.seed(0)<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
colors = np.random.rand(100)  # \u968f\u673a\u751f\u6210\u989c\u8272\u6570\u636e<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u521b\u5efa3D\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528Matplotlib\u521b\u5efa\u4e00\u4e2a3D\u6563\u70b9\u56fe\uff0c\u5e76\u6839\u636e\u989c\u8272\u6570\u636e\u5bf9\u70b9\u8fdb\u884c\u7740\u8272\u3002<\/p>\n<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
\u4f7f\u7528scatter\u7ed8\u52363D\u6563\u70b9\u56fe<\/strong><\/h2>\n
sc = ax.scatter(x, y, z, c=colors, cmap='viridis')<\/p>\n
\u6dfb\u52a0\u989c\u8272\u6761<\/strong><\/h2>\n
plt.colorbar(sc)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u901a\u8fc7\u989c\u8272\u6620\u5c04\u5c55\u793a\u6570\u636e\u7279\u5f81<\/h3>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86cmap='viridis'<\/code>\u6765\u6307\u5b9a\u989c\u8272\u6620\u5c04\u3002\u989c\u8272\u6620\u5c04\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u7684\u6570\u503c\u7279\u5f81\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u7ed3\u5408\u5206\u7c7b\u6570\u636e\u5206\u989c\u8272<\/h3>\n<\/p>\n
\u5982\u679c\u6211\u4eec\u6709\u5206\u7c7b\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u6765\u8868\u793a\u4e0d\u540c\u7684\u7c7b\u522b\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u5206\u7c7b\u6570\u636e<\/p>\n
categories = np.random.randint(0, 5, size=100)<\/p>\n
\u521b\u5efa\u989c\u8272\u6620\u5c04<\/strong><\/h2>\n
cmap = plt.get_cmap('viridis', 5)<\/p>\n
\u4f7f\u7528\u5206\u7c7b\u6570\u636e\u7ed8\u52363D\u6563\u70b9\u56fe<\/strong><\/h2>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
sc = ax.scatter(x, y, z, c=categories, cmap=cmap)<\/p>\n
\u6dfb\u52a0\u989c\u8272\u6761\uff0c\u5e76\u8bbe\u7f6e\u6807\u7b7e<\/strong><\/h2>\n
cb = plt.colorbar(sc, ticks=range(5))<\/p>\n
cb.set_label('Category')<\/p>\n
cb.set_ticks([0, 1, 2, 3, 4])<\/p>\n
cb.set_ticklabels(['A', 'B', 'C', 'D', 'E'])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u7ec6\u8282\u8c03\u6574\u4e0e\u4f18\u5316<\/h3>\n<\/p>\n
\u5728\u7ed8\u52363D\u6563\u70b9\u56fe\u65f6\uff0c\u6709\u8bb8\u591a\u7ec6\u8282\u53ef\u4ee5\u8c03\u6574\u548c\u4f18\u5316\uff0c\u4f8b\u5982\u70b9\u7684\u5927\u5c0f\u3001\u900f\u660e\u5ea6\u3001\u8f74\u6807\u7b7e\u7b49\u3002<\/p>\n<\/p>\n
# \u8c03\u6574\u70b9\u7684\u5927\u5c0f\u548c\u900f\u660e\u5ea6<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
sc = ax.scatter(x, y, z, c=categories, cmap=cmap, s=100, alpha=0.6)<\/p>\n
\u8bbe\u7f6e\u8f74\u6807\u7b7e<\/strong><\/h2>\n
ax.set_xlabel('X Label')<\/p>\n
ax.set_ylabel('Y Label')<\/p>\n
ax.set_zlabel('Z Label')<\/p>\n
\u6dfb\u52a0\u989c\u8272\u6761<\/strong><\/h2>\n
cb = plt.colorbar(sc, ticks=range(5))<\/p>\n
cb.set_label('Category')<\/p>\n
cb.set_ticks([0, 1, 2, 3, 4])<\/p>\n
cb.set_ticklabels(['A', 'B', 'C', 'D', 'E'])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c3D\u6563\u70b9\u56fe\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\uff0c\u7279\u522b\u662f\u5728\u79d1\u5b66\u7814\u7a76\u3001\u5de5\u7a0b\u548c\u91d1\u878d\u7b49\u9886\u57df\u3002<\/p>\n<\/p>\n
1\u3001\u79d1\u5b66\u7814\u7a76\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5728\u79d1\u5b66\u7814\u7a76\u4e2d\uff0c3D\u6563\u70b9\u56fe\u53ef\u4ee5\u7528\u4e8e\u5c55\u793a\u5b9e\u9a8c\u6570\u636e\u7684\u5206\u5e03\u548c\u8d8b\u52bf\u3002\u4f8b\u5982\uff0c\u5728\u6c14\u8c61\u5b66\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u75283D\u6563\u70b9\u56fe\u5c55\u793a\u4e0d\u540c\u9ad8\u5ea6\u3001\u6e29\u5ea6\u548c\u6e7f\u5ea6\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u6c14\u8c61\u6570\u636e<\/p>\n
height = np.random.rand(100) * 1000  # \u9ad8\u5ea6\uff08\u7c73\uff09<\/p>\n
temperature = np.random.rand(100) * 40  # \u6e29\u5ea6\uff08\u6444\u6c0f\u5ea6\uff09<\/p>\n
humidity = np.random.rand(100) * 100  # \u6e7f\u5ea6\uff08%\uff09<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
sc = ax.scatter(height, temperature, humidity, c=temperature, cmap='coolwarm')<\/p>\n
ax.set_xlabel('Height (m)')<\/p>\n
ax.set_ylabel('Temperature (\u00b0C)')<\/p>\n
ax.set_zlabel('Humidity (%)')<\/p>\n
cb = plt.colorbar(sc)<\/p>\n
cb.set_label('Temperature (\u00b0C)')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5de5\u7a0b\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5728\u5de5\u7a0b\u9886\u57df\uff0c3D\u6563\u70b9\u56fe\u53ef\u4ee5\u7528\u4e8e\u5206\u6790\u6750\u6599\u7684\u529b\u5b66\u6027\u80fd\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u75283D\u6563\u70b9\u56fe\u5c55\u793a\u4e0d\u540c\u6750\u6599\u5728\u4e0d\u540c\u5e94\u529b\u548c\u5e94\u53d8\u6761\u4ef6\u4e0b\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u6750\u6599\u6570\u636e<\/p>\n
stress = np.random.rand(100) * 1000  # \u5e94\u529b\uff08MPa\uff09<\/p>\n
strAI<\/a>n = np.random.rand(100) * 0.1  # \u5e94\u53d8\uff08%\uff09<\/p>\n
hardness = np.random.rand(100) * 100  # \u786c\u5ea6\uff08HRB\uff09<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
sc = ax.scatter(stress, strain, hardness, c=hardness, cmap='plasma')<\/p>\n
ax.set_xlabel('Stress (MPa)')<\/p>\n
ax.set_ylabel('Strain (%)')<\/p>\n
ax.set_zlabel('Hardness (HRB)')<\/p>\n
cb = plt.colorbar(sc)<\/p>\n
cb.set_label('Hardness (HRB)')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u91d1\u878d\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n
\u5728\u91d1\u878d\u9886\u57df\uff0c3D\u6563\u70b9\u56fe\u53ef\u4ee5\u7528\u4e8e\u5206\u6790\u4e0d\u540c\u80a1\u7968\u7684\u4ef7\u683c\u3001\u4ea4\u6613\u91cf\u548c\u6ce2\u52a8\u7387\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u75283D\u6563\u70b9\u56fe\u5c55\u793a\u4e0d\u540c\u80a1\u7968\u5728\u4e0d\u540c\u65f6\u95f4\u70b9\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u91d1\u878d\u6570\u636e<\/p>\n
price = np.random.rand(100) * 1000  # \u4ef7\u683c\uff08\u7f8e\u5143\uff09<\/p>\n
volume = np.random.rand(100) * 1000000  # \u4ea4\u6613\u91cf\uff08\u80a1\uff09<\/p>\n
volatility = np.random.rand(100) * 50  # \u6ce2\u52a8\u7387\uff08%\uff09<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
sc = ax.scatter(price, volume, volatility, c=price, cmap='inferno')<\/p>\n
ax.set_xlabel('Price ($)')<\/p>\n
ax.set_ylabel('Volume (shares)')<\/p>\n
ax.set_zlabel('Volatility (%)')<\/p>\n
cb = plt.colorbar(sc)<\/p>\n
cb.set_label('Price ($)')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Matplotlib\u548cNumPy\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa3D\u6563\u70b9\u56fe\uff0c\u5e76\u6839\u636e\u4e0d\u540c\u7c7b\u522b\u6216\u6570\u503c\u5206\u989c\u8272\u3002\u4f7f\u7528\u4e0d\u540c\u989c\u8272\u6765\u533a\u5206\u6570\u636e\u70b9\u3001\u901a\u8fc7\u989c\u8272\u6620\u5c04\u6765\u5c55\u793a\u6570\u636e\u7684\u6570\u503c\u7279\u5f81\u3001\u7ed3\u5408Matplotlib\u548cNumPy\u5b9e\u73b03D\u6563\u70b9\u56fe\u7684\u989c\u8272\u5206\u914d<\/strong>\u3002\u8fd9\u4e9b\u6280\u672f\u4e0d\u4ec5\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\uff0c\u8fd8\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u53ef\u89c6\u5316\u7684\u6548\u679c\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c3D\u6563\u70b9\u56fe\u5e7f\u6cdb\u7528\u4e8e\u79d1\u5b66\u7814\u7a76\u3001\u5de5\u7a0b\u548c\u91d1\u878d\u7b49\u9886\u57df\uff0c\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u60a8\u80fd\u591f\u638c\u63e1\u5982\u4f55\u5728Python\u4e2d\u521b\u5efa3D\u6563\u70b9\u56fe\uff0c\u5e76\u6839\u636e\u4e0d\u540c\u7c7b\u522b\u6216\u6570\u503c\u5206\u989c\u8272\u3002\u5982\u679c\u60a8\u6709\u4efb\u4f55\u95ee\u9898\u6216\u5efa\u8bae\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4e3a3D\u6563\u70b9\u56fe\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u4f7f\u7528matplotlib<\/code>\u5e93\u53ef\u4ee5\u8f7b\u677e\u5730\u4e3a3D\u6563\u70b9\u56fe\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u4e3a\u6bcf\u4e2a\u70b9\u6307\u5b9a\u989c\u8272\u53c2\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002\u901a\u5e38\uff0c\u53ef\u4ee5\u4f7f\u7528c<\/code>\u53c2\u6570\u4f20\u9012\u4e00\u4e2a\u989c\u8272\u6570\u7ec4\uff0c\u989c\u8272\u53ef\u4ee5\u57fa\u4e8e\u70b9\u7684\u67d0\u4e9b\u5c5e\u6027\uff08\u5982\u5927\u5c0f\u3001\u7c7b\u522b\u7b49\uff09\u3002\u5982\u679c\u60a8\u4f7f\u7528\u7684\u662fscatter<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9009\u62e9cmap<\/code>\u6765\u8bbe\u7f6e\u989c\u8272\u6620\u5c04\uff0c\u5e2e\u52a9\u533a\u5206\u4e0d\u540c\u7684\u6570\u636e\u70b9\u3002<\/p>\n
\u57283D\u6563\u70b9\u56fe\u4e2d\u5982\u4f55\u6839\u636e\u6570\u636e\u503c\u52a8\u6001\u8c03\u6574\u989c\u8272\uff1f<\/strong>
\u901a\u8fc7\u4f7f\u7528matplotlib<\/code>\u7684scatter<\/code>\u51fd\u6570\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u6570\u636e\u70b9\u7684\u503c\u6765\u52a8\u6001\u8c03\u6574\u989c\u8272\u3002\u53ef\u4ee5\u4f7f\u7528Normalize<\/code>\u7c7b\u6765\u6807\u51c6\u5316\u6570\u636e\u8303\u56f4\uff0c\u5e76\u7ed3\u5408cmap<\/code>\uff08\u989c\u8272\u6620\u5c04\uff09\u6765\u5f97\u5230\u5bf9\u5e94\u7684\u989c\u8272\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u5c06\u6570\u636e\u503c\u4f20\u9012\u7ed9c<\/code>\u53c2\u6570\uff0c\u5e76\u5e94\u7528\u4e00\u4e2a\u5408\u9002\u7684\u989c\u8272\u6620\u5c04\uff0c\u5982plt.cm.viridis<\/code>\uff0c\u4ece\u800c\u5b9e\u73b0\u6570\u636e\u503c\u4e0e\u989c\u8272\u7684\u5bf9\u5e94\u5173\u7cfb\u3002<\/p>\n
\u662f\u5426\u53ef\u4ee5\u5728Python\u76843D\u6563\u70b9\u56fe\u4e2d\u6dfb\u52a0\u989c\u8272\u6761\uff1f<\/strong>
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