{"id":1102855,"date":"2025-01-08T16:04:20","date_gmt":"2025-01-08T08:04:20","guid":{"rendered":""},"modified":"2025-01-08T16:04:24","modified_gmt":"2025-01-08T08:04:24","slug":"python%e5%a6%82%e4%bd%95%e7%94%bb%e4%b8%89%e7%bb%b4%e6%9b%b2%e7%ba%bf%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1102855.html","title":{"rendered":"python\u5982\u4f55\u753b\u4e09\u7ef4\u66f2\u7ebf\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u753b\u4e09\u7ef4\u66f2\u7ebf\u56fe\"<\/p>\n

\u5728Python\u4e2d\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Plotly\u3001Mayavi\u7b49\u3002<\/strong> \u5176\u4e2d\uff0cMatplotlib<\/strong> \u662f\u6700\u6d41\u884c\u7684\u5e93\u4e4b\u4e00\uff0c\u9002\u5408\u521d\u5b66\u8005\u4f7f\u7528<\/strong>\u3002 Plotly<\/strong> \u63d0\u4f9b\u4e86\u66f4\u4e3a\u73b0\u4ee3\u548c\u4ea4\u4e92\u7684\u56fe\u5f62\u9009\u9879\uff0c\u9002\u5408\u9700\u8981\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u7528\u6237<\/strong>\u3002 Mayavi<\/strong> \u5219\u9002\u7528\u4e8e\u9700\u8981\u9ad8\u6027\u80fd3D\u56fe\u5f62\u7684\u7528\u6237\uff0c\u9002\u5408\u79d1\u5b66\u8ba1\u7b97\u548c\u53ef\u89c6\u5316<\/strong>\u3002 \u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528 Matplotlib<\/strong> \u5e93\u6765\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u548c\u5bfc\u5165\u6240\u9700\u5e93<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3002\u9996\u5148\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86 Matplotlib \u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u5728\u4f60\u7684 Python \u811a\u672c\u6216 Jupyter Notebook \u4e2d\u5bfc\u5165\u6240\u9700\u7684\u5e93\uff1a<\/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\u521b\u5efa\u6570\u636e<\/h3>\n<\/p>\n
\u5728\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u751f\u6210\u6216\u83b7\u53d6\u8981\u7ed8\u5236\u7684\u6570\u636e\u3002\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528 NumPy \u5e93\u6765\u751f\u6210\u4e00\u4e9b\u793a\u4f8b\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u8981\u7ed8\u5236\u4e00\u4e2a\u87ba\u65cb\u7ebf\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u751f\u6210\u6570\u636e\u70b9\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e<\/p>\n
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)<\/p>\n
z = np.linspace(-2, 2, 100)<\/p>\n
r = z2 + 1<\/p>\n
x = r * np.sin(theta)<\/p>\n
y = r * np.cos(theta)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u521b\u5efa\u4e09\u7ef4\u56fe\u5f62\u5bf9\u8c61<\/h3>\n<\/p>\n
\u5728 Matplotlib \u4e2d\uff0c\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u9700\u8981\u4f7f\u7528 Axes3D<\/code> \u5bf9\u8c61\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u56fe\u5f62\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe<\/h3>\n<\/p>\n
\u4f7f\u7528 plot<\/code> \u65b9\u6cd5\u53ef\u4ee5\u5728\u4e09\u7ef4\u5750\u6807\u7cfb\u4e2d\u7ed8\u5236\u66f2\u7ebf\u56fe\u3002\u4f20\u5165\u751f\u6210\u7684\u6570\u636e\u70b9 x<\/code>, y<\/code>, z<\/code>\uff1a<\/p>\n<\/p>\n
ax.plot(x, y, z, label='3D curve')<\/p>\n
ax.legend()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u8bbe\u7f6e\u56fe\u5f62\u5c5e\u6027<\/h3>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u56fe\u5f62\u5c5e\u6027\u6765\u7f8e\u5316\u56fe\u8868\uff0c\u5305\u62ec\u6807\u9898\u3001\u8f74\u6807\u7b7e\u3001\u56fe\u4f8b\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/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
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)<\/p>\n
z = np.linspace(-2, 2, 100)<\/p>\n
r = z2 + 1<\/p>\n
x = r * np.sin(theta)<\/p>\n
y = r * np.cos(theta)<\/p>\n
\u521b\u5efa\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\u66f2\u7ebf\u56fe<\/strong><\/h2>\n
ax.plot(x, y, z, label='3D curve')<\/p>\n
ax.legend()<\/p>\n
\u8bbe\u7f6e\u6807\u9898\u548c\u8f74\u6807\u7b7e<\/strong><\/h2>\n
ax.set_title('3D Curve Example')<\/p>\n
ax.set_xlabel('X axis')<\/p>\n
ax.set_ylabel('Y axis')<\/p>\n
ax.set_zlabel('Z axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u66f4\u591a\u7684\u7ed8\u56fe\u9009\u9879<\/h3>\n<\/p>\n
\u9664\u4e86\u57fa\u7840\u7684\u4e09\u7ef4\u66f2\u7ebf\u56fe\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7 Matplotlib \u5b9e\u73b0\u66f4\u591a\u590d\u6742\u7684\u4e09\u7ef4\u56fe\u5f62\uff0c\u6bd4\u5982\u6563\u70b9\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u7f51\u683c\u56fe\u7b49\u3002<\/p>\n<\/p>\n
1. \u4e09\u7ef4\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
# \u4e09\u7ef4\u6563\u70b9\u56fe<\/p>\n
ax.scatter(x, y, z, c='r', marker='o')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4e09\u7ef4\u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
# \u4e09\u7ef4\u67f1\u72b6\u56fe<\/p>\n
ax.bar3d(x, y, z, dx=0.1, dy=0.1, dz=z, color='b')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4e09\u7ef4\u7f51\u683c\u56fe<\/h4>\n<\/p>\n
# \u4e09\u7ef4\u7f51\u683c\u56fe<\/p>\n
X, Y = np.meshgrid(x, y)<\/p>\n
Z = np.sin(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
ax.plot_surface(X, Y, Z, cmap='viridis')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u4f7f\u7528 Plotly \u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u9700\u8981\u66f4\u73b0\u4ee3\u548c\u4ea4\u4e92\u5f0f\u7684\u56fe\u8868\uff0c\u53ef\u4ee5\u4f7f\u7528 Plotly \u5e93\u3002\u9996\u5148\u5b89\u88c5 Plotly\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import plotly.graph_objects as go<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)<\/p>\n
z = np.linspace(-2, 2, 100)<\/p>\n
r = z2 + 1<\/p>\n
x = r * np.sin(theta)<\/p>\n
y = r * np.cos(theta)<\/p>\n
\u521b\u5efa\u4e09\u7ef4\u66f2\u7ebf\u56fe<\/strong><\/h2>\n
fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z, mode='lines')])<\/p>\n
\u8bbe\u7f6e\u56fe\u5f62\u5c5e\u6027<\/strong><\/h2>\n
fig.update_layout(title='3D Curve Example', scene=dict(<\/p>\n
                    xaxis_title='X axis',<\/p>\n
                    yaxis_title='Y axis',<\/p>\n
                    zaxis_title='Z axis'))<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u4f7f\u7528 Mayavi \u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe<\/h3>\n<\/p>\n
Mayavi \u662f\u4e00\u4e2a\u5f3a\u5927\u7684 3D \u53ef\u89c6\u5316\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u9700\u8981\u9ad8\u6027\u80fd\u56fe\u5f62\u7684\u7528\u6237\u3002\u9996\u5148\u5b89\u88c5 Mayavi\uff1a<\/p>\n<\/p>\n
pip install mayavi<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
from mayavi import mlab<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)<\/p>\n
z = np.linspace(-2, 2, 100)<\/p>\n
r = z2 + 1<\/p>\n
x = r * np.sin(theta)<\/p>\n
y = r * np.cos(theta)<\/p>\n
\u521b\u5efa\u4e09\u7ef4\u66f2\u7ebf\u56fe<\/strong><\/h2>\n
mlab.plot3d(x, y, z)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
mlab.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728 Python \u4e2d\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u9ad8\u6548\u5730\u5b8c\u6210\u4efb\u52a1\u3002 Matplotlib<\/strong> \u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u5e93\uff0c\u9002\u5408\u521d\u5b66\u8005\u548c\u4e00\u822c\u7ed8\u56fe\u9700\u6c42\u3002 Plotly<\/strong> \u63d0\u4f9b\u73b0\u4ee3\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u7528\u6237\u3002 Mayavi<\/strong> \u9002\u7528\u4e8e\u9ad8\u6027\u80fd\u548c\u79d1\u5b66\u8ba1\u7b97\u56fe\u5f62\u7684\u9700\u6c42\u3002 \u901a\u8fc7\u5b66\u4e60\u548c\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\uff0c\u4f60\u53ef\u4ee5\u5728\u4e0d\u540c\u7684\u573a\u666f\u4e0b\u8fdb\u884c\u7075\u6d3b\u5e94\u7528\uff0c\u521b\u9020\u51fa\u4e30\u5bcc\u591a\u5f69\u7684\u4e09\u7ef4\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u901a\u5e38\u4f7f\u7528Matplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7mpl_toolkits.mplot3d<\/code>\u6a21\u5757\u6765\u521b\u5efa\u4e09\u7ef4\u56fe\u5f62\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\uff0c\u5e76\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757\u3002\u63a5\u7740\uff0c\u4f7f\u7528Axes3D<\/code>\u7c7b\u521b\u5efa\u4e09\u7ef4\u5750\u6807\u7cfb\uff0c\u5229\u7528plot<\/code>\u51fd\u6570\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u3002\u901a\u5e38\uff0c\u60a8\u8fd8\u53ef\u4ee5\u81ea\u5b9a\u4e49\u5750\u6807\u8f74\u6807\u7b7e\u548c\u56fe\u5f62\u6807\u9898\u4ee5\u589e\u5f3a\u53ef\u8bfb\u6027\u3002<\/p>\n
\u5728\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u65f6\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u53c2\u6570\u548c\u9009\u9879\u53ef\u4ee5\u8c03\u6574\uff1f<\/strong>
\u5728\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u65f6\uff0c\u53ef\u4ee5\u8c03\u6574\u591a\u79cd\u53c2\u6570\u4ee5\u6ee1\u8db3\u9700\u6c42\u3002\u8fd9\u4e9b\u5305\u62ec\u66f2\u7ebf\u7684\u989c\u8272\u3001\u7ebf\u578b\u3001\u7ebf\u5bbd\u4ee5\u53ca\u6570\u636e\u70b9\u7684\u6807\u8bb0\u6837\u5f0f\u7b49\u3002\u60a8\u4e5f\u53ef\u4ee5\u901a\u8fc7set_xlabel<\/code>\u3001set_ylabel<\/code>\u548cset_zlabel<\/code>\u65b9\u6cd5\u6765\u8bbe\u7f6e\u5750\u6807\u8f74\u7684\u6807\u7b7e\uff0c\u4f7f\u7528title<\/code>\u65b9\u6cd5\u6dfb\u52a0\u56fe\u5f62\u6807\u9898\u3002\u6b64\u5916\uff0cview_init<\/code>\u65b9\u6cd5\u53ef\u4ee5\u6539\u53d8\u89c2\u5bdf\u89d2\u5ea6\uff0c\u4f7f\u5f97\u56fe\u5f62\u66f4\u5177\u7acb\u4f53\u611f\u3002<\/p>\n
\u7ed8\u5236\u4e09\u7ef4\u66f2\u7ebf\u56fe\u65f6\u5982\u4f55\u5904\u7406\u6570\u636e\u7684\u7ef4\u5ea6\u95ee\u9898\uff1f<\/strong>
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