{"id":1087747,"date":"2025-01-08T13:38:00","date_gmt":"2025-01-08T05:38:00","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1087747.html"},"modified":"2025-01-08T13:38:03","modified_gmt":"2025-01-08T05:38:03","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e4%b8%89%e7%bb%b4%e5%9b%be-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1087747.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u4e09\u7ef4\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u7ed8\u5236\u4e09\u7ef4\u56fe\"<\/p>\n

Python\u7ed8\u5236\u4e09\u7ef4\u56fe\u53ef\u4ee5\u4f7f\u7528Matplotlib\u3001Mayavi\u3001Plotly\u7b49\u5e93<\/strong>\uff0c\u5176\u4e2d\u4f7f\u7528\u6700\u5e7f\u6cdb\u7684\u662fMatplotlib\u3002\u4e0b\u9762\u6211\u4eec\u8be6\u7ec6\u8bb2\u89e3\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e09\u7ef4\u56fe\u3002<\/p>\n<\/p>\n

\u4e00\u3001Matplotlib\u7ed8\u5236\u4e09\u7ef4\u56fe<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u4e5f\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u3002\u901a\u8fc7\u5176mplot3d\u5de5\u5177\u5305\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u3002<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
\u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u5bfc\u5165\u5fc5\u8981\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
3\u3001\u521b\u5efa\u4e09\u7ef4\u5750\u6807\u7cfb<\/h4>\n<\/p>\n
\u5728Matplotlib\u4e2d\uff0c\u521b\u5efa\u4e09\u7ef4\u5750\u6807\u7cfb\u662f\u901a\u8fc7Axes3D<\/code>\u5bf9\u8c61\u6765\u5b9e\u73b0\u7684\uff1a<\/p>\n<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u7ed8\u5236\u4e09\u7ef4\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u4e09\u7ef4\u6563\u70b9\u56fe\u662f\u6700\u5e38\u89c1\u7684\u4e09\u7ef4\u56fe\u4e4b\u4e00\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u6570\u636e<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
ax.scatter(x, y, z)<\/p>\n
\u8bbe\u7f6e\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
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u7ed8\u5236\u4e09\u7ef4\u66f2\u9762\u56fe<\/h4>\n<\/p>\n
\u4e09\u7ef4\u66f2\u9762\u56fe\u5728\u5c55\u793a\u6570\u5b66\u51fd\u6570\u548c\u6570\u636e\u6a21\u578b\u65f6\u975e\u5e38\u6709\u7528\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u6570\u636e<\/p>\n
X = np.linspace(-5, 5, 100)<\/p>\n
Y = np.linspace(-5, 5, 100)<\/p>\n
X, Y = np.meshgrid(X, Y)<\/p>\n
Z = np.sin(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
\u7ed8\u5236\u66f2\u9762\u56fe<\/strong><\/h2>\n
ax.plot_surface(X, Y, Z, cmap='viridis')<\/p>\n
\u8bbe\u7f6e\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
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Mayavi\u7ed8\u5236\u4e09\u7ef4\u56fe<\/h3>\n<\/p>\n
Mayavi\u662f\u4e00\u6b3e\u529f\u80fd\u5f3a\u5927\u7684\u4e09\u7ef4\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u66f4\u590d\u6742\u7684\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Mayavi<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Mayavi\uff1a<\/p>\n<\/p>\n
pip install mayavi<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
\u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n<\/p>\n
from mayavi import mlab<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u4e09\u7ef4\u6570\u636e\u70b9<\/h4>\n<\/p>\n
Mayavi\u7684\u7ed8\u56fe\u65b9\u5f0f\u4e0eMatplotlib\u6709\u6240\u4e0d\u540c\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u6570\u636e<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
s = np.random.rand(100)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
mlab.points3d(x, y, z, s, scale_factor=0.1)<\/p>\n
mlab.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u7ed8\u5236\u4e09\u7ef4\u66f2\u9762\u56fe<\/h4>\n<\/p>\n
\u4e09\u7ef4\u66f2\u9762\u56fe\u5728Mayavi\u4e2d\u4e5f\u975e\u5e38\u5bb9\u6613\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u6570\u636e<\/p>\n
X = np.linspace(-5, 5, 100)<\/p>\n
Y = np.linspace(-5, 5, 100)<\/p>\n
X, Y = np.meshgrid(X, Y)<\/p>\n
Z = np.sin(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
\u7ed8\u5236\u66f2\u9762\u56fe<\/strong><\/h2>\n
mlab.surf(X, Y, Z, colormap='viridis')<\/p>\n
mlab.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Plotly\u7ed8\u5236\u4e09\u7ef4\u56fe<\/h3>\n<\/p>\n
Plotly\u662f\u4e00\u6b3e\u4ea4\u4e92\u6027\u975e\u5e38\u5f3a\u7684\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u64cd\u4f5c\u7684\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Plotly<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Plotly\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
\u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n<\/p>\n
import plotly.graph_objects as go<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u4e09\u7ef4\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
Plotly\u7684\u7ed8\u5236\u65b9\u5f0f\u4e5f\u975e\u5e38\u7b80\u4fbf\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u6570\u636e<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z, mode='markers')])<\/p>\n
fig.update_layout(scene=dict(<\/p>\n
    xaxis_title='X Axis',<\/p>\n
    yaxis_title='Y Axis',<\/p>\n
    zaxis_title='Z Axis'<\/p>\n
))<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u7ed8\u5236\u4e09\u7ef4\u66f2\u9762\u56fe<\/h4>\n<\/p>\n
\u4e09\u7ef4\u66f2\u9762\u56fe\u5728Plotly\u4e2d\u4e5f\u6709\u4e13\u95e8\u7684\u51fd\u6570\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u6570\u636e<\/p>\n
X = np.linspace(-5, 5, 100)<\/p>\n
Y = np.linspace(-5, 5, 100)<\/p>\n
X, Y = np.meshgrid(X, Y)<\/p>\n
Z = np.sin(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
\u7ed8\u5236\u66f2\u9762\u56fe<\/strong><\/h2>\n
fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y)])<\/p>\n
fig.update_layout(scene=dict(<\/p>\n
    xaxis_title='X Axis',<\/p>\n
    yaxis_title='Y Axis',<\/p>\n
    zaxis_title='Z Axis'<\/p>\n
))<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\u7684\u5e38\u89c1\u95ee\u9898<\/h3>\n<\/p>\n
\u5728\u4f7f\u7528Python\u7ed8\u5236\u4e09\u7ef4\u56fe\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\uff1a<\/p>\n<\/p>\n
1\u3001\u5750\u6807\u8f74\u8303\u56f4\u8bbe\u7f6e<\/h4>\n<\/p>\n
\u5728\u4e09\u7ef4\u56fe\u5f62\u4e2d\uff0c\u8bbe\u7f6e\u5750\u6807\u8f74\u8303\u56f4\u975e\u5e38\u91cd\u8981\uff0c\u53ef\u4ee5\u901a\u8fc7set_xlim<\/code>\u3001set_ylim<\/code>\u3001set_zlim<\/code>\u7b49\u51fd\u6570\u6765\u8bbe\u7f6e\uff1a<\/p>\n<\/p>\n
ax.set_xlim([xmin, xmax])<\/p>\n
ax.set_ylim([ymin, ymax])<\/p>\n
ax.set_zlim([zmin, zmax])<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u989c\u8272\u6620\u5c04<\/h4>\n<\/p>\n
\u989c\u8272\u6620\u5c04\u53ef\u4ee5\u5e2e\u52a9\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u5206\u5e03\uff0c\u53ef\u4ee5\u901a\u8fc7cmap<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\uff1a<\/p>\n<\/p>\n
ax.plot_surface(X, Y, Z, cmap='viridis')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4ea4\u4e92\u64cd\u4f5c<\/h4>\n<\/p>\n
\u4f7f\u7528Plotly\u7ed8\u5236\u7684\u4e09\u7ef4\u56fe\u5f62\u5177\u6709\u5f88\u5f3a\u7684\u4ea4\u4e92\u6027\uff0c\u53ef\u4ee5\u901a\u8fc7\u9f20\u6807\u64cd\u4f5c\u8fdb\u884c\u65cb\u8f6c\u3001\u7f29\u653e\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
Python\u63d0\u4f9b\u4e86\u591a\u79cd\u7ed8\u5236\u4e09\u7ef4\u56fe\u7684\u5de5\u5177\uff0c\u6bcf\u79cd\u5de5\u5177\u90fd\u6709\u5176\u72ec\u7279\u7684\u7279\u70b9\u548c\u9002\u7528\u573a\u666f\u3002Matplotlib\u9002\u7528\u4e8e\u57fa\u7840\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\u3001Mayavi\u9002\u7528\u4e8e\u590d\u6742\u7684\u4e09\u7ef4\u6570\u636e\u53ef\u89c6\u5316\u3001Plotly\u9002\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u64cd\u4f5c\u7684\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236<\/strong>\u3002\u901a\u8fc7\u5408\u7406\u9009\u62e9\u548c\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e30\u5bcc\u591a\u6837\u7684\u4e09\u7ef4\u6570\u636e\u53ef\u89c6\u5316\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728Python\u4e2d\u7ed8\u5236\u4e09\u7ef4\u56fe\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u7ed8\u5236\u4e09\u7ef4\u56fe\u901a\u5e38\u4f7f\u7528matplotlib<\/code>\u5e93\uff0c\u5c24\u5176\u662fmpl_toolkits.mplot3d<\/code>\u6a21\u5757\u3002\u6b64\u5916\uff0cnumpy<\/code>\u5e93\u5e38\u7528\u4e8e\u751f\u6210\u6570\u636e\uff0cpandas<\/code>\u5e93\u5219\u53ef\u4ee5\u5e2e\u52a9\u5904\u7406\u6570\u636e\u96c6\u3002\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u4e09\u7ef4\u53ef\u89c6\u5316\uff0cplotly<\/code>\u548cMayavi<\/code>\u4e5f\u662f\u4e0d\u9519\u7684\u9009\u62e9\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u7b80\u5355\u7684\u4e09\u7ef4\u6563\u70b9\u56fe\uff1f<\/strong>
\u521b\u5efa\u4e09\u7ef4\u6563\u70b9\u56fe\u7684\u6b65\u9aa4\u5305\u62ec\u5bfc\u5165\u6240\u9700\u7684\u5e93\uff0c\u751f\u6210\u6570\u636e\uff0c\u4ee5\u53ca\u4f7f\u7528Axes3D.scatter<\/code>\u65b9\u6cd5\u7ed8\u5236\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u8981\u793a\u4f8b\uff1a<\/p>\n
import matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nx = np.random.rand(100)\ny = np.random.rand(100)\nz = np.random.rand(100)\nax.scatter(x, y, z)\nplt.show()\n<\/code><\/pre>\n
\u8fd9\u4e2a\u4ee3\u7801\u5c06\u751f\u6210\u4e00\u4e2a\u5305\u542b100\u4e2a\u968f\u673a\u70b9\u7684\u4e09\u7ef4\u6563\u70b9\u56fe\u3002<\/p>\n
\u53ef\u4ee5\u5728\u4e09\u7ef4\u56fe\u4e2d\u6dfb\u52a0\u54ea\u4e9b\u5143\u7d20\u4ee5\u63d0\u5347\u53ef\u8bfb\u6027\uff1f<\/strong>
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