{"id":986146,"date":"2024-12-27T07:42:27","date_gmt":"2024-12-26T23:42:27","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/986146.html"},"modified":"2024-12-27T07:42:29","modified_gmt":"2024-12-26T23:42:29","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8%e7%9f%a9%e9%98%b5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/986146.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u4f7f\u7528\u77e9\u9635"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25062853\/77595fe6-ed6c-4060-9c83-463c3ce0d288.webp\" alt=\"python\u4e2d\u5982\u4f55\u4f7f\u7528\u77e9\u9635\" \/><\/p>\n<p><p> \u5728Python\u4e2d\uff0c\u4f7f\u7528\u77e9\u9635\u7684\u65b9\u5f0f\u6709\u5f88\u591a\u79cd\uff0c\u4e3b\u8981\u901a\u8fc7NumPy\u5e93\u6765\u8fdb\u884c\u5b9e\u73b0\u3002<strong>\u77e9\u9635\u5728Python\u4e2d\u4f7f\u7528\u7684\u5173\u952e\u662f\u9009\u62e9\u5408\u9002\u7684\u5e93\u3001\u4e86\u89e3\u77e9\u9635\u7684\u57fa\u672c\u64cd\u4f5c\u4ee5\u53ca\u7406\u89e3\u77e9\u9635\u8fd0\u7b97\u7684\u5e94\u7528<\/strong>\u3002NumPy\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61ndarray\uff0c\u5e76\u4e14\u652f\u6301\u591a\u79cd\u77e9\u9635\u8fd0\u7b97\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u77e9\u9635\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001NUMPY\u5e93\u7684\u5b89\u88c5\u4e0e\u5bfc\u5165<\/h3>\n<\/p>\n<p><h4>1. \u5b89\u88c5NumPy<\/h4>\n<\/p>\n<p><p>\u5728\u4f7f\u7528NumPy\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install numpy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u5bfc\u5165NumPy<\/h4>\n<\/p>\n<p><p>\u5728Python\u811a\u672c\u4e2d\u4f7f\u7528NumPy\uff0c\u9700\u8981\u5148\u5bfc\u5165\u5b83\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528<code>np<\/code>\u4f5c\u4e3aNumPy\u7684\u7b80\u79f0\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u77e9\u9635\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u521b\u5efa\u77e9\u9635<\/h3>\n<\/p>\n<p><h4>1. \u4f7f\u7528\u6570\u7ec4\u521b\u5efa\u77e9\u9635<\/h4>\n<\/p>\n<p><p>NumPy\u4e2d\u7684ndarray\u5bf9\u8c61\u662f\u4e00\u79cd\u591a\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u7528\u6765\u8868\u793a\u77e9\u9635\u3002\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u4e8c\u7ef4\u77e9\u9635\u53ef\u4ee5\u4f7f\u7528NumPy\u7684<code>array<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">matrix = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e0a\u8ff0\u4ee3\u7801\u521b\u5efa\u4e86\u4e00\u4e2a2&#215;3\u7684\u77e9\u9635\u3002<\/p>\n<\/p>\n<p><h4>2. \u4f7f\u7528zeros\u3001ones\u548ceye\u51fd\u6570<\/h4>\n<\/p>\n<p><p>NumPy\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u51fd\u6570\u6765\u5feb\u901f\u521b\u5efa\u7279\u5b9a\u7c7b\u578b\u7684\u77e9\u9635\uff1a<\/p>\n<\/p>\n<ul>\n<li>\n<p><strong>Zeros\u77e9\u9635<\/strong>\uff1a\u521b\u5efa\u5168\u96f6\u77e9\u9635<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">zeros_matrix = np.zeros((3, 3))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>Ones\u77e9\u9635<\/strong>\uff1a\u521b\u5efa\u5168\u4e00\u77e9\u9635<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">ones_matrix = np.ones((2, 2))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5355\u4f4d\u77e9\u9635<\/strong>\uff1a\u521b\u5efa\u5355\u4f4d\u77e9\u9635<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">identity_matrix = np.eye(3)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><h3>\u4e09\u3001\u77e9\u9635\u7684\u57fa\u672c\u64cd\u4f5c<\/h3>\n<\/p>\n<p><h4>1. \u77e9\u9635\u7684\u8bbf\u95ee\u4e0e\u5207\u7247<\/h4>\n<\/p>\n<p><p>NumPy\u77e9\u9635\u652f\u6301\u901a\u8fc7\u7d22\u5f15\u8bbf\u95ee\u548c\u5207\u7247\uff1a<\/p>\n<\/p>\n<ul>\n<li>\n<p><strong>\u8bbf\u95ee\u5143\u7d20<\/strong>\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">element = matrix[0, 1]  # \u8bbf\u95ee\u7b2c\u4e00\u884c\u7b2c\u4e8c\u5217\u7684\u5143\u7d20<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5207\u7247\u64cd\u4f5c<\/strong>\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">sub_matrix = matrix[0:2, 1:3]  # \u53d6\u7b2c\u4e00\u3001\u4e8c\u884c\uff0c\u7b2c\u4e8c\u3001\u4e09\u5217\u7684\u5b50\u77e9\u9635<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><h4>2. \u77e9\u9635\u7684\u5f62\u72b6\u4e0e\u53d8\u5f62<\/h4>\n<\/p>\n<ul>\n<li>\n<p><strong>\u83b7\u53d6\u77e9\u9635\u5f62\u72b6<\/strong>\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">shape = matrix.shape<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u53d8\u5f62\u77e9\u9635<\/strong>\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">reshaped_matrix = matrix.reshape((3, 2))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><h4>3. \u77e9\u9635\u7684\u57fa\u672c\u8fd0\u7b97<\/h4>\n<\/p>\n<p><p>NumPy\u652f\u6301\u5bf9\u77e9\u9635\u8fdb\u884c\u52a0\u51cf\u4e58\u9664\u7b49\u57fa\u672c\u8fd0\u7b97\uff1a<\/p>\n<\/p>\n<ul>\n<li>\n<p><strong>\u77e9\u9635\u52a0\u51cf\u6cd5<\/strong>\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">matrix1 = np.array([[1, 2], [3, 4]])<\/p>\n<p>matrix2 = np.array([[5, 6], [7, 8]])<\/p>\n<p>sum_matrix = matrix1 + matrix2<\/p>\n<p>diff_matrix = matrix1 - matrix2<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u77e9\u9635\u4e58\u6cd5<\/strong>\uff08\u70b9\u4e58\u4e0e\u77e9\u9635\u4e58\u6cd5\uff09\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">elementwise_product = matrix1 * matrix2  # \u5143\u7d20\u5bf9\u5e94\u76f8\u4e58<\/p>\n<p>matrix_product = np.dot(matrix1, matrix2)  # \u77e9\u9635\u4e58\u6cd5<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n<p><h3>\u56db\u3001\u77e9\u9635\u7684\u9ad8\u7ea7\u64cd\u4f5c<\/h3>\n<\/p>\n<p><h4>1. \u77e9\u9635\u7684\u8f6c\u7f6e<\/h4>\n<\/p>\n<p><p>\u8f6c\u7f6e\u64cd\u4f5c\u4ea4\u6362\u77e9\u9635\u7684\u884c\u548c\u5217\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">transposed_matrix = matrix.T<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u77e9\u9635\u7684\u9006<\/h4>\n<\/p>\n<p><p>\u8ba1\u7b97\u77e9\u9635\u7684\u9006\u9700\u8981\u4f7f\u7528NumPy\u7684\u7ebf\u6027\u4ee3\u6570\u6a21\u5757\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">inverse_matrix = np.linalg.inv(matrix1)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u53ea\u6709\u65b9\u9635\uff08\u884c\u6570\u7b49\u4e8e\u5217\u6570\uff09\u4e14\u53ef\u9006\u65f6\u624d\u80fd\u8ba1\u7b97\u9006\u77e9\u9635\u3002<\/p>\n<\/p>\n<p><h4>3. \u77e9\u9635\u7684\u884c\u5217\u5f0f<\/h4>\n<\/p>\n<p><p>\u884c\u5217\u5f0f\u662f\u4e00\u4e2a\u6807\u91cf\uff0c\u63cf\u8ff0\u4e86\u77e9\u9635\u7684\u67d0\u4e9b\u6027\u8d28\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">determinant = np.linalg.det(matrix1)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>4. \u7279\u5f81\u503c\u4e0e\u7279\u5f81\u5411\u91cf<\/h4>\n<\/p>\n<p><p>\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\u5728\u8bb8\u591a\u79d1\u5b66\u8ba1\u7b97\u4e2d\u90fd\u975e\u5e38\u91cd\u8981\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">eigenvalues, eigenvectors = np.linalg.eig(matrix1)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e94\u3001\u77e9\u9635\u7684\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n<p><h4>1. \u7ebf\u6027\u4ee3\u6570<\/h4>\n<\/p>\n<p><p>\u77e9\u9635\u662f\u7ebf\u6027\u4ee3\u6570\u7684\u6838\u5fc3\u5de5\u5177\uff0c\u5e7f\u6cdb\u7528\u4e8e\u89e3\u51b3\u7ebf\u6027\u65b9\u7a0b\u7ec4\u3001\u8ba1\u7b97\u53d8\u6362\u7b49\u95ee\u9898\u3002\u4f7f\u7528NumPy\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u8fd9\u4e9b\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><h4>2. \u6570\u636e\u5206\u6790\u4e0e<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a><\/h4>\n<\/p>\n<p><p>\u5728\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u77e9\u9635\u7528\u4e8e\u8868\u793a\u6570\u636e\u96c6\u3001\u8fdb\u884c\u6570\u636e\u53d8\u6362\u548c\u8bad\u7ec3\u6a21\u578b\u3002NumPy\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\uff08\u5982Pandas\u3001SciPy\u3001Scikit-learn\u7b49\uff09\u7ed3\u5408\uff0c\u53ef\u4ee5\u6784\u5efa\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\u3002<\/p>\n<\/p>\n<p><h4>3. \u56fe\u50cf\u5904\u7406<\/h4>\n<\/p>\n<p><p>\u56fe\u50cf\u53ef\u4ee5\u88ab\u89c6\u4e3a\u4e00\u4e2a\u77e9\u9635\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5143\u7d20\u8868\u793a\u50cf\u7d20\u503c\u3002\u4f7f\u7528NumPy\u53ef\u4ee5\u8fdb\u884c\u5404\u79cd\u56fe\u50cf\u53d8\u6362\u548c\u5904\u7406\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><h3>\u516d\u3001\u5176\u4ed6\u77e9\u9635\u5e93<\/h3>\n<\/p>\n<p><p>\u9664\u4e86NumPy\uff0cPython\u8fd8\u6709\u5176\u4ed6\u7684\u77e9\u9635\u5e93\uff0c\u5982SciPy\u548cPandas\uff0c\u5b83\u4eec\u63d0\u4f9b\u4e86\u989d\u5916\u7684\u529f\u80fd\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>SciPy<\/strong>\uff1a\u5728NumPy\u7684\u57fa\u7840\u4e0a\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u7ebf\u6027\u4ee3\u6570\u64cd\u4f5c\u3002<\/li>\n<li><strong>Pandas<\/strong>\uff1a\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u5206\u6790\uff0c\u63d0\u4f9b\u4e86DataFrame\u7ed3\u6784\uff0c\u53ef\u4ee5\u89c6\u4e3a\u589e\u5f3a\u7248\u7684\u77e9\u9635\u3002<\/li>\n<\/ul>\n<p><h3>\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u77e9\u9635\u7684\u4f7f\u7528\u4e3b\u8981\u4f9d\u8d56\u4e8eNumPy\u5e93\u3002\u901a\u8fc7NumPy\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u8fdb\u884c\u77e9\u9635\u7684\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u8fd0\u7b97\u3002\u6b64\u5916\uff0c\u7ed3\u5408\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0cPython\u4e3a\u6570\u636e\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u7b49\u9886\u57df\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u652f\u6301\u3002\u4e86\u89e3\u5e76\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\u548c\u6280\u672f\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u8ba1\u7b97\u80fd\u529b\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u4e00\u4e2a\u77e9\u9635\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efa\u77e9\u9635\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86NumPy\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7<code>pip install numpy<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u521b\u5efa\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u6700\u5e38\u89c1\u7684\u662f\u4f7f\u7528<code>numpy.array()<\/code>\u51fd\u6570\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a2&#215;3\u7684\u77e9\u9635\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\n\nmatrix = np.array([[1, 2, 3], [4, 5, 6]])\nprint(matrix)\n<\/code><\/pre>\n<p>\u8fd9\u6bb5\u4ee3\u7801\u5c06\u8f93\u51fa\u4e00\u4e2a\u5305\u542b\u4e24\u4e2a\u884c\u548c\u4e09\u4e2a\u5217\u7684\u77e9\u9635\u3002<\/p>\n<p><strong>Python\u4e2d\u5982\u4f55\u5bf9\u77e9\u9635\u8fdb\u884c\u57fa\u672c\u8fd0\u7b97\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u53ef\u4ee5\u8f7b\u677e\u8fdb\u884c\u77e9\u9635\u8fd0\u7b97\u3002\u53ef\u4ee5\u8fdb\u884c\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u8f6c\u7f6e\u7b49\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u4e24\u4e2a\u77e9\u9635\u7684\u52a0\u6cd5\u53ef\u4ee5\u901a\u8fc7<code>+<\/code>\u8fd0\u7b97\u7b26\u5b9e\u73b0\uff0c\u800c\u77e9\u9635\u7684\u4e58\u6cd5\u5219\u53ef\u4ee5\u4f7f\u7528<code>numpy.dot()<\/code>\u6216<code>@<\/code>\u7b26\u53f7\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\n\nA = np.array([[1, 2], [3, 4]])\nB = np.array([[5, 6], [7, 8]])\n\n# \u77e9\u9635\u52a0\u6cd5\nC = A + B\nprint(&quot;\u52a0\u6cd5\u7ed3\u679c:\\n&quot;, C)\n\n# \u77e9\u9635\u4e58\u6cd5\nD = np.dot(A, B)\nprint(&quot;\u4e58\u6cd5\u7ed3\u679c:\\n&quot;, D)\n<\/code><\/pre>\n<p>\u8fd9\u6bb5\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u8fdb\u884c\u57fa\u672c\u7684\u77e9\u9635\u52a0\u6cd5\u548c\u4e58\u6cd5\u8fd0\u7b97\u3002<\/p>\n<p><strong>\u5982\u4f55\u5728Python\u4e2d\u5bf9\u77e9\u9635\u8fdb\u884c\u5207\u7247\u548c\u7d22\u5f15\u64cd\u4f5c\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u4f7f\u7528NumPy\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u77e9\u9635\u8fdb\u884c\u5207\u7247\u548c\u7d22\u5f15\u3002\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u884c\u548c\u5217\u7684\u7d22\u5f15\u6765\u8bbf\u95ee\u7279\u5b9a\u7684\u5143\u7d20\u3001\u884c\u6216\u5217\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5207\u7247\u548c\u7d22\u5f15\u7684\u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\n\nmatrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n\n# \u8bbf\u95ee\u7279\u5b9a\u5143\u7d20\nelement = matrix[1, 2]  # \u8bbf\u95ee\u7b2c\u4e8c\u884c\u7b2c\u4e09\u5217\u7684\u5143\u7d20\nprint(&quot;\u7279\u5b9a\u5143\u7d20:&quot;, element)\n\n# \u5207\u7247\u64cd\u4f5c\uff0c\u83b7\u53d6\u524d\u4e24\u884c\u548c\u524d\u4e24\u5217\nsub_matrix = matrix[:2, :2]\nprint(&quot;\u5207\u7247\u7ed3\u679c:\\n&quot;, sub_matrix)\n<\/code><\/pre>\n<p>\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u77e9\u9635\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u4f7f\u7528\u77e9\u9635\u7684\u65b9\u5f0f\u6709\u5f88\u591a\u79cd\uff0c\u4e3b\u8981\u901a\u8fc7NumPy\u5e93\u6765\u8fdb\u884c\u5b9e\u73b0\u3002\u77e9\u9635\u5728Python\u4e2d\u4f7f\u7528\u7684\u5173\u952e\u662f\u9009\u62e9 [&hellip;]","protected":false},"author":3,"featured_media":986150,"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\/986146"}],"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=986146"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/986146\/revisions"}],"predecessor-version":[{"id":986154,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/986146\/revisions\/986154"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/986150"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=986146"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=986146"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=986146"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}