{"id":953048,"date":"2024-12-27T01:39:05","date_gmt":"2024-12-26T17:39:05","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/953048.html"},"modified":"2024-12-27T01:39:07","modified_gmt":"2024-12-26T17:39:07","slug":"python-%e5%9b%be%e5%a6%82%e4%bd%95%e6%98%be%e7%a4%ba%e8%b4%9f%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/953048.html","title":{"rendered":"python \u56fe\u5982\u4f55\u663e\u793a\u8d1f\u503c"},"content":{"rendered":"

\"python<\/p>\n

\u663e\u793a\u8d1f\u503c\u56fe\u5f62\u5728Python\u4e2d\u5f88\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u4f7f\u7528\u5408\u9002\u7684\u5e93\u3001\u786e\u4fdd\u6570\u636e\u51c6\u5907\u6b63\u786e\u3001\u8bbe\u7f6e\u5408\u9002\u7684\u5750\u6807\u8f74\u8303\u56f4<\/strong>\uff0c\u662f\u5173\u952e\u6b65\u9aa4\u3002\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u901a\u8fc7Python\u7ed8\u5236\u8d1f\u503c\u56fe\u5f62\uff0c\u5e76\u5c55\u793a\u4e00\u4e9b\u5b9e\u7528\u7684\u6280\u5de7\u548c\u4ee3\u7801\u793a\u4f8b\u6765\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u638c\u63e1\u8fd9\u4e00\u6280\u5de7\u3002<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0cMatplotlib\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u5b83\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\uff0c\u5305\u62ec\u663e\u793a\u8d1f\u503c\u7684\u56fe\u5f62\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u901a\u8fc7\u51e0\u4e2a\u6b65\u9aa4\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u6765\u8be6\u7ec6\u8ba8\u8bba\u5982\u4f55\u5728\u56fe\u4e2d\u663e\u793a\u8d1f\u503c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528MATPLOTLIB\u7ed8\u5236\u8d1f\u503c\u56fe\u5f62<\/h2>\n<\/p>\n

1.1\u3001\u5b89\u88c5\u548c\u5bfc\u5165MATPLOTLIB\u5e93<\/h3>\n<\/p>\n

\u8981\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u5e93\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5b83\u5df2\u7ecf\u5b89\u88c5\u3002\u5982\u679c\u672a\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
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Matplotlib\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n
\u5728\u7ed8\u5236\u56fe\u5f62\u4e4b\u524d\uff0c\u9700\u8981\u51c6\u5907\u597d\u5305\u542b\u8d1f\u503c\u7684\u6570\u636e\u3002\u6570\u636e\u53ef\u4ee5\u4ee5\u5217\u8868\u3001\u6570\u7ec4\u6216\u5176\u4ed6\u5f62\u5f0f\u5b58\u5728\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
x = [-10, -5, 0, 5, 10]<\/p>\n
y = [100, 25, 0, 25, 100]<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3\u3001\u7ed8\u5236\u8d1f\u503c\u56fe\u5f62<\/h3>\n<\/p>\n
\u4f7f\u7528Matplotlib\u7ed8\u5236\u8d1f\u503c\u56fe\u5f62\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u8c03\u7528\u76f8\u5e94\u7684\u7ed8\u56fe\u51fd\u6570\u3002\u4f8b\u5982\uff0c\u7ed8\u5236\u7ebf\u56fe\u53ef\u4ee5\u4f7f\u7528plot<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
plt.plot(x, y)<\/p>\n
plt.title('Plot with Negative Values')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cx<\/code>\u548cy<\/code>\u8f74\u90fd\u5305\u542b\u8d1f\u503c\uff0c\u4f7f\u7528plt.plot<\/code>\u51fd\u6570\u5373\u53ef\u8f7b\u677e\u5730\u5728\u56fe\u4e2d\u663e\u793a\u8fd9\u4e9b\u8d1f\u503c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5904\u7406\u8d1f\u503c\u7684\u5176\u4ed6\u56fe\u5f62\u7c7b\u578b<\/h2>\n<\/p>\n
2.1\u3001\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
\u6563\u70b9\u56fe\u662f\u4e00\u79cd\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5173\u7cfb\u7684\u56fe\u5f62\u7c7b\u578b\u3002\u8981\u7ed8\u5236\u5305\u542b\u8d1f\u503c\u7684\u6563\u70b9\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528scatter<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
x = [-10, -5, 0, 5, 10]<\/p>\n
y = [100, 25, 0, 25, 100]<\/p>\n
plt.scatter(x, y, color='red')<\/p>\n
plt.title('Scatter Plot with Negative Values')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
\u67f1\u72b6\u56fe\u53ef\u4ee5\u7528\u4e8e\u663e\u793a\u8d1f\u503c\uff0c\u5c24\u5176\u662f\u5728\u6bd4\u8f83\u6b63\u8d1f\u503c\u65f6\u975e\u5e38\u6709\u7528\u3002\u4f7f\u7528bar<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u5305\u542b\u8d1f\u503c\u7684\u67f1\u72b6\u56fe\uff1a<\/p>\n<\/p>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [-30, -10, 20, 10, 30]<\/p>\n
plt.bar(categories, values, color='blue')<\/p>\n
plt.title('Bar Chart with Negative Values')<\/p>\n
plt.xlabel('Categories')<\/p>\n
plt.ylabel('Values')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3\u3001\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u76f4\u65b9\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u4f7f\u7528hist<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u5305\u542b\u8d1f\u503c\u7684\u76f4\u65b9\u56fe\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.random.normal(loc=0, scale=1, size=1000)<\/p>\n
plt.hist(data, bins=30, color='green', alpha=0.7)<\/p>\n
plt.title('Histogram with Negative Values')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8c03\u6574\u56fe\u5f62\u663e\u793a\u8303\u56f4<\/h2>\n<\/p>\n
3.1\u3001\u8bbe\u7f6e\u5750\u6807\u8f74\u8303\u56f4<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u9700\u8981\u624b\u52a8\u8bbe\u7f6e\u5750\u6807\u8f74\u7684\u8303\u56f4\u4ee5\u786e\u4fdd\u8d1f\u503c\u663e\u793a\u5f97\u5f53\u3002\u53ef\u4ee5\u4f7f\u7528xlim<\/code>\u548cylim<\/code>\u51fd\u6570\u6765\u8bbe\u7f6ex\u8f74\u548cy\u8f74\u7684\u663e\u793a\u8303\u56f4\uff1a<\/p>\n<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlim(-15, 15)<\/p>\n
plt.ylim(-50, 150)<\/p>\n
plt.title('Plot with Custom Axis Limits')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2\u3001\u4f7f\u7528\u5bf9\u6570\u5750\u6807\u8f74<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4f7f\u7528\u5bf9\u6570\u5750\u6807\u8f74\u53ef\u4ee5\u66f4\u597d\u5730\u663e\u793a\u6570\u636e\u7684\u7279\u5f81\u3002Matplotlib\u5141\u8bb8\u901a\u8fc7yscale<\/code>\u548cxscale<\/code>\u51fd\u6570\u8bbe\u7f6e\u5bf9\u6570\u5750\u6807\u8f74\uff1a<\/p>\n<\/p>\n
x = np.linspace(-10, 10, 400)<\/p>\n
y = np.sinh(x)<\/p>\n
plt.plot(x, y)<\/p>\n
plt.yscale('symlog')<\/p>\n
plt.title('Plot with Symmetric Logarithmic Scale')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u5176\u4ed6\u7ed8\u56fe\u5e93\u7ed8\u5236\u8d1f\u503c\u56fe\u5f62<\/h2>\n<\/p>\n
\u867d\u7136Matplotlib\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u4f46Python\u4e2d\u8fd8\u6709\u5176\u4ed6\u5e93\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u56fe\u5f62\uff0c\u5982Seaborn\u548cPlotly\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u529f\u80fd\u548c\u66f4\u597d\u7684\u56fe\u5f62\u7f8e\u89c2\u6027\u3002<\/p>\n<\/p>\n
4.1\u3001Seaborn\u5e93<\/h3>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7f8e\u89c2\u7684\u9ed8\u8ba4\u56fe\u5f62\u6837\u5f0f\u3002\u8981\u4f7f\u7528Seaborn\u7ed8\u5236\u8d1f\u503c\u56fe\u5f62\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165Seaborn\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
import seaborn as sns<\/p>\n
\u793a\u4f8b\uff1a\u4f7f\u7528Seaborn\u7ed8\u5236\u5305\u542b\u8d1f\u503c\u7684\u6563\u70b9\u56fe<\/strong><\/h2>\n
tips = sns.load_dataset('tips')<\/p>\n
sns.scatterplot(x='total_bill', y='tip', data=tips)<\/p>\n
plt.title('Scatter Plot with Seaborn')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2\u3001Plotly\u5e93<\/h3>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u56fe\u5f62\u3002\u4f7f\u7528Plotly\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u5305\u542b\u8d1f\u503c\u7684\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
import plotly.express as px<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\uff1a\u4f7f\u7528Plotly\u7ed8\u5236\u5305\u542b\u8d1f\u503c\u7684\u6298\u7ebf\u56fe<\/strong><\/h2>\n
df = pd.DataFrame({<\/p>\n
    'x': [-10, -5, 0, 5, 10],<\/p>\n
    'y': [100, 25, 0, 25, 100]<\/p>\n
})<\/p>\n
fig = px.line(df, x='x', y='y', title='Plotly Line Plot with Negative Values')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5904\u7406\u8d1f\u503c\u6570\u636e\u7684\u6280\u5de7<\/h2>\n<\/p>\n
5.1\u3001\u6570\u636e\u6807\u51c6\u5316<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u8d1f\u503c\u6570\u636e\u65f6\uff0c\u6570\u636e\u6807\u51c6\u5316\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u6807\u51c6\u5316\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u5728\u76f8\u540c\u7684\u5c3a\u5ea6\u4e0a\u8fdb\u884c\u6bd4\u8f83\u3002\u53ef\u4ee5\u4f7f\u7528scikit-learn<\/code>\u5e93\u4e2d\u7684StandardScaler<\/code>\u8fdb\u884c\u6570\u636e\u6807\u51c6\u5316\uff1a<\/p>\n<\/p>\n
pip install scikit-learn<\/p>\n
<\/code><\/pre>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
import numpy as np<\/p>\n
data = np.array([-100, -50, 0, 50, 100]).reshape(-1, 1)<\/p>\n
scaler = StandardScaler()<\/p>\n
standardized_data = scaler.fit_transform(data)<\/p>\n
print(standardized_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2\u3001\u4f7f\u7528\u5408\u9002\u7684\u989c\u8272\u548c\u6837\u5f0f<\/h3>\n<\/p>\n
\u5728\u56fe\u5f62\u4e2d\u4f7f\u7528\u5408\u9002\u7684\u989c\u8272\u548c\u6837\u5f0f\uff0c\u53ef\u4ee5\u5e2e\u52a9\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002\u5c24\u5176\u662f\u5728\u5904\u7406\u8d1f\u503c\u65f6\uff0c\u9009\u62e9\u5bf9\u6bd4\u9c9c\u660e\u7684\u989c\u8272\u53ef\u4ee5\u7a81\u51fa\u663e\u793a\u8d1f\u503c\u3002<\/p>\n<\/p>\n
plt.plot(x, y, color='red', linestyle='--', marker='o')<\/p>\n
plt.title('Styled Plot with Negative Values')<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u5728Python\u4e2d\u663e\u793a\u8d1f\u503c\u56fe\u5f62\u5e76\u4e0d\u56f0\u96be\uff0c\u901a\u8fc7\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\u3002\u5728\u7ed8\u5236\u8d1f\u503c\u56fe\u5f62\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u51c6\u5907\u6b63\u786e\u3001\u8bbe\u7f6e\u5408\u9002\u7684\u5750\u6807\u8f74\u8303\u56f4\uff0c\u5e76\u4f7f\u7528\u5408\u9002\u7684\u989c\u8272\u548c\u6837\u5f0f\uff0c\u662f\u6210\u529f\u7684\u5173\u952e\u3002\u901a\u8fc7\u672c\u6587\u63d0\u4f9b\u7684\u793a\u4f8b\u4ee3\u7801\u548c\u6280\u5de7\uff0c\u4f60\u5e94\u8be5\u80fd\u591f\u66f4\u597d\u5730\u5728Python\u4e2d\u5904\u7406\u548c\u663e\u793a\u8d1f\u503c\u56fe\u5f62\u3002\u5e0c\u671b\u8fd9\u4e9b\u4fe1\u606f\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u56fe\u8868\u4e2d\u663e\u793a\u8d1f\u503c\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7b49\u7ed8\u56fe\u5e93\u6765\u521b\u5efa\u56fe\u8868\u5e76\u663e\u793a\u8d1f\u503c\u3002\u786e\u4fdd\u5728\u7ed8\u5236\u56fe\u5f62\u65f6\uff0cy\u8f74\u8303\u56f4\u5305\u62ec\u8d1f\u503c\u3002\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eplt.ylim()<\/code>\u51fd\u6570\u6765\u63a7\u5236y\u8f74\u7684\u4e0a\u4e0b\u9650\u3002\u4f8b\u5982\uff0cplt.ylim(-10, 10)<\/code>\u53ef\u4ee5\u8ba9y\u8f74\u8303\u56f4\u4ece-10\u523010\uff0c\u786e\u4fdd\u8d1f\u503c\u80fd\u591f\u6b63\u786e\u663e\u793a\u3002<\/p>\n
\u4f7f\u7528\u54ea\u4e2a\u5e93\u6700\u9002\u5408\u663e\u793a\u8d1f\u503c\u7684\u56fe\u8868\uff1f<\/strong>
Matplotlib\u662f\u6700\u5e38\u7528\u7684\u5e93\u4e4b\u4e00\uff0c\u5b83\u652f\u6301\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u7b49\u3002Seaborn\u662f\u5efa\u7acb\u5728Matplotlib\u57fa\u7840\u4e0a\u7684\u5e93\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\uff0c\u9002\u5408\u6570\u636e\u53ef\u89c6\u5316\u3002\u5982\u679c\u60a8\u9700\u8981\u66f4\u590d\u6742\u7684\u56fe\u5f62\uff0cPlotly\u4e5f\u53ef\u4ee5\u5e2e\u52a9\u60a8\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n
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\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u8d1f\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u7edf\u8ba1\u7ed3\u679c\u548c\u53ef\u89c6\u5316\u6548\u679c\u3002\u5728\u5206\u6790\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u8003\u8651\u5c06\u8d1f\u503c\u5355\u72ec\u5904\u7406\uff0c\u6bd4\u5982\u4f7f\u7528\u7edd\u5bf9\u503c\u6765\u8fdb\u884c\u6bd4\u8f83\uff0c\u6216\u8005\u5c06\u8d1f\u503c\u7684\u610f\u4e49\u7ed3\u5408\u4e0a\u4e0b\u6587\u8fdb\u884c\u89e3\u91ca\u3002\u5728\u56fe\u8868\u4e2d\uff0c\u786e\u4fdd\u8d1f\u503c\u90e8\u5206\u7684\u989c\u8272\u6216\u6837\u5f0f\u4e0e\u6b63\u503c\u6709\u6240\u533a\u5206\uff0c\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u663e\u793a\u8d1f\u503c\u56fe\u5f62\u5728Python\u4e2d\u5f88\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u4f7f\u7528\u5408\u9002\u7684\u5e93\u3001\u786e\u4fdd\u6570\u636e\u51c6\u5907\u6b63\u786e\u3001\u8bbe\u7f6e\u5408\u9002\u7684\u5750\u6807\u8f74\u8303\u56f4 […]","protected":false},"author":3,"featured_media":953051,"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\/953048"}],"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=953048"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/953048\/revisions"}],"predecessor-version":[{"id":953056,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/953048\/revisions\/953056"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/953051"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=953048"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=953048"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=953048"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}