{"id":1186670,"date":"2025-01-15T19:55:31","date_gmt":"2025-01-15T11:55:31","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1186670.html"},"modified":"2025-01-15T19:55:35","modified_gmt":"2025-01-15T11:55:35","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e6%a0%bc%e5%ad%90%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1186670.html","title":{"rendered":"\u5982\u4f55\u7528python\u753b\u683c\u5b50\u56fe"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753b\u683c\u5b50\u56fe\"<\/p>\n

\u7528python\u753b\u683c\u5b50\u56fe\u7684\u65b9\u6cd5\u4e3b\u8981\u6709<\/strong>\uff1a\u4f7f\u7528matplotlib\u5e93\u3001\u4f7f\u7528seaborn\u5e93\u3001\u4f7f\u7528pandas\u5e93\u3002<\/strong> \u5728\u8fd9\u4e09\u79cd\u65b9\u6cd5\u4e2d\uff0cmatplotlib<\/strong> \u662f\u6700\u5e38\u7528\u4e14\u529f\u80fd\u6700\u5f3a\u5927\u7684\u5e93\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528 matplotlib<\/strong> \u5e93\u6765\u7ed8\u5236\u683c\u5b50\u56fe\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u5e93<\/h3>\n<\/p>\n

1\u3001\u5b89\u88c5\u548c\u5bfc\u5165MATPLOTLIB<\/h4>\n<\/p>\n

MATPLOTLIB\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86MATPLOTLIB\u5e93\u3002\u5982\u679c\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
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165MATPLOTLIB\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u6570\u636e<\/h4>\n<\/p>\n
\u7ed8\u5236\u683c\u5b50\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e9b\u793a\u4f8b\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u8981\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u683c\u5b50\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u751f\u6210\u6570\u636e\uff1a<\/p>\n<\/p>\n
data = np.random.rand(10, 10)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u683c\u5b50\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528MATPLOTLIB\u4e2d\u7684 imshow<\/code> \u51fd\u6570\u6765\u7ed8\u5236\u683c\u5b50\u56fe\u3002 imshow<\/code> \u51fd\u6570\u53ef\u4ee5\u663e\u793a\u4e8c\u7ef4\u6570\u636e\u6570\u7ec4\uff0c\u5e76\u5c06\u5176\u4f5c\u4e3a\u56fe\u50cf\u663e\u793a\u3002<\/p>\n<\/p>\n
plt.imshow(data, cmap='viridis', interpolation='none')<\/p>\n
plt.colorbar()  # \u6dfb\u52a0\u989c\u8272\u6761<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u91cc\uff0ccmap<\/code> \u53c2\u6570\u6307\u5b9a\u4e86\u989c\u8272\u6620\u5c04\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u989c\u8272\u6620\u5c04\u65b9\u6848\uff0c\u6bd4\u5982 viridis<\/code>\u3001plasma<\/code>\u3001inferno<\/code> \u7b49\u7b49\u3002 interpolation<\/code> \u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u63d2\u503c\u65b9\u6cd5\uff0cnone<\/code> \u8868\u793a\u4e0d\u4f7f\u7528\u63d2\u503c\u3002<\/p>\n<\/p>\n
4\u3001\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/h4>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u52a0\u6e05\u6670\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff1a<\/p>\n<\/p>\n
plt.imshow(data, cmap='viridis', interpolation='none')<\/p>\n
plt.colorbar()<\/p>\n
plt.title('Grid Plot Example')<\/p>\n
plt.xlabel('X-axis Label')<\/p>\n
plt.ylabel('Y-axis Label')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u56fe\u8868\u4e2d\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n
\u4e8c\u3001SEABORN\u5e93<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165SEABORN<\/h4>\n<\/p>\n
SEABORN\u662f\u57fa\u4e8eMATPLOTLIB\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u914d\u8272\u65b9\u6848\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5SEABORN\u5e93\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165SEABORN\u5e93\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u6570\u636e<\/h4>\n<\/p>\n
\u540c\u6837\u5730\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e9b\u793a\u4f8b\u6570\u636e\uff1a<\/p>\n<\/p>\n
data = np.random.rand(10, 10)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u683c\u5b50\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528SEABORN\u4e2d\u7684 heatmap<\/code> \u51fd\u6570\u6765\u7ed8\u5236\u683c\u5b50\u56fe\u3002 heatmap<\/code> \u51fd\u6570\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u9009\u9879\u6765\u5b9a\u5236\u56fe\u8868\uff1a<\/p>\n<\/p>\n
sns.heatmap(data, cmap='viridis', annot=True)<\/p>\n
plt.title('Heatmap Example with Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u91cc\uff0cannot<\/code> \u53c2\u6570\u7528\u4e8e\u5728\u6bcf\u4e2a\u683c\u5b50\u4e2d\u663e\u793a\u6570\u636e\u503c\u3002<\/p>\n<\/p>\n
\u4e09\u3001PANDAS\u5e93<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165PANDAS<\/h4>\n<\/p>\n
PANDAS\u662fPython\u4e2d\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u4e5f\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u683c\u5b50\u56fe\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5PANDAS\u5e93\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165PANDAS\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u6570\u636e<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528PANDAS DataFrame\u6765\u5b58\u50a8\u6570\u636e\uff1a<\/p>\n<\/p>\n
data = pd.DataFrame(np.random.rand(10, 10))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u683c\u5b50\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528PANDAS\u4e2d\u7684 plot<\/code> \u51fd\u6570\u6765\u7ed8\u5236\u683c\u5b50\u56fe\uff1a<\/p>\n<\/p>\n
data.plot(kind='heatmap', cmap='viridis')<\/p>\n
plt.title('Heatmap Example with Pandas')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7efc\u5408\u5e94\u7528<\/h3>\n<\/p>\n
1\u3001\u5b9a\u5236\u5316\u683c\u5b50\u56fe<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5bf9\u683c\u5b50\u56fe\u8fdb\u884c\u66f4\u591a\u7684\u5b9a\u5236\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5b9a\u5236\u5316\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
    \n
  • \u8c03\u6574\u989c\u8272\u6620\u5c04<\/strong>\uff1a\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u989c\u8272\u6620\u5c04\u65b9\u6848\u6765\u7a81\u51fa\u663e\u793a\u6570\u636e\u7684\u7279\u5f81\u3002<\/li>\n
  • \u6dfb\u52a0\u6ce8\u91ca<\/strong>\uff1a\u53ef\u4ee5\u5728\u6bcf\u4e2a\u683c\u5b50\u4e2d\u663e\u793a\u6570\u636e\u503c\uff0c\u4fbf\u4e8e\u89c2\u5bdf\u5177\u4f53\u6570\u503c\u3002<\/li>\n
  • \u8c03\u6574\u523b\u5ea6<\/strong>\uff1a\u53ef\u4ee5\u8c03\u6574X\u8f74\u548cY\u8f74\u7684\u523b\u5ea6\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u663e\u793a\u6570\u636e\u3002<\/li>\n<\/ul>\n
    \u4ee5\u4e0b\u662f\u4e00\u4e2a\u7efc\u5408\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    import numpy as np<\/p>\n
    \u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
    data = np.random.rand(10, 10)<\/p>\n
    \u7ed8\u5236\u683c\u5b50\u56fe<\/strong><\/h2>\n
    plt.imshow(data, cmap='coolwarm', interpolation='nearest')<\/p>\n
    \u6dfb\u52a0\u989c\u8272\u6761<\/strong><\/h2>\n
    plt.colorbar()<\/p>\n
    \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
    plt.title('Customized Grid Plot')<\/p>\n
    plt.xlabel('X-axis')<\/p>\n
    plt.ylabel('Y-axis')<\/p>\n
    \u6dfb\u52a0\u6ce8\u91ca<\/strong><\/h2>\n
    for i in range(data.shape[0]):<\/p>\n
        for j in range(data.shape[1]):<\/p>\n
            plt.text(j, i, f'{data[i, j]:.2f}', ha='center', va='center', color='black')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86 coolwarm<\/code> \u989c\u8272\u6620\u5c04\u65b9\u6848\uff0c\u5e76\u5728\u6bcf\u4e2a\u683c\u5b50\u4e2d\u663e\u793a\u6570\u636e\u503c\u3002<\/p>\n<\/p>\n
    2\u3001\u5e94\u7528\u573a\u666f<\/h4>\n<\/p>\n
    \u683c\u5b50\u56fe\u5728\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
      \n
    • \u70ed\u529b\u56fe<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u4e8c\u7ef4\u6570\u636e\u5206\u5e03\u7684\u70ed\u529b\u56fe\uff0c\u53ef\u4ee5\u7528\u6765\u5206\u6790\u6570\u636e\u7684\u805a\u96c6\u548c\u5206\u5e03\u60c5\u51b5\u3002<\/li>\n
    • \u76f8\u5173\u77e9\u9635<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u53d8\u91cf\u4e4b\u95f4\u76f8\u5173\u6027\u7684\u76f8\u5173\u77e9\u9635\uff0c\u53ef\u4ee5\u7528\u6765\u5206\u6790\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/li>\n
    • \u6df7\u6dc6\u77e9\u9635<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u5206\u7c7b\u6a21\u578b\u9884\u6d4b\u7ed3\u679c\u7684\u6df7\u6dc6\u77e9\u9635\uff0c\u53ef\u4ee5\u7528\u6765\u8bc4\u4f30\u5206\u7c7b\u6a21\u578b\u7684\u6027\u80fd\u3002<\/li>\n<\/ul>\n
      \u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
      \u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528MATPLOTLIB\u3001SEABORN\u548cPANDAS\u5e93\u6765\u7ed8\u5236\u683c\u5b50\u56fe\uff0c\u5e76\u4e86\u89e3\u4e86\u5982\u4f55\u8fdb\u884c\u5b9a\u5236\u5316\u64cd\u4f5c\u3002\u683c\u5b50\u56fe\u5728\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u7279\u5f81\u548c\u5206\u5e03\u3002<\/p>\n<\/p>\n
      \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u5e93\uff0c\u5e76\u8fdb\u884c\u76f8\u5e94\u7684\u5b9a\u5236\u5316\u64cd\u4f5c\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u5c55\u793a\u548c\u5206\u6790\u6570\u636e\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff0c\u80fd\u591f\u5728\u60a8\u7684\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u5de5\u4f5c\u4e2d\u63d0\u4f9b\u4e00\u4e9b\u53c2\u8003\u548c\u6307\u5bfc\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u7528Python\u7ed8\u5236\u683c\u5b50\u56fe\u7684\u57fa\u672c\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
      \u8981\u7ed8\u5236\u683c\u5b50\u56fe\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\uff0c\u8fd9\u662f\u4e00\u4e2a\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u3002\u901a\u8fc7\u4f7f\u7528matplotlib.pyplot<\/code>\u6a21\u5757\u4e2d\u7684plot()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u51fa\u6240\u9700\u7684\u683c\u5b50\u56fe\u3002\u9700\u8981\u8bbe\u7f6e\u5750\u6807\u8f74\u7684\u523b\u5ea6\u548c\u7f51\u683c\u7ebf\uff0c\u8c03\u7528grid()<\/code>\u51fd\u6570\u6765\u663e\u793a\u683c\u5b50\u6548\u679c\u3002\u786e\u4fdd\u5728\u7ed8\u5236\u56fe\u5f62\u4e4b\u524d\uff0c\u51c6\u5907\u597d\u6570\u636e\u548c\u76f8\u5173\u7684\u5750\u6807\u8f74\u6807\u7b7e\u3002<\/p>\n
      \u5728\u4f7f\u7528Python\u7ed8\u5236\u683c\u5b50\u56fe\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u54ea\u4e9b\u6837\u5f0f\u6216\u989c\u8272\uff1f<\/strong>
      Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6837\u5f0f\u548c\u989c\u8272\u9009\u9879\uff0c\u53ef\u4ee5\u901a\u8fc7plt.style.use()<\/code>\u51fd\u6570\u9009\u62e9\u9884\u8bbe\u7684\u6837\u5f0f\uff0c\u5982'seaborn'<\/code>\u6216'ggplot'<\/code>\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570color<\/code>\u548clinestyle<\/code>\u81ea\u5b9a\u4e49\u7ebf\u6761\u7684\u989c\u8272\u548c\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u4f7f\u7528color='red'<\/code>\u548clinestyle='--'<\/code>\u53ef\u4ee5\u4f7f\u7ebf\u6761\u53d8\u4e3a\u7ea2\u8272\u4e14\u4e3a\u865a\u7ebf\u3002\u8fd8\u53ef\u4ee5\u901a\u8fc7marker<\/code>\u53c2\u6570\u6dfb\u52a0\u6570\u636e\u70b9\u7684\u6807\u8bb0\u6837\u5f0f\u3002<\/p>\n
      \u5728\u7ed8\u5236\u683c\u5b50\u56fe\u65f6\uff0c\u5982\u4f55\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u4ee5\u589e\u5f3a\u53ef\u8bfb\u6027\uff1f<\/strong>
      \u4e3a\u4e86\u589e\u5f3a\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u4f7f\u7528plt.title()<\/code>\u6dfb\u52a0\u6807\u9898\uff0c\u901a\u8fc7plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u4e3ax\u8f74\u548cy\u8f74\u6dfb\u52a0\u6807\u7b7e\u3002\u8fd9\u4e9b\u4fe1\u606f\u53ef\u4ee5\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3\u56fe\u5f62\u6240\u8868\u793a\u7684\u6570\u636e\u3002\u6b64\u5916\uff0c\u8bbe\u7f6e\u5b57\u4f53\u5927\u5c0f\u548c\u6837\u5f0f\u4e5f\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570\u8fdb\u884c\u8c03\u6574\uff0c\u4ece\u800c\u63d0\u5347\u6574\u4f53\u89c6\u89c9\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528python\u753b\u683c\u5b50\u56fe\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\uff1a\u4f7f\u7528matplotlib\u5e93\u3001\u4f7f\u7528seaborn\u5e93\u3001\u4f7f\u7528pandas\u5e93\u3002 \u5728 […]","protected":false},"author":3,"featured_media":1186679,"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\/1186670"}],"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=1186670"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186670\/revisions"}],"predecessor-version":[{"id":1186681,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186670\/revisions\/1186681"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1186679"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1186670"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1186670"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1186670"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}