![\"\u4e09\u5217python\u5982\u4f55\u751f\u6210\u77e9\u9635\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25202240\/0a08bc97-9297-42ef-a6d2-dc419eb68502.webp\")
<\/p>\n<\/p>\n
\u5728Python\u4e2d\u751f\u6210\u77e9\u9635\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u89e3\u6790\u3001Numpy\u5e93\u3001Pandas\u5e93<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528Numpy\u5e93\u662f\u6700\u5e38\u89c1\u548c\u6700\u65b9\u4fbf\u7684\u65b9\u6cd5\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u4f7f\u7528Numpy\u5e93\u751f\u6210\u77e9\u9635\u3002<\/p>\n<\/p>\n \u5217\u8868\u89e3\u6790\uff08List Comprehension\uff09\u662fPython\u7684\u4e00\u79cd\u7b80\u6d01\u800c\u5f3a\u5927\u7684\u751f\u6210\u5217\u8868\u7684\u65b9\u6cd5\u3002\u53ef\u4ee5\u5229\u7528\u5b83\u6765\u751f\u6210\u77e9\u9635\u3002<\/p>\n<\/p>\n matrix = [[j + i * 3 for j in range(1, 4)] for i in range(3)]<\/p>\n print(matrix)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n \u8fd9\u79cd\u65b9\u6cd5\u9002\u5408\u751f\u6210\u7b80\u5355\u7684\u77e9\u9635\uff0c\u4f46\u64cd\u4f5c\u590d\u6742\u77e9\u9635\u65f6\u4f1a\u6bd4\u8f83\u7e41\u7410\u3002<\/p>\n<\/p>\n Numpy\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u4e13\u95e8\u7528\u6765\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u548c\u77e9\u9635\u3002\u4f7f\u7528Numpy\u751f\u6210\u77e9\u9635\u65e2\u7b80\u5355\u53c8\u9ad8\u6548\u3002<\/p>\n<\/p>\n \u5728\u4f7f\u7528Numpy\u5e93\u4e4b\u524d\u9700\u8981\u5b89\u88c5\u5b83\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n Numpy\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u521b\u5efa\u77e9\u9635\uff0c\u5982\u4f7f\u7528\u6570\u7ec4\u3001\u968f\u673a\u6570\u3001\u5168\u96f6\u77e9\u9635\u3001\u5168\u4e00\u77e9\u9635\u7b49\u3002<\/p>\n<\/p>\n matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n print(matrix)<\/p>\n zero_matrix = np.zeros((3, 3))<\/p>\n print(zero_matrix)<\/p>\n one_matrix = np.ones((3, 3))<\/p>\n print(one_matrix)<\/p>\n random_matrix = np.random.rand(3, 3)<\/p>\n print(random_matrix)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n \u4f7f\u7528Numpy\u4e0d\u4ec5\u53ef\u4ee5\u65b9\u4fbf\u5730\u751f\u6210\u77e9\u9635\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u590d\u6742\u7684\u77e9\u9635\u8fd0\u7b97\u3002<\/p>\n<\/p>\n Pandas\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u6570\u636e\u64cd\u4f5c\u3002\u867d\u7136Pandas\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u6570\u636e\u6846\uff08DataFrame\uff09\uff0c\u4f46\u4e5f\u53ef\u4ee5\u7528\u6765\u751f\u6210\u77e9\u9635\u3002<\/p>\n<\/p>\n \u5728\u4f7f\u7528Pandas\u5e93\u4e4b\u524d\u9700\u8981\u5b89\u88c5\u5b83\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528Pandas\u5e93\u751f\u6210\u77e9\u9635\u901a\u5e38\u662f\u901a\u8fc7\u521b\u5efa\u6570\u636e\u6846\uff0c\u7136\u540e\u5c06\u5176\u8f6c\u6362\u4e3a\u77e9\u9635\u3002<\/p>\n<\/p>\n df = pd.DataFrame({<\/p>\n 'A': [1, 4, 7],<\/p>\n 'B': [2, 5, 8],<\/p>\n 'C': [3, 6, 9]<\/p>\n })<\/p>\n matrix = df.values<\/p>\n print(matrix)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n Pandas\u9002\u5408\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u5904\u7406\uff0c\u4f46\u5728\u751f\u6210\u77e9\u9635\u65f6\u4e0d\u5982Numpy\u65b9\u4fbf\u3002<\/p>\n<\/p>\n \u751f\u6210\u77e9\u9635\u540e\uff0c\u53ef\u4ee5\u5bf9\u77e9\u9635\u8fdb\u884c\u5404\u79cd\u64cd\u4f5c\uff0c\u5982\u77e9\u9635\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u3001\u8f6c\u7f6e\u7b49\u3002\u4e0b\u9762\u4ee5Numpy\u5e93\u4e3a\u4f8b\uff0c\u4ecb\u7ecd\u4e00\u4e9b\u57fa\u672c\u64cd\u4f5c\u3002<\/p>\n<\/p>\n matrix1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n matrix2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])<\/p>\n matrix_add = matrix1 + matrix2<\/p>\n print(matrix_add)<\/p>\n matrix_sub = matrix1 - matrix2<\/p>\n print(matrix_sub)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n matrix1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n matrix2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])<\/p>\n matrix_mul = np.dot(matrix1, matrix2)<\/p>\n print(matrix_mul)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n matrix_transpose = np.transpose(matrix)<\/p>\n print(matrix_transpose)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n \u9664\u4e86\u57fa\u672c\u64cd\u4f5c\uff0cNumpy\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u64cd\u4f5c\uff0c\u5982\u77e9\u9635\u6c42\u9006\u3001\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\u3001\u5947\u5f02\u503c\u5206\u89e3\u7b49\u3002<\/p>\n<\/p>\n matrix = np.array([[1, 2], [3, 4]])<\/p>\n matrix_inverse = np.linalg.inv(matrix)<\/p>\n print(matrix_inverse)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n matrix = np.array([[1, 2], [3, 4]])<\/p>\n eigenvalues, eigenvectors = np.linalg.eig(matrix)<\/p>\n print("\u7279\u5f81\u503c:", eigenvalues)<\/p>\n print("\u7279\u5f81\u5411\u91cf:", eigenvectors)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n matrix = np.array([[1, 2], [3, 4]])<\/p>\n U, S, V = np.linalg.svd(matrix)<\/p>\n print("U\u77e9\u9635:", U)<\/p>\n print("\u5947\u5f02\u503c:", S)<\/p>\n print("V\u77e9\u9635:", V)<\/p>\n <\/code><\/pre>\n<\/p>\n \u89e3\u91ca\uff1a<\/strong><\/p>\n<\/p>\n \u5728Python\u4e2d\u751f\u6210\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u6700\u5e38\u7528\u7684\u662fNumpy\u5e93\u3002\u5217\u8868\u89e3\u6790\u9002\u5408\u751f\u6210\u7b80\u5355\u7684\u77e9\u9635\uff0cNumpy\u5e93\u9002\u5408\u751f\u6210\u548c\u64cd\u4f5c\u590d\u6742\u7684\u77e9\u9635\uff0cPandas\u5e93\u9002\u5408\u6570\u636e\u5206\u6790\u548c\u5904\u7406<\/strong>\u3002\u5728\u77e9\u9635\u7684\u57fa\u672c\u64cd\u4f5c\u65b9\u9762\uff0cNumpy\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\uff0c\u5982\u77e9\u9635\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u3001\u8f6c\u7f6e\u7b49\u3002\u5728\u77e9\u9635\u7684\u9ad8\u7ea7\u64cd\u4f5c\u65b9\u9762\uff0cNumpy\u5e93\u540c\u6837\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u5982\u77e9\u9635\u6c42\u9006\u3001\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\u3001\u5947\u5f02\u503c\u5206\u89e3\u7b49\u3002\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\u548c\u64cd\u4f5c\uff0c\u53ef\u4ee5\u6781\u5927\u5730\u63d0\u9ad8\u6570\u636e\u5904\u7406\u548c\u79d1\u5b66\u8ba1\u7b97\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n \u5982\u4f55\u4f7f\u7528Python\u751f\u6210\u4e00\u4e2a\u7279\u5b9a\u5927\u5c0f\u7684\u77e9\u9635\uff1f<\/strong> \u662f\u5426\u53ef\u4ee5\u4f7f\u7528Python\u751f\u6210\u5305\u542b\u7279\u5b9a\u503c\u7684\u77e9\u9635\uff1f<\/strong> \u5982\u4f55\u4ece\u73b0\u6709\u6570\u636e\u521b\u5efa\u4e00\u4e2a\u77e9\u9635\uff1f<\/strong>\u4e00\u3001\u4f7f\u7528\u5217\u8868\u89e3\u6790\u751f\u6210\u77e9\u9635<\/h3>\n<\/p>\n
# \u751f\u6210\u4e00\u4e2a3x3\u7684\u77e9\u9635\uff0c\u5176\u4e2d\u5143\u7d20\u4e3a\u4ece1\u52309\u7684\u6570\u5b57<\/p>\n\n
for i in range(3)<\/code>\u751f\u6210\u4e09\u884c\u3002<\/li>\nfor j in range(1, 4)<\/code>\u751f\u6210\u6bcf\u884c\u4e2d\u7684\u4e09\u4e2a\u5143\u7d20\u3002<\/li>\n<\/ul>\n\u4e8c\u3001\u4f7f\u7528Numpy\u5e93\u751f\u6210\u77e9\u9635<\/h3>\n<\/p>\n
1. \u5b89\u88c5Numpy\u5e93<\/h4>\n<\/p>\n
pip install numpy<\/p>\n2. \u521b\u5efa\u77e9\u9635<\/h4>\n<\/p>\n
import numpy as np<\/p>\n\u521b\u5efa\u4e00\u4e2a3x3\u7684\u77e9\u9635\uff0c\u5176\u4e2d\u5143\u7d20\u4e3a\u4ece1\u52309\u7684\u6570\u5b57<\/strong><\/h2>\n
\u521b\u5efa\u4e00\u4e2a3x3\u7684\u5168\u96f6\u77e9\u9635<\/strong><\/h2>\n
\u521b\u5efa\u4e00\u4e2a3x3\u7684\u5168\u4e00\u77e9\u9635<\/strong><\/h2>\n
\u521b\u5efa\u4e00\u4e2a3x3\u7684\u968f\u673a\u77e9\u9635<\/strong><\/h2>\n
\n
np.array<\/code>\u53ef\u4ee5\u76f4\u63a5\u5c06\u5217\u8868\u8f6c\u6362\u4e3a\u77e9\u9635\u3002<\/li>\nnp.zeros<\/code>\u521b\u5efa\u5168\u96f6\u77e9\u9635\u3002<\/li>\nnp.ones<\/code>\u521b\u5efa\u5168\u4e00\u77e9\u9635\u3002<\/li>\nnp.random.rand<\/code>\u521b\u5efa\u968f\u673a\u77e9\u9635\u3002<\/li>\n<\/ul>\n\u4e09\u3001\u4f7f\u7528Pandas\u5e93\u751f\u6210\u77e9\u9635<\/h3>\n<\/p>\n
1. \u5b89\u88c5Pandas\u5e93<\/h4>\n<\/p>\n
pip install pandas<\/p>\n2. \u521b\u5efa\u77e9\u9635<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n\u521b\u5efa\u4e00\u4e2a3x3\u7684\u6570\u636e\u6846<\/strong><\/h2>\n
\u5c06\u6570\u636e\u6846\u8f6c\u6362\u4e3a\u77e9\u9635<\/strong><\/h2>\n
\n
pd.DataFrame<\/code>\u53ef\u4ee5\u521b\u5efa\u6570\u636e\u6846\u3002<\/li>\ndf.values<\/code>\u53ef\u4ee5\u5c06\u6570\u636e\u6846\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4\uff0c\u5373\u77e9\u9635\u3002<\/li>\n<\/ul>\n\u56db\u3001\u77e9\u9635\u7684\u57fa\u672c\u64cd\u4f5c<\/h3>\n<\/p>\n
1. \u77e9\u9635\u52a0\u6cd5\u548c\u51cf\u6cd5<\/h4>\n<\/p>\n
import numpy as np<\/p>\n\u521b\u5efa\u4e24\u4e2a3x3\u7684\u77e9\u9635<\/strong><\/h2>\n
\u77e9\u9635\u52a0\u6cd5<\/strong><\/h2>\n
\u77e9\u9635\u51cf\u6cd5<\/strong><\/h2>\n
\n
+<\/code>\u53ef\u4ee5\u8fdb\u884c\u77e9\u9635\u52a0\u6cd5\u3002<\/li>\n-<\/code>\u53ef\u4ee5\u8fdb\u884c\u77e9\u9635\u51cf\u6cd5\u3002<\/li>\n<\/ul>\n2. \u77e9\u9635\u4e58\u6cd5<\/h4>\n<\/p>\n
import numpy as np<\/p>\n\u521b\u5efa\u4e24\u4e2a3x3\u7684\u77e9\u9635<\/strong><\/h2>\n
\u77e9\u9635\u4e58\u6cd5<\/strong><\/h2>\n
\n
np.dot<\/code>\u53ef\u4ee5\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5\u3002<\/li>\n<\/ul>\n3. \u77e9\u9635\u8f6c\u7f6e<\/h4>\n<\/p>\n
import numpy as np<\/p>\n\u521b\u5efa\u4e00\u4e2a3x3\u7684\u77e9\u9635<\/strong><\/h2>\n
\u77e9\u9635\u8f6c\u7f6e<\/strong><\/h2>\n
\n
np.transpose<\/code>\u53ef\u4ee5\u5bf9\u77e9\u9635\u8fdb\u884c\u8f6c\u7f6e\u3002<\/li>\n<\/ul>\n\u4e94\u3001\u77e9\u9635\u7684\u9ad8\u7ea7\u64cd\u4f5c<\/h3>\n<\/p>\n
1. \u77e9\u9635\u6c42\u9006<\/h4>\n<\/p>\n
import numpy as np<\/p>\n\u521b\u5efa\u4e00\u4e2a2x2\u7684\u77e9\u9635<\/strong><\/h2>\n
\u77e9\u9635\u6c42\u9006<\/strong><\/h2>\n
\n
np.linalg.inv<\/code>\u53ef\u4ee5\u5bf9\u77e9\u9635\u8fdb\u884c\u6c42\u9006\u3002<\/li>\n<\/ul>\n2. \u77e9\u9635\u7684\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf<\/h4>\n<\/p>\n
import numpy as np<\/p>\n\u521b\u5efa\u4e00\u4e2a2x2\u7684\u77e9\u9635<\/strong><\/h2>\n
\u77e9\u9635\u7684\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf<\/strong><\/h2>\n
\n
np.linalg.eig<\/code>\u53ef\u4ee5\u6c42\u89e3\u77e9\u9635\u7684\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\u3002<\/li>\n<\/ul>\n3. \u77e9\u9635\u7684\u5947\u5f02\u503c\u5206\u89e3<\/h4>\n<\/p>\n
import numpy as np<\/p>\n\u521b\u5efa\u4e00\u4e2a2x2\u7684\u77e9\u9635<\/strong><\/h2>\n
\u77e9\u9635\u7684\u5947\u5f02\u503c\u5206\u89e3<\/strong><\/h2>\n
\n
np.linalg.svd<\/code>\u53ef\u4ee5\u5bf9\u77e9\u9635\u8fdb\u884c\u5947\u5f02\u503c\u5206\u89e3\u3002<\/li>\n<\/ul>\n\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684numpy.zeros()<\/code>\u3001numpy.ones()<\/code>\u6216numpy.random.rand()<\/code>\u7b49\u51fd\u6570\u6765\u751f\u6210\u7279\u5b9a\u5927\u5c0f\u7684\u77e9\u9635\u3002\u4f8b\u5982\uff0cnumpy.zeros((3, 3))<\/code>\u5c06\u751f\u6210\u4e00\u4e2a3×3\u7684\u96f6\u77e9\u9635\uff0c\u800cnumpy.random.rand(3, 3)<\/code>\u5c06\u751f\u6210\u4e00\u4e2a3×3\u7684\u968f\u673a\u77e9\u9635\u3002\u786e\u4fdd\u5728\u4f7f\u7528\u8fd9\u4e9b\u529f\u80fd\u4e4b\u524d\u5df2\u5b89\u88c5NumPy\u5e93\uff0c\u5e76\u901a\u8fc7import numpy as np<\/code>\u5bfc\u5165\u5b83\u3002<\/p>\n
\u5f53\u7136\u53ef\u4ee5\uff01\u60a8\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684numpy.full()<\/code>\u51fd\u6570\u6765\u751f\u6210\u4e00\u4e2a\u5305\u542b\u7279\u5b9a\u503c\u7684\u77e9\u9635\u3002\u4f8b\u5982\uff0cnumpy.full((3, 3), 7)<\/code>\u5c06\u521b\u5efa\u4e00\u4e2a3×3\u7684\u77e9\u9635\uff0c\u6240\u6709\u5143\u7d20\u90fd\u4e3a7\u3002\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u63a8\u5bfc\u5f0f\u6216\u8005\u5d4c\u5957\u5faa\u73af\u624b\u52a8\u6784\u9020\u8fd9\u6837\u7684\u77e9\u9635\u3002<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528NumPy\u7684numpy.array()<\/code>\u65b9\u6cd5\u4ece\u73b0\u6709\u6570\u636e\uff08\u5982\u5217\u8868\u6216\u5143\u7ec4\uff09\u521b\u5efa\u77e9\u9635\u3002\u4f8b\u5982\uff0cnumpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/code>\u5c06\u751f\u6210\u4e00\u4e2a3×3\u7684\u77e9\u9635\u3002\u786e\u4fdd\u8f93\u5165\u7684\u6570\u636e\u7ed3\u6784\u662f\u89c4\u5219\u7684\uff0c\u5373\u6bcf\u4e00\u884c\u7684\u957f\u5ea6\u76f8\u540c\uff0c\u8fd9\u6837\u624d\u80fd\u6210\u529f\u521b\u5efa\u77e9\u9635\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u751f\u6210\u77e9\u9635\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u89e3\u6790\u3001Numpy\u5e93\u3001Pandas\u5e93\u3002\u5176\u4e2d\uff0c\u4f7f\u7528Numpy\u5e93\u662f\u6700 […]","protected":false},"author":3,"featured_media":1160876,"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\/1160866"}],"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=1160866"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1160866\/revisions"}],"predecessor-version":[{"id":1160879,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1160866\/revisions\/1160879"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1160876"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1160866"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1160866"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1160866"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}