{"id":1080655,"date":"2025-01-08T12:32:11","date_gmt":"2025-01-08T04:32:11","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1080655.html"},"modified":"2025-01-08T12:32:14","modified_gmt":"2025-01-08T04:32:14","slug":"python%e9%a5%bc%e5%9b%be%e9%87%8c%e9%9d%a2%e5%a6%82%e4%bd%95%e6%98%be%e7%a4%ba%e5%90%8d%e7%a7%b0-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1080655.html","title":{"rendered":"Python\u997c\u56fe\u91cc\u9762\u5982\u4f55\u663e\u793a\u540d\u79f0"},"content":{"rendered":"

\"Python\u997c\u56fe\u91cc\u9762\u5982\u4f55\u663e\u793a\u540d\u79f0\"<\/p>\n

\u8981\u5728Python\u7684\u997c\u56fe\u4e2d\u663e\u793a\u540d\u79f0\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3002\u5173\u952e\u6b65\u9aa4\u5305\u62ec\u5b9a\u4e49\u6807\u7b7e\u3001\u5c06\u6807\u7b7e\u4f20\u9012\u7ed9 pie()<\/code> \u51fd\u6570\u3001\u5e76\u4f7f\u7528 autopct<\/code> \u53c2\u6570\u5728\u56fe\u4e2d\u663e\u793a\u767e\u5206\u6bd4\u3002\u786e\u4fdd\u6570\u636e\u548c\u6807\u7b7e\u5339\u914d\uff0c\u9009\u62e9\u5408\u9002\u7684\u663e\u793a\u683c\u5f0f\uff0c\u8c03\u6574\u56fe\u5f62\u7f8e\u89c2\u5ea6<\/strong>\u3002\u5728\u4e0b\u9762\u7684\u8be6\u7ec6\u63cf\u8ff0\u4e2d\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\u5b9e\u73b0\u7cbe\u7f8e\u4e14\u529f\u80fd\u5b8c\u5907\u7684\u997c\u56fe\u3002<\/p>\n<\/p>\n

\u4e00\u3001Matplotlib\u5e93\u6982\u8ff0<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\uff0c\u5177\u6709\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\u3002\u901a\u8fc7Matplotlib\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u548c\u997c\u56fe\u7b49\u3002\u997c\u56fe\u7279\u522b\u9002\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u6bd4\u4f8b\u5173\u7cfb\uff0c\u4f8b\u5982\u5e02\u573a\u4efd\u989d\u3001\u9884\u7b97\u5206\u914d\u7b49\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u5b89\u88c5Matplotlib<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u5982\u679c\u5c1a\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
\u4e09\u3001\u521b\u5efa\u57fa\u672c\u7684\u997c\u56fe<\/h3>\n<\/p>\n
\u4e3a\u4e86\u521b\u5efa\u4e00\u4e2a\u57fa\u672c\u7684\u997c\u56fe\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u4e00\u4e9b\u793a\u4f8b\u6570\u636e\uff0c\u5e76\u4f7f\u7528 plt.pie()<\/code> \u51fd\u6570\u7ed8\u5236\u997c\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
\u7ed8\u5236\u997c\u56fe<\/strong><\/h2>\n
plt.pie(sizes, labels=labels)<\/p>\n
plt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0csizes<\/code> \u5217\u8868\u8868\u793a\u6bcf\u4e2a\u90e8\u5206\u7684\u5927\u5c0f\uff0clabels<\/code> \u5217\u8868\u8868\u793a\u6bcf\u4e2a\u90e8\u5206\u7684\u6807\u7b7e\u3002plt.axis('equal')<\/code> \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684\uff0c\u800c\u4e0d\u662f\u692d\u5706\u5f62\u7684\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5728\u997c\u56fe\u4e2d\u663e\u793a\u767e\u5206\u6bd4<\/h3>\n<\/p>\n
\u4e3a\u4e86\u5728\u997c\u56fe\u4e2d\u663e\u793a\u6bcf\u4e2a\u90e8\u5206\u7684\u767e\u5206\u6bd4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 autopct<\/code> \u53c2\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u663e\u793a\u767e\u5206\u6bd4<\/strong><\/h2>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%')<\/p>\n
plt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cautopct='%1.1f%%'<\/code> \u53c2\u6570\u5c06\u767e\u5206\u6bd4\u663e\u793a\u4e3a1\u4f4d\u5c0f\u6570\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u7a81\u51fa\u663e\u793a\u67d0\u4e00\u90e8\u5206<\/h3>\n<\/p>\n
\u5982\u679c\u6211\u4eec\u5e0c\u671b\u7a81\u51fa\u663e\u793a\u67d0\u4e00\u90e8\u5206\uff08\u4f8b\u5982\uff0c\u5c06\u67d0\u4e00\u90e8\u5206\u4ece\u997c\u56fe\u4e2d\u5206\u79bb\u51fa\u6765\uff09\uff0c\u53ef\u4ee5\u4f7f\u7528 explode<\/code> \u53c2\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
explode = (0, 0.1, 0, 0)  # \u4ec5\u5c06\u7b2c\u4e8c\u90e8\u5206\uff08B\uff09\u5206\u79bb\u51fa\u6765<\/p>\n
\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u663e\u793a\u767e\u5206\u6bd4<\/strong><\/h2>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%', explode=explode)<\/p>\n
plt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cexplode<\/code> \u53c2\u6570\u662f\u4e00\u4e2a\u4e0e\u6570\u636e\u5927\u5c0f\u76f8\u540c\u7684\u5143\u7ec4\uff0c\u6307\u5b9a\u6bcf\u4e2a\u90e8\u5206\u7684\u504f\u79fb\u8ddd\u79bb\u3002<\/p>\n<\/p>\n
\u516d\u3001\u81ea\u5b9a\u4e49\u997c\u56fe\u7684\u989c\u8272\u548c\u9634\u5f71<\/h3>\n<\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7 colors<\/code> \u53c2\u6570\u81ea\u5b9a\u4e49\u997c\u56fe\u7684\u989c\u8272\uff0c\u5e76\u901a\u8fc7 shadow<\/code> \u53c2\u6570\u6dfb\u52a0\u9634\u5f71\u6548\u679c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']<\/p>\n
\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u663e\u793a\u767e\u5206\u6bd4<\/strong><\/h2>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%', colors=colors, shadow=True)<\/p>\n
plt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0ccolors<\/code> \u53c2\u6570\u662f\u4e00\u4e2a\u989c\u8272\u5217\u8868\uff0c\u7528\u4e8e\u81ea\u5b9a\u4e49\u6bcf\u4e2a\u90e8\u5206\u7684\u989c\u8272\u3002shadow=True<\/code> \u53c2\u6570\u6dfb\u52a0\u4e86\u9634\u5f71\u6548\u679c\uff0c\u4f7f\u997c\u56fe\u66f4\u52a0\u7f8e\u89c2\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u8bbe\u7f6e\u6807\u7b7e\u5b57\u4f53\u5927\u5c0f\u548c\u6837\u5f0f<\/h3>\n<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u997c\u56fe\u7684\u53ef\u8bfb\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u8bbe\u7f6e\u6807\u7b7e\u7684\u5b57\u4f53\u5927\u5c0f\u548c\u6837\u5f0f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u663e\u793a\u767e\u5206\u6bd4<\/strong><\/h2>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%', textprops={'fontsize': 14, 'fontweight': 'bold'})<\/p>\n
plt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0ctextprops={'fontsize': 14, 'fontweight': 'bold'}<\/code> \u53c2\u6570\u7528\u4e8e\u8bbe\u7f6e\u6807\u7b7e\u7684\u5b57\u4f53\u5927\u5c0f\u548c\u6837\u5f0f\u3002<\/p>\n<\/p>\n
\u516b\u3001\u6dfb\u52a0\u56fe\u4f8b<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6dfb\u52a0\u56fe\u4f8b\u53ef\u4ee5\u66f4\u597d\u5730\u89e3\u91ca\u997c\u56fe\u7684\u5185\u5bb9\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 plt.legend()<\/code> \u51fd\u6570\u6dfb\u52a0\u56fe\u4f8b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u663e\u793a\u767e\u5206\u6bd4<\/strong><\/h2>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%')<\/p>\n
plt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684<\/p>\n
plt.legend(loc='best')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.legend(loc='best')<\/code> \u51fd\u6570\u7528\u4e8e\u6dfb\u52a0\u56fe\u4f8b\uff0c\u5e76\u5c06\u5176\u653e\u7f6e\u5728\u6700\u4f73\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u4fdd\u5b58\u997c\u56fe<\/h3>\n<\/p>\n
\u5b8c\u6210\u997c\u56fe\u7ed8\u5236\u540e\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5c06\u5176\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6\u3002\u53ef\u4ee5\u4f7f\u7528 plt.savefig()<\/code> \u51fd\u6570\u4fdd\u5b58\u56fe\u50cf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u663e\u793a\u767e\u5206\u6bd4<\/strong><\/h2>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%')<\/p>\n
plt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684<\/p>\n
plt.savefig('pie_chart.png')  # \u4fdd\u5b58\u4e3aPNG\u6587\u4ef6<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.savefig('pie_chart.png')<\/code> \u51fd\u6570\u5c06\u997c\u56fe\u4fdd\u5b58\u4e3aPNG\u6587\u4ef6\u3002<\/p>\n<\/p>\n
\u5341\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u4e86\u89e3\u4e86\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u997c\u56fe\uff0c\u5e76\u5728\u997c\u56fe\u4e2d\u663e\u793a\u540d\u79f0\u548c\u767e\u5206\u6bd4\u3002\u6211\u4eec\u8fd8\u4ecb\u7ecd\u4e86\u5982\u4f55\u81ea\u5b9a\u4e49\u997c\u56fe\u7684\u989c\u8272\u3001\u6dfb\u52a0\u9634\u5f71\u3001\u8bbe\u7f6e\u6807\u7b7e\u5b57\u4f53\u5927\u5c0f\u548c\u6837\u5f0f\u3001\u6dfb\u52a0\u56fe\u4f8b\u4ee5\u53ca\u4fdd\u5b58\u997c\u56fe\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u638c\u63e1Python\u6570\u636e\u53ef\u89c6\u5316\u6280\u672f\uff0c\u5e76\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u5e94\u7528\u6240\u5b66\u77e5\u8bc6\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u997c\u56fe\u4e2d\u6dfb\u52a0\u6807\u7b7e\uff1f<\/strong>
\u5728Python\u4e2d\u7ed8\u5236\u997c\u56fe\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u4e2d\u7684plt.pie()<\/code>\u51fd\u6570\u7684labels<\/code>\u53c2\u6570\u6765\u6dfb\u52a0\u540d\u79f0\u3002\u901a\u8fc7\u5c06\u4e00\u4e2a\u5305\u542b\u6807\u7b7e\u7684\u5217\u8868\u4f20\u9012\u7ed9\u8fd9\u4e2a\u53c2\u6570\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5728\u997c\u56fe\u4e2d\u663e\u793a\u6bcf\u4e2a\u90e8\u5206\u7684\u540d\u79f0\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
import matplotlib.pyplot as plt\n\nsizes = [15, 30, 45, 10]\nlabels = ['\u82f9\u679c', '\u9999\u8549', '\u6a59\u5b50', '\u8461\u8404']\nplt.pie(sizes, labels=labels)\nplt.axis('equal')  # \u786e\u4fdd\u997c\u56fe\u662f\u5706\u5f62\u7684\nplt.show()\n<\/code><\/pre>\n
\u53ef\u4ee5\u5728\u997c\u56fe\u4e0a\u663e\u793a\u767e\u5206\u6bd4\u5417\uff1f<\/strong>
\u662f\u7684\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eautopct<\/code>\u53c2\u6570\u6765\u5728\u997c\u56fe\u4e0a\u663e\u793a\u767e\u5206\u6bd4\u3002\u5728plt.pie()<\/code>\u51fd\u6570\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f20\u5165\u4e00\u4e2a\u683c\u5f0f\u5b57\u7b26\u4e32\uff0c\u6bd4\u5982'%.1f%%'<\/code>\uff0c\u8fd9\u4f1a\u5728\u6bcf\u4e2a\u6247\u533a\u4e0a\u663e\u793a\u4e00\u4f4d\u5c0f\u6570\u7684\u767e\u5206\u6bd4\u3002\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n
plt.pie(sizes, labels=labels, autopct='%.1f%%')\n<\/code><\/pre>\n
\u5982\u4f55\u81ea\u5b9a\u4e49\u997c\u56fe\u7684\u989c\u8272\u548c\u6837\u5f0f\uff1f<\/strong>
\u5728Python\u7684Matplotlib\u5e93\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7colors<\/code>\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u997c\u56fe\u7684\u989c\u8272\u3002\u60a8\u53ef\u4ee5\u63d0\u4f9b\u4e00\u4e2a\u989c\u8272\u5217\u8868\uff0c\u4e0e\u6bcf\u4e2a\u6247\u533a\u5bf9\u5e94\u3002\u4f8b\u5982\uff1a<\/p>\n
colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']\nplt.pie(sizes, labels=labels, colors=colors)\n<\/code><\/pre>\n
\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6estartangle<\/code>\u53c2\u6570\u6765\u65cb\u8f6c\u997c\u56fe\uff0c\u6216\u8005\u4f7f\u7528explode<\/code>\u53c2\u6570\u6765\u7a81\u51fa\u663e\u793a\u7279\u5b9a\u90e8\u5206\uff0c\u4f7f\u5176\u66f4\u5177\u89c6\u89c9\u5438\u5f15\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u7684\u997c\u56fe\u4e2d\u663e\u793a\u540d\u79f0\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u3002\u5173\u952e\u6b65\u9aa4\u5305\u62ec\u5b9a\u4e49\u6807\u7b7e\u3001\u5c06\u6807\u7b7e\u4f20\u9012\u7ed9 pie( […]","protected":false},"author":3,"featured_media":1080661,"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\/1080655"}],"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=1080655"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1080655\/revisions"}],"predecessor-version":[{"id":1080663,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1080655\/revisions\/1080663"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1080661"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1080655"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1080655"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1080655"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}