{"id":1069414,"date":"2025-01-08T10:48:39","date_gmt":"2025-01-08T02:48:39","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1069414.html"},"modified":"2025-01-08T10:48:41","modified_gmt":"2025-01-08T02:48:41","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e7%94%9f%e6%88%90%e4%b8%89%e7%bb%b4%e5%9d%90%e6%a0%87-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1069414.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u751f\u6210\u4e09\u7ef4\u5750\u6807"},"content":{"rendered":"

\"\u5982\u4f55\u5229\u7528python\u751f\u6210\u4e09\u7ef4\u5750\u6807\"<\/p>\n

\u5982\u4f55\u5229\u7528Python\u751f\u6210\u4e09\u7ef4\u5750\u6807<\/strong><\/p>\n<\/p>\n

\u8981\u751f\u6210\u4e09\u7ef4\u5750\u6807\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u4e2a\u5e93\uff0c\u5982NumPy\u3001Matplotlib\u3001Pandas\u7b49\u3002\u4f7f\u7528NumPy\u521b\u5efa\u5750\u6807\u6570\u636e\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u3001\u4f7f\u7528Pandas\u5904\u7406\u548c\u5206\u6790\u4e09\u7ef4\u6570\u636e<\/strong>\u3002\u8fd9\u4e9b\u5e93\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u751f\u6210\u3001\u53ef\u89c6\u5316\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u751f\u6210\u5e76\u64cd\u4f5c\u4e09\u7ef4\u5750\u6807\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NumPy\u521b\u5efa\u4e09\u7ef4\u5750\u6807\u6570\u636e<\/h3>\n<\/p>\n

NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u9002\u7528\u4e8e\u751f\u6210\u548c\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528NumPy\u751f\u6210\u4e09\u7ef4\u5750\u6807\u6570\u636e\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u4e09\u7ef4\u5750\u6807\u6570\u636e<\/strong><\/h2>\n
num_points = 1000<\/p>\n
x = np.random.uniform(-10, 10, num_points)<\/p>\n
y = np.random.uniform(-10, 10, num_points)<\/p>\n
z = np.random.uniform(-10, 10, num_points)<\/p>\n
\u5c06\u4e09\u7ef4\u5750\u6807\u6570\u636e\u6253\u5305\u6210\u4e00\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
coordinates = np.array([x, y, z]).T<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528np.random.uniform<\/code>\u751f\u6210\u4e861000\u4e2a\u5728-10\u523010\u4e4b\u95f4\u5747\u5300\u5206\u5e03\u7684\u968f\u673a\u70b9\uff0c\u5e76\u5c06\u8fd9\u4e9b\u70b9\u6253\u5305\u6210\u4e00\u4e2a\u4e09\u7ef4\u5750\u6807\u6570\u7ec4\u3002\u4f7f\u7528NumPy\u751f\u6210\u968f\u673a\u4e09\u7ef4\u5750\u6807\u6570\u636e\u5341\u5206\u9ad8\u6548\u4e14\u7b80\u4fbf<\/strong>\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62<\/h3>\n<\/p>\n
Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u4e09\u7ef4\u56fe\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528Matplotlib\u4e2d\u7684mpl_toolkits.mplot3d<\/code>\u6a21\u5757\u6765\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
from mpl_toolkits.mplot3d import Axes3D<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u5f62\u5bf9\u8c61<\/strong><\/h2>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
\u7ed8\u5236\u4e09\u7ef4\u6563\u70b9\u56fe<\/strong><\/h2>\n
ax.scatter(coordinates[:, 0], coordinates[:, 1], coordinates[:, 2], c='b', marker='o')<\/p>\n
ax.set_xlabel('X Label')<\/p>\n
ax.set_ylabel('Y Label')<\/p>\n
ax.set_zlabel('Z Label')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u65b0\u7684\u56fe\u5f62\u5bf9\u8c61\uff0c\u7136\u540e\u5728\u56fe\u5f62\u5bf9\u8c61\u4e2d\u6dfb\u52a0\u4e86\u4e00\u4e2a\u4e09\u7ef4\u5750\u6807\u7cfb\u3002\u63a5\u7740\uff0c\u6211\u4eec\u4f7f\u7528ax.scatter<\/code>\u65b9\u6cd5\u7ed8\u5236\u4e86\u4e00\u4e2a\u4e09\u7ef4\u6563\u70b9\u56fe\u3002\u6700\u540e\uff0c\u6211\u4eec\u8bbe\u7f6e\u4e86\u5750\u6807\u8f74\u7684\u6807\u7b7e\u5e76\u663e\u793a\u4e86\u56fe\u5f62\u3002\u4f7f\u7528Matplotlib\u53ef\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u4e09\u7ef4\u5750\u6807\u6570\u636e\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u548c\u7ed3\u6784<\/strong>\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u5904\u7406\u548c\u5206\u6790\u4e09\u7ef4\u6570\u636e<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528Pandas\u6765\u5904\u7406\u548c\u5206\u6790\u4e09\u7ef4\u5750\u6807\u6570\u636e\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c06NumPy\u751f\u6210\u7684\u4e09\u7ef4\u5750\u6807\u6570\u636e\u8f6c\u6362\u4e3aPandas\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efaPandas\u6570\u636e\u6846<\/strong><\/h2>\n
df = pd.DataFrame(coordinates, columns=['X', 'Y', 'Z'])<\/p>\n
\u663e\u793a\u6570\u636e\u6846\u7684\u524d\u4e94\u884c<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5c06\u4e09\u7ef4\u5750\u6807\u6570\u636e\u8f6c\u6362\u4e3a\u4e86Pandas\u6570\u636e\u6846\uff0c\u5e76\u663e\u793a\u4e86\u6570\u636e\u6846\u7684\u524d\u4e94\u884c\u3002\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5229\u7528Pandas\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\u5bf9\u4e09\u7ef4\u5750\u6807\u6570\u636e\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n
\u7edf\u8ba1\u5206\u6790\u4e09\u7ef4\u5750\u6807\u6570\u636e<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u5229\u7528Pandas\u5bf9\u4e09\u7ef4\u5750\u6807\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u4ee5\u4e86\u89e3\u6570\u636e\u7684\u57fa\u672c\u7279\u5f81\u3002<\/p>\n<\/p>\n
# \u7edf\u8ba1\u63cf\u8ff0<\/p>\n
print(df.describe())<\/p>\n
\u8ba1\u7b97\u6bcf\u4e2a\u5750\u6807\u8f74\u7684\u5747\u503c<\/strong><\/h2>\n
mean_x = df['X'].mean()<\/p>\n
mean_y = df['Y'].mean()<\/p>\n
mean_z = df['Z'].mean()<\/p>\n
print(f"Mean of X: {mean_x}, Mean of Y: {mean_y}, Mean of Z: {mean_z}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528df.describe()<\/code>\u65b9\u6cd5\u751f\u6210\u4e86\u6570\u636e\u7684\u7edf\u8ba1\u63cf\u8ff0\uff0c\u5305\u62ec\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u7b49\u3002\u63a5\u7740\uff0c\u6211\u4eec\u5206\u522b\u8ba1\u7b97\u4e86\u6bcf\u4e2a\u5750\u6807\u8f74\u7684\u5747\u503c\u3002\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u83b7\u5f97\u4e09\u7ef4\u5750\u6807\u6570\u636e\u7684\u57fa\u672c\u7279\u5f81\uff0c\u8fdb\u4e00\u6b65\u6307\u5bfc\u6570\u636e\u7684\u5904\u7406\u548c\u5e94\u7528<\/strong>\u3002<\/p>\n<\/p>\n
\u6570\u636e\u8fc7\u6ee4\u548c\u7b5b\u9009<\/h4>\n<\/p>\n
Pandas\u8fd8\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u8fc7\u6ee4\u548c\u7b5b\u9009\u529f\u80fd\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4ece\u4e09\u7ef4\u5750\u6807\u6570\u636e\u4e2d\u7b5b\u9009\u51fa\u7279\u5b9a\u6761\u4ef6\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u7b5b\u9009\u51fa\u6240\u6709X\u5750\u6807\u5927\u4e8e0\u7684\u70b9<\/p>\n
filtered_data = df[df['X'] > 0]<\/p>\n
print(filtered_data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u7b5b\u9009\u51fa\u4e86\u6240\u6709X\u5750\u6807\u5927\u4e8e0\u7684\u70b9\uff0c\u5e76\u663e\u793a\u4e86\u7b5b\u9009\u7ed3\u679c\u7684\u524d\u4e94\u884c\u3002\u6570\u636e\u8fc7\u6ee4\u548c\u7b5b\u9009\u529f\u80fd\u4f7f\u5f97\u6211\u4eec\u80fd\u591f\u6839\u636e\u7279\u5b9a\u6761\u4ef6\u5bf9\u6570\u636e\u8fdb\u884c\u7cbe\u7ec6\u5316\u5904\u7406\u548c\u5206\u6790<\/strong>\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
\u751f\u6210\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u6570\u636e\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u573a\u666f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
1. \u4e09\u7ef4\u5efa\u6a21<\/h4>\n<\/p>\n
\u5728\u8ba1\u7b97\u673a\u56fe\u5f62\u5b66\u4e2d\uff0c\u4e09\u7ef4\u5efa\u6a21\u662f\u751f\u6210\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u6570\u636e\u7684\u4e00\u4e2a\u91cd\u8981\u5e94\u7528\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e09\u7ef4\u5750\u6807\u6570\u636e\u6765\u8868\u793a\u4e09\u7ef4\u7269\u4f53\u7684\u5f62\u72b6\u548c\u7ed3\u6784\uff0c\u8fdb\u800c\u751f\u6210\u4e09\u7ef4\u6a21\u578b\u3002<\/p>\n<\/p>\n
2. \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u751f\u6210\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u6570\u636e\u7684\u53e6\u4e00\u4e2a\u91cd\u8981\u5e94\u7528\u3002\u901a\u8fc7\u4e09\u7ef4\u56fe\u5f62\u5c55\u793a\u6570\u636e\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u548c\u7ed3\u6784\uff0c\u8fdb\u800c\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u89c4\u5f8b\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
3. \u7a7a\u95f4\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\u5728\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09\u548c\u9065\u611f\u9886\u57df\uff0c\u4e09\u7ef4\u5750\u6807\u6570\u636e\u7528\u4e8e\u8868\u793a\u5730\u7406\u7a7a\u95f4\u6570\u636e\u3002\u901a\u8fc7\u5bf9\u4e09\u7ef4\u5750\u6807\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\uff0c\u53ef\u4ee5\u5b9e\u73b0\u7a7a\u95f4\u6570\u636e\u7684\u53ef\u89c6\u5316\u3001\u5efa\u6a21\u548c\u5206\u6790\uff0c\u8fdb\u800c\u652f\u6301\u7a7a\u95f4\u51b3\u7b56\u3002<\/p>\n<\/p>\n
4. \u79d1\u5b66\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5728\u79d1\u5b66\u8ba1\u7b97\u4e2d\uff0c\u751f\u6210\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u6570\u636e\u662f\u6a21\u62df\u548c\u5206\u6790\u7269\u7406\u73b0\u8c61\u7684\u91cd\u8981\u624b\u6bb5\u3002\u4f8b\u5982\uff0c\u5728\u6d41\u4f53\u529b\u5b66\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e09\u7ef4\u5750\u6807\u6570\u636e\u8868\u793a\u6d41\u573a\u4e2d\u7684\u901f\u5ea6\u548c\u538b\u529b\u5206\u5e03\uff0c\u8fdb\u800c\u8fdb\u884c\u6d41\u4f53\u6a21\u62df\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5229\u7528Python\u751f\u6210\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u6570\u636e\u3002\u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u751f\u6210\u4e86\u4e09\u7ef4\u5750\u6807\u6570\u636e\uff0c\u5e76\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e86\u4e09\u7ef4\u56fe\u5f62\u3002\u63a5\u7740\uff0c\u6211\u4eec\u4f7f\u7528Pandas\u5904\u7406\u548c\u5206\u6790\u4e86\u4e09\u7ef4\u5750\u6807\u6570\u636e\uff0c\u5c55\u793a\u4e86\u7edf\u8ba1\u5206\u6790\u548c\u6570\u636e\u8fc7\u6ee4\u7684\u529f\u80fd\u3002\u6700\u540e\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u751f\u6210\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u6570\u636e\u7684\u5e94\u7528\u573a\u666f\uff0c\u5305\u62ec\u4e09\u7ef4\u5efa\u6a21\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u7a7a\u95f4\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u5b66\u4e60\uff0c\u6211\u4eec\u53ef\u4ee5\u638c\u63e1\u5229\u7528Python\u751f\u6210\u3001\u53ef\u89c6\u5316\u548c\u5904\u7406\u4e09\u7ef4\u5750\u6807\u6570\u636e\u7684\u57fa\u672c\u65b9\u6cd5\u548c\u6280\u5de7\uff0c\u8fdb\u800c\u5e94\u7528\u4e8e\u5b9e\u9645\u95ee\u9898\u7684\u89e3\u51b3<\/strong>\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u5e93\u751f\u6210\u4e09\u7ef4\u5750\u6807\uff1f<\/strong>
Python\u63d0\u4f9b\u4e86\u591a\u4e2a\u5e93\u6765\u751f\u6210\u548c\u53ef\u89c6\u5316\u4e09\u7ef4\u5750\u6807\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecNumPy\u3001Matplotlib\u548cMayavi\u3002\u4f7f\u7528NumPy\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u4e09\u7ef4\u5750\u6807\u6570\u7ec4\uff0c\u800cMatplotlib\u5219\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a  <\/p>\n
import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\n\n# \u751f\u6210\u4e09\u7ef4\u5750\u6807\nx = np.random.rand(100)\ny = np.random.rand(100)\nz = np.random.rand(100)\n\nax.scatter(x, y, z)\nplt.show()\n<\/code><\/pre>\n
\u5982\u4f55\u5728Python\u4e2d\u81ea\u5b9a\u4e49\u4e09\u7ef4\u5750\u6807\u7684\u8303\u56f4\u548c\u5206\u5e03\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5750\u6807\u6570\u7ec4\u7684\u751f\u6210\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u4e09\u7ef4\u5750\u6807\u7684\u8303\u56f4\u548c\u5206\u5e03\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528numpy.linspace<\/code>\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u5750\u6807\u70b9\uff0c\u6216\u4f7f\u7528numpy.random.normal<\/code>\u751f\u6210\u6b63\u6001\u5206\u5e03\u7684\u5750\u6807\u3002\u901a\u8fc7\u8c03\u6574\u53c2\u6570\uff0c\u7528\u6237\u53ef\u4ee5\u5b9e\u73b0\u4e0d\u540c\u7684\u5750\u6807\u5206\u5e03\u6548\u679c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a  <\/p>\n
x = np.linspace(-5, 5, 100)\ny = np.linspace(-5, 5, 100)\nz = np.linspace(-5, 5, 100)\n<\/code><\/pre>\n
\u600e\u6837\u5c06\u751f\u6210\u7684\u4e09\u7ef4\u5750\u6807\u6570\u636e\u4fdd\u5b58\u4e3a\u6587\u4ef6\uff1f<\/strong>
\u7528\u6237\u53ef\u4ee5\u5c06\u751f\u6210\u7684\u4e09\u7ef4\u5750\u6807\u6570\u636e\u4fdd\u5b58\u4e3aCSV\u6216TXT\u6587\u4ef6\uff0c\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002\u4ee5\u4e0b\u662f\u4fdd\u5b58\u4e09\u7ef4\u5750\u6807\u5230CSV\u6587\u4ef6\u7684\u793a\u4f8b\uff1a  <\/p>\n
import pandas as pd\n\ndata = {'x': x, 'y': y, 'z': z}\ndf = pd.DataFrame(data)\ndf.to_csv('3d_coordinates.csv', index=False)\n<\/code><\/pre>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ba1\u7406\u548c\u5171\u4eab\u4e09\u7ef4\u5750\u6807\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u5229\u7528Python\u751f\u6210\u4e09\u7ef4\u5750\u6807 \u8981\u751f\u6210\u4e09\u7ef4\u5750\u6807\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u4e2a\u5e93\uff0c\u5982NumPy\u3001Matplotl […]","protected":false},"author":3,"featured_media":1069419,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1069414"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1069414"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1069414\/revisions"}],"predecessor-version":[{"id":1069421,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1069414\/revisions\/1069421"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1069419"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1069414"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1069414"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1069414"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}