{"id":1063807,"date":"2024-12-31T16:03:44","date_gmt":"2024-12-31T08:03:44","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1063807.html"},"modified":"2024-12-31T16:03:46","modified_gmt":"2024-12-31T08:03:46","slug":"python%e4%b8%89%e7%bb%b4%e5%9b%be%e5%a6%82%e4%bd%95%e6%a0%87%e6%b3%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1063807.html","title":{"rendered":"python\u4e09\u7ef4\u56fe\u5982\u4f55\u6807\u6ce8"},"content":{"rendered":"

\"python\u4e09\u7ef4\u56fe\u5982\u4f55\u6807\u6ce8\"<\/p>\n

Python\u4e09\u7ef4\u56fe\u5982\u4f55\u6807\u6ce8<\/strong>\uff1a\u4f7f\u7528Matplotlib\u3001\u4f7f\u7528Mayavi\u3001\u4f7f\u7528Plotly\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e09\u79cd\u5de5\u5177\u5728Python\u4e2d\u7ed8\u5236\u4e09\u7ef4\u56fe\u5e76\u8fdb\u884c\u6807\u6ce8\u3002\u63a8\u8350\u4f7f\u7528Matplotlib<\/strong>\uff0c\u56e0\u4e3a\u5b83\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u4e8c\u7ef4\u548c\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\uff0c\u5e76\u4e14\u6709\u4e30\u5bcc\u7684\u6587\u6863\u548c\u793e\u533a\u652f\u6301\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib<\/h3>\n<\/p>\n

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

\u5728\u5f00\u59cb\u4f7f\u7528Matplotlib\u4e4b\u524d\uff0c\u60a8\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8be5\u5e93\u3002\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\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\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62<\/h4>\n<\/p>\n
Matplotlib\u901a\u8fc7mpl_toolkits.mplot3d<\/code>\u6a21\u5757\u6765\u521b\u5efa\u4e09\u7ef4\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u7ed8\u5236\u4e00\u4e2a\u4e09\u7ef4\u6563\u70b9\u56fe\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
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
ax.scatter(x, y, z)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4e09\u7ef4\u56fe\u5f62\u6807\u6ce8<\/h4>\n<\/p>\n
\u5728\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u65f6\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u4e3a\u7279\u5b9a\u7684\u6570\u636e\u70b9\u6dfb\u52a0\u6807\u6ce8\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u6dfb\u52a0\u6807\u6ce8\u7684\u793a\u4f8b\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
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
ax.scatter(x, y, z)<\/p>\n
\u4e3a\u7279\u5b9a\u70b9\u6dfb\u52a0\u6807\u6ce8<\/strong><\/h2>\n
for i in range(len(x)):<\/p>\n
    ax.text(x[i], y[i], z[i], f'Point {i}', size=8, zorder=1, color='k')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Mayavi<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5Mayavi<\/h4>\n<\/p>\n
Mayavi\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u4e09\u7ef4\u53ef\u89c6\u5316\u5e93\uff0c\u9002\u7528\u4e8e\u66f4\u590d\u6742\u7684\u4e09\u7ef4\u7ed8\u56fe\u9700\u6c42\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5Mayavi\uff1a<\/p>\n<\/p>\n
pip install mayavi<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62<\/h4>\n<\/p>\n
Mayavi\u4f7f\u7528mlab\u6a21\u5757\u6765\u521b\u5efa\u4e09\u7ef4\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from mayavi import mlab<\/p>\n
import numpy as np<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
mlab.points3d(x, y, z, scale_factor=0.1)<\/p>\n
mlab.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4e09\u7ef4\u56fe\u5f62\u6807\u6ce8<\/h4>\n<\/p>\n
Mayavi\u652f\u6301\u5728\u4e09\u7ef4\u56fe\u5f62\u4e2d\u6dfb\u52a0\u6807\u6ce8\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u6dfb\u52a0\u6807\u6ce8\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from mayavi import mlab<\/p>\n
import numpy as np<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
points = mlab.points3d(x, y, z, scale_factor=0.1)<\/p>\n
for i in range(len(x)):<\/p>\n
    mlab.text3d(x[i], y[i], z[i], f'{i}', scale=0.1)<\/p>\n
mlab.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Plotly<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5Plotly<\/h4>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u56fe\u5f62\u5e93\uff0c\u9002\u7528\u4e8eWeb\u5e94\u7528\u7a0b\u5e8f\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5Plotly\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62<\/h4>\n<\/p>\n
Plotly\u975e\u5e38\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u7684\u4e09\u7ef4\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
import numpy as np<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
trace = go.Scatter3d(<\/p>\n
    x=x,<\/p>\n
    y=y,<\/p>\n
    z=z,<\/p>\n
    mode='markers'<\/p>\n
)<\/p>\n
data = [trace]<\/p>\n
fig = go.Figure(data=data)<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4e09\u7ef4\u56fe\u5f62\u6807\u6ce8<\/h4>\n<\/p>\n
Plotly\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6807\u6ce8\u529f\u80fd\uff0c\u53ef\u4ee5\u5728\u4e09\u7ef4\u56fe\u5f62\u4e2d\u6dfb\u52a0\u6807\u7b7e\u548c\u6ce8\u91ca\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u6dfb\u52a0\u6807\u6ce8\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
import numpy as np<\/p>\n
x = np.random.rand(100)<\/p>\n
y = np.random.rand(100)<\/p>\n
z = np.random.rand(100)<\/p>\n
trace = go.Scatter3d(<\/p>\n
    x=x,<\/p>\n
    y=y,<\/p>\n
    z=z,<\/p>\n
    mode='markers+text',<\/p>\n
    text=[f'Point {i}' for i in range(len(x))],<\/p>\n
    textposition='top center'<\/p>\n
)<\/p>\n
data = [trace]<\/p>\n
fig = go.Figure(data=data)<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u4e09\u79cd\u4e0d\u540c\u7684\u5e93\uff08Matplotlib\u3001Mayavi\u3001Plotly\uff09\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\u5e76\u8fdb\u884c\u6807\u6ce8\u3002Matplotlib\u9002\u5408\u4e00\u822c\u7684\u4e09\u7ef4\u7ed8\u56fe\u9700\u6c42<\/strong>\uff0cMayavi\u9002\u7528\u4e8e\u66f4\u590d\u6742\u7684\u4e09\u7ef4\u53ef\u89c6\u5316<\/strong>\uff0cPlotly\u5219\u975e\u5e38\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868<\/strong>\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u56fe\u8868\u7684\u7f8e\u89c2\u6027\u3002<\/p>\n<\/p>\n
1\u3001\u9009\u62e9\u5408\u9002\u7684\u5e93<\/h4>\n<\/p>\n
\u6839\u636e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u5e93\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u5982\u679c\u60a8\u7684\u9700\u6c42\u662f\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u4e09\u7ef4\u56fe\u5f62\u5e76\u8fdb\u884c\u6807\u6ce8\uff0cMatplotlib\u53ef\u80fd\u662f\u6700\u597d\u7684\u9009\u62e9\u3002\u5982\u679c\u9700\u8981\u66f4\u9ad8\u7ea7\u7684\u4e09\u7ef4\u53ef\u89c6\u5316\u529f\u80fd\uff0cMayavi\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002\u5982\u679c\u9700\u8981\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0cPlotly\u5c06\u4f1a\u662f\u60a8\u7684\u6700\u4f73\u9009\u62e9\u3002<\/p>\n<\/p>\n
2\u3001\u719f\u6089\u5e93\u7684\u4f7f\u7528<\/h4>\n<\/p>\n
\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u5e93\uff0c\u90fd\u9700\u8981\u82b1\u65f6\u95f4\u719f\u6089\u5176\u4f7f\u7528\u65b9\u6cd5\u548c\u529f\u80fd\u3002\u9605\u8bfb\u5b98\u65b9\u6587\u6863\u548c\u793a\u4f8b\u4ee3\u7801\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u5f00\u59cb\u3002\u901a\u8fc7\u4e0d\u65ad\u7684\u5b9e\u8df5\u548c\u5c1d\u8bd5\uff0c\u60a8\u5c06\u80fd\u591f\u638c\u63e1\u8fd9\u4e9b\u5e93\u7684\u4f7f\u7528\u6280\u5de7\uff0c\u4ece\u800c\u80fd\u591f\u521b\u5efa\u51fa\u66f4\u52a0\u4e13\u4e1a\u548c\u7f8e\u89c2\u7684\u4e09\u7ef4\u56fe\u5f62\u3002<\/p>\n<\/p>\n
3\u3001\u6301\u7eed\u5b66\u4e60\u548c\u6539\u8fdb<\/h4>\n<\/p>\n
\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\u548c\u6807\u6ce8\u662f\u4e00\u4e2a\u4e0d\u65ad\u5b66\u4e60\u548c\u6539\u8fdb\u7684\u8fc7\u7a0b\u3002\u968f\u7740\u6280\u672f\u7684\u53d1\u5c55\u548c\u9700\u6c42\u7684\u53d8\u5316\uff0c\u65b0\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u4e0d\u65ad\u51fa\u73b0\u3002\u4fdd\u6301\u5bf9\u65b0\u6280\u672f\u7684\u5173\u6ce8\uff0c\u6301\u7eed\u5b66\u4e60\u548c\u6539\u8fdb\uff0c\u662f\u63d0\u9ad8\u7ed8\u56fe\u80fd\u529b\u548c\u56fe\u8868\u8d28\u91cf\u7684\u5173\u952e\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u76f8\u4fe1\u60a8\u5df2\u7ecf\u5bf9Python\u4e2d\u4e09\u7ef4\u56fe\u5f62\u7684\u7ed8\u5236\u548c\u6807\u6ce8\u6709\u4e86\u4e00\u4e2a\u5168\u9762\u7684\u4e86\u89e3\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u80fd\u5bf9\u60a8\u7684\u5de5\u4f5c\u548c\u5b66\u4e60\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u4e09\u7ef4\u56fe\u5e76\u8fdb\u884c\u6807\u6ce8\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u521b\u5efa\u4e09\u7ef4\u56fe\u3002\u4f7f\u7528mpl_toolkits.mplot3d<\/code>\u6a21\u5757\u4e2d\u7684Axes3D<\/code>\u7c7b\uff0c\u80fd\u591f\u8f7b\u677e\u7ed8\u5236\u4e09\u7ef4\u6563\u70b9\u56fe\u3001\u66f2\u9762\u56fe\u7b49\u3002\u4e3a\u4e86\u8fdb\u884c\u6807\u6ce8\uff0c\u53ef\u4ee5\u4f7f\u7528text()<\/code>\u65b9\u6cd5\u5728\u7279\u5b9a\u7684\u5750\u6807\u4f4d\u7f6e\u6dfb\u52a0\u6587\u672c\u6807\u7b7e\uff0c\u6216\u8005\u4f7f\u7528annotate()<\/code>\u65b9\u6cd5\u4e3a\u7279\u5b9a\u7684\u6570\u636e\u70b9\u6dfb\u52a0\u6ce8\u91ca\u3002<\/p>\n
\u4f7f\u7528\u54ea\u4e9b\u5e93\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u4e09\u7ef4\u56fe\u6807\u6ce8\uff1f<\/strong>
\u9664\u4e86Matplotlib\uff0cPlotly\u548cMayavi\u4e5f\u662f\u975e\u5e38\u9002\u5408\u8fdb\u884c\u4e09\u7ef4\u53ef\u89c6\u5316\u548c\u6807\u6ce8\u7684\u5e93\u3002Plotly\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u7684\u56fe\u5f62\uff0c\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u9f20\u6807\u60ac\u505c\u83b7\u5f97\u8be6\u7ec6\u7684\u4fe1\u606f\uff0c\u800cMayavi\u5219\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u4e09\u7ef4\u6570\u636e\u5e76\u63d0\u4f9b\u4e30\u5bcc\u7684\u53ef\u89c6\u5316\u9009\u9879\u3002<\/p>\n
\u5728\u4e09\u7ef4\u56fe\u4e2d\u6807\u6ce8\u7684\u6700\u4f73\u5b9e\u8df5\u662f\u4ec0\u4e48\uff1f<\/strong>
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