{"id":1053563,"date":"2024-12-31T14:35:01","date_gmt":"2024-12-31T06:35:01","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1053563.html"},"modified":"2024-12-31T14:35:04","modified_gmt":"2024-12-31T06:35:04","slug":"python%e5%a6%82%e4%bd%95%e8%b0%83%e8%8a%82hist%e7%9a%84%e5%9d%90%e6%a0%87%e5%b0%ba%e5%ba%a6","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1053563.html","title":{"rendered":"python\u5982\u4f55\u8c03\u8282hist\u7684\u5750\u6807\u5c3a\u5ea6"},"content":{"rendered":"

\"python\u5982\u4f55\u8c03\u8282hist\u7684\u5750\u6807\u5c3a\u5ea6\"<\/p>\n

Python\u8c03\u8282hist\u7684\u5750\u6807\u5c3a\u5ea6<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u76f4\u65b9\u56fe\uff0c\u5e76\u8c03\u8282\u5176\u5750\u6807\u5c3a\u5ea6\u3002\u4f7f\u7528plt.xlim()<\/code>\u548cplt.ylim()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u8303\u56f4\u3001\u901a\u8fc7plt.xticks()<\/code>\u548cplt.yticks()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u8f74\u523b\u5ea6<\/strong>\u3002\u4e0b\u9762\u662f\u8be6\u7ec6\u89e3\u91ca\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u51fd\u6570\u6765\u8c03\u8282\u76f4\u65b9\u56fe\u7684\u5750\u6807\u5c3a\u5ea6\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u76f4\u65b9\u56fe\u5e76\u8c03\u8282\u5750\u6807\u5c3a\u5ea6<\/h3>\n<\/p>\n

Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u76f4\u65b9\u56fe\u5e76\u8c03\u8282\u5176\u5750\u6807\u5c3a\u5ea6\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u5bfc\u5165Matplotlib\u5e93\u548cNumPy\u5e93\u3002NumPy\u5e93\u7528\u4e8e\u751f\u6210\u6570\u636e\uff0c\u800cMatplotlib\u5e93\u7528\u4e8e\u7ed8\u56fe\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u751f\u6210\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528NumPy\u751f\u6210\u4e00\u7ec4\u6570\u636e\uff0c\u4ee5\u4fbf\u5728\u76f4\u65b9\u56fe\u4e2d\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
data = np.random.randn(1000)  # \u751f\u62101000\u4e2a\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528plt.hist()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, edgecolor='k')  # \u7ed8\u5236\u76f4\u65b9\u56fe\uff0c\u8bbe\u7f6e30\u4e2a\u67f1\u5b50\uff0c\u67f1\u5b50\u8fb9\u7f18\u989c\u8272\u4e3a\u9ed1\u8272<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u8303\u56f4<\/h4>\n<\/p>\n
\u4f7f\u7528plt.xlim()<\/code>\u548cplt.ylim()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u8303\u56f4\u3002<\/p>\n<\/p>\n
plt.xlim(-5, 5)  # \u8bbe\u7f6eX\u8f74\u8303\u56f4\u4e3a-5\u52305<\/p>\n
plt.ylim(0, 100)  # \u8bbe\u7f6eY\u8f74\u8303\u56f4\u4e3a0\u5230100<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u8bbe\u7f6e\u8f74\u523b\u5ea6<\/h4>\n<\/p>\n
\u4f7f\u7528plt.xticks()<\/code>\u548cplt.yticks()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u8f74\u523b\u5ea6\u3002<\/p>\n<\/p>\n
plt.xticks(np.arange(-5, 6, 1))  # \u8bbe\u7f6eX\u8f74\u523b\u5ea6\u4e3a-5\u52305\uff0c\u95f4\u9694\u4e3a1<\/p>\n
plt.yticks(np.arange(0, 101, 10))  # \u8bbe\u7f6eY\u8f74\u523b\u5ea6\u4e3a0\u5230100\uff0c\u95f4\u9694\u4e3a10<\/p>\n
<\/code><\/pre>\n<\/p>\n
6\u3001\u663e\u793a\u56fe\u5f62<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u4f7f\u7528plt.show()<\/code>\u51fd\u6570\u6765\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8be6\u7ec6\u89e3\u91ca\u5404\u4e2a\u6b65\u9aa4<\/h3>\n<\/p>\n
1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
\u5728\u5bfc\u5165\u5e93\u65f6\uff0cimport matplotlib.pyplot as plt<\/code>\u662f\u6807\u51c6\u7684\u5bfc\u5165\u65b9\u5f0f\uff0c\u5c06Matplotlib\u7684pyplot\u6a21\u5757\u5bfc\u5165\u4e3aplt\u3002import numpy as np<\/code>\u662f\u6807\u51c6\u7684\u5bfc\u5165NumPy\u5e93\u7684\u65b9\u5f0f\uff0c\u5c06NumPy\u5bfc\u5165\u4e3anp\u3002<\/p>\n<\/p>\n
2\u3001\u751f\u6210\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528np.random.randn(1000)<\/code>\u751f\u62101000\u4e2a\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u5c06\u7528\u4e8e\u7ed8\u5236\u76f4\u65b9\u56fe\u3002NumPy\u7684randn<\/code>\u51fd\u6570\u751f\u6210\u7b26\u5408\u6807\u51c6\u6b63\u6001\u5206\u5e03\u7684\u6837\u672c\u6570\u636e\u3002<\/p>\n<\/p>\n
3\u3001\u7ed8\u5236\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
plt.hist(data, bins=30, edgecolor='k')<\/code>\u7528\u4e8e\u7ed8\u5236\u76f4\u65b9\u56fe\u3002data<\/code>\u662f\u8f93\u5165\u6570\u636e\uff0cbins=30<\/code>\u8868\u793a\u5c06\u6570\u636e\u5206\u621030\u4e2a\u67f1\u5b50\uff0cedgecolor='k'<\/code>\u8bbe\u7f6e\u67f1\u5b50\u7684\u8fb9\u7f18\u989c\u8272\u4e3a\u9ed1\u8272\u3002<\/p>\n<\/p>\n
4\u3001\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u8303\u56f4<\/h4>\n<\/p>\n
\u4f7f\u7528plt.xlim(-5, 5)<\/code>\u548cplt.ylim(0, 100)<\/code>\u6765\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u8303\u56f4\u3002plt.xlim(-5, 5)<\/code>\u5c06X\u8f74\u7684\u8303\u56f4\u8bbe\u7f6e\u4e3a-5\u52305\uff0cplt.ylim(0, 100)<\/code>\u5c06Y\u8f74\u7684\u8303\u56f4\u8bbe\u7f6e\u4e3a0\u5230100\u3002<\/p>\n<\/p>\n
5\u3001\u8bbe\u7f6e\u8f74\u523b\u5ea6<\/h4>\n<\/p>\n
\u4f7f\u7528plt.xticks(np.arange(-5, 6, 1))<\/code>\u548cplt.yticks(np.arange(0, 101, 10))<\/code>\u6765\u8bbe\u7f6e\u8f74\u523b\u5ea6\u3002plt.xticks(np.arange(-5, 6, 1))<\/code>\u5c06X\u8f74\u7684\u523b\u5ea6\u8bbe\u7f6e\u4e3a-5\u52305\uff0c\u95f4\u9694\u4e3a1\u3002plt.yticks(np.arange(0, 101, 10))<\/code>\u5c06Y\u8f74\u7684\u523b\u5ea6\u8bbe\u7f6e\u4e3a0\u5230100\uff0c\u95f4\u9694\u4e3a10\u3002<\/p>\n<\/p>\n
6\u3001\u663e\u793a\u56fe\u5f62<\/h4>\n<\/p>\n
\u4f7f\u7528plt.show()<\/code>\u6765\u663e\u793a\u56fe\u5f62\u3002\u8fd9\u4e2a\u51fd\u6570\u5c06\u5f53\u524d\u7ed8\u56fe\u7a97\u53e3\u4e2d\u7684\u6240\u6709\u7ed8\u56fe\u5bf9\u8c61\u663e\u793a\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u66f4\u591a\u8c03\u6574\u76f4\u65b9\u56fe\u7684\u6280\u5de7<\/h3>\n<\/p>\n
\u9664\u4e86\u8bbe\u7f6e\u5750\u6807\u5c3a\u5ea6\u4e4b\u5916\uff0cMatplotlib\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u5176\u4ed6\u529f\u80fd\u6765\u8c03\u6574\u76f4\u65b9\u56fe\u7684\u5916\u89c2\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u8c03\u6574\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u8bbe\u7f6e\u6807\u9898\u548c\u6807\u7b7e<\/h4>\n<\/p>\n
\u4f7f\u7528plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u56fe\u5f62\u7684\u6807\u9898\u548c\u8f74\u6807\u7b7e\u3002<\/p>\n<\/p>\n
plt.title('Histogram of Normally Distributed Data')  # \u8bbe\u7f6e\u6807\u9898<\/p>\n
plt.xlabel('Value')  # \u8bbe\u7f6eX\u8f74\u6807\u7b7e<\/p>\n
plt.ylabel('Frequency')  # \u8bbe\u7f6eY\u8f74\u6807\u7b7e<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bbe\u7f6e\u7f51\u683c\u7ebf<\/h4>\n<\/p>\n
\u4f7f\u7528plt.grid()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u7f51\u683c\u7ebf\u3002<\/p>\n<\/p>\n
plt.grid(True)  # \u663e\u793a\u7f51\u683c\u7ebf<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bbe\u7f6e\u989c\u8272\u548c\u900f\u660e\u5ea6<\/h4>\n<\/p>\n
\u5728\u7ed8\u5236\u76f4\u65b9\u56fe\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528color<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u67f1\u5b50\u7684\u989c\u8272\uff0c\u4f7f\u7528alpha<\/code>\u53c2\u6570\u6765\u8bbe\u7f6e\u900f\u660e\u5ea6\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, color='blue', alpha=0.7, edgecolor='k')  # \u8bbe\u7f6e\u989c\u8272\u4e3a\u84dd\u8272\uff0c\u900f\u660e\u5ea6\u4e3a0.7<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6dfb\u52a0\u56fe\u4f8b<\/h4>\n<\/p>\n
\u4f7f\u7528plt.legend()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, color='blue', alpha=0.7, edgecolor='k', label='Data')  # \u6dfb\u52a0\u6807\u7b7e<\/p>\n
plt.legend()  # \u663e\u793a\u56fe\u4f8b<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7efc\u5408\u5b9e\u4f8b<\/h3>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7efc\u5408\u5b9e\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u4e0a\u8ff0\u6240\u6709\u6280\u5de7\u6765\u7ed8\u5236\u548c\u8c03\u6574\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u6570\u636e<\/strong><\/h2>\n
data = np.random.randn(1000)<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.hist(data, bins=30, color='blue', alpha=0.7, edgecolor='k', label='Data')<\/p>\n
\u8bbe\u7f6e\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Histogram of Normally Distributed Data')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u8303\u56f4<\/strong><\/h2>\n
plt.xlim(-5, 5)<\/p>\n
plt.ylim(0, 100)<\/p>\n
\u8bbe\u7f6e\u8f74\u523b\u5ea6<\/strong><\/h2>\n
plt.xticks(np.arange(-5, 6, 1))<\/p>\n
plt.yticks(np.arange(0, 101, 10))<\/p>\n
\u8bbe\u7f6e\u7f51\u683c\u7ebf<\/strong><\/h2>\n
plt.grid(True)<\/p>\n
\u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
plt.legend()<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u7ed8\u5236\u76f4\u65b9\u56fe\uff0c\u5e76\u8c03\u8282\u5176\u5750\u6807\u5c3a\u5ea6\u3002\u5e0c\u671b\u8fd9\u4e9b\u6280\u5de7\u80fd\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8c03\u6574hist\u56fe\u7684\u5750\u6807\u8f74\u8303\u56f4\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u4e2d\u7684xlim()<\/code>\u548cylim()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u76f4\u65b9\u56fe\u7684\u5750\u6807\u8f74\u8303\u56f4\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7plt.xlim(min_value, max_value)<\/code>\u6765\u9650\u5236x\u8f74\u7684\u8303\u56f4\uff0cplt.ylim(min_value, max_value)<\/code>\u6765\u9650\u5236y\u8f74\u7684\u8303\u56f4\u3002\u8fd9\u4f7f\u5f97\u60a8\u53ef\u4ee5\u4e13\u6ce8\u4e8e\u6570\u636e\u7684\u7279\u5b9a\u90e8\u5206\uff0c\u589e\u5f3a\u56fe\u8868\u7684\u53ef\u8bfb\u6027\u3002<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u65b9\u6cd5\u6765\u6539\u53d8hist\u56fe\u7684\u5c3a\u5ea6\u7c7b\u578b\uff1f<\/strong>
\u5728Python\u7684matplotlib\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5750\u6807\u8f74\u7684scale\u5c5e\u6027\u6765\u6539\u53d8\u5c3a\u5ea6\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u4f7f\u7528plt.xscale('log')<\/code>\u5c06x\u8f74\u8bbe\u7f6e\u4e3a\u5bf9\u6570\u5c3a\u5ea6\uff0c\u9002\u5408\u5904\u7406\u5177\u6709\u5927\u8303\u56f4\u6570\u636e\u7684\u60c5\u51b5\u3002\u7c7b\u4f3c\u5730\uff0c\u53ef\u4ee5\u4f7f\u7528plt.yscale('log')<\/code>\u8c03\u6574y\u8f74\u4e3a\u5bf9\u6570\u5c3a\u5ea6\u3002\u8fd9\u4e9b\u8bbe\u7f6e\u53ef\u4ee5\u4f7f\u6570\u636e\u7684\u5206\u5e03\u66f4\u52a0\u660e\u786e\u3002<\/p>\n
\u5982\u4f55\u81ea\u5b9a\u4e49hist\u56fe\u7684\u6807\u7b7e\u548c\u6807\u9898\uff1f<\/strong>
\u81ea\u5b9a\u4e49\u76f4\u65b9\u56fe\u7684\u6807\u7b7e\u548c\u6807\u9898\u662f\u63d0\u5347\u53ef\u8bfb\u6027\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528plt.xlabel('\u60a8\u7684X\u8f74\u6807\u7b7e')<\/code>\u548cplt.ylabel('\u60a8\u7684Y\u8f74\u6807\u7b7e')<\/code>\u6765\u8bbe\u7f6e\u5750\u6807\u8f74\u7684\u6807\u7b7e\u3002\u540c\u65f6\uff0c\u4f7f\u7528plt.title('\u60a8\u7684\u6807\u9898')<\/code>\u6dfb\u52a0\u76f4\u65b9\u56fe\u7684\u6807\u9898\u3002\u8fd9\u4e9b\u5143\u7d20\u53ef\u4ee5\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u56fe\u8868\u7684\u5185\u5bb9\u548c\u80cc\u666f\u3002<\/p>\n
\u662f\u5426\u53ef\u4ee5\u901a\u8fc7Python\u4e2d\u7684hist\u529f\u80fd\u7ed8\u5236\u591a\u7ec4\u6570\u636e\u7684\u76f4\u65b9\u56fe\uff1f<\/strong>
\u5f53\u7136\u53ef\u4ee5\u3002\u5229\u7528matplotlib\u7684plt.hist()<\/code>\u51fd\u6570\uff0c\u60a8\u53ef\u4ee5\u5c06\u591a\u7ec4\u6570\u636e\u4f20\u9012\u7ed9\u8be5\u51fd\u6570\uff0c\u5e76\u901a\u8fc7alpha<\/code>\u53c2\u6570\u8bbe\u7f6e\u900f\u660e\u5ea6\uff0c\u4ee5\u4fbf\u5728\u540c\u4e00\u5750\u6807\u7cfb\u4e2d\u7ed8\u5236\u4e0d\u540c\u7684\u6570\u636e\u96c6\u3002\u901a\u8fc7\u8c03\u6574\u989c\u8272\u548c\u900f\u660e\u5ea6\uff0c\u60a8\u53ef\u4ee5\u6e05\u6670\u5730\u6bd4\u8f83\u4e0d\u540c\u6570\u636e\u96c6\u4e4b\u95f4\u7684\u5206\u5e03\u5dee\u5f02\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8c03\u8282hist\u7684\u5750\u6807\u5c3a\u5ea6 \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u76f4\u65b9\u56fe\uff0c\u5e76\u8c03\u8282\u5176 […]","protected":false},"author":3,"featured_media":1053573,"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\/1053563"}],"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=1053563"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1053563\/revisions"}],"predecessor-version":[{"id":1053577,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1053563\/revisions\/1053577"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1053573"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1053563"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1053563"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1053563"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}