{"id":965661,"date":"2024-12-27T04:40:12","date_gmt":"2024-12-26T20:40:12","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/965661.html"},"modified":"2024-12-27T04:40:15","modified_gmt":"2024-12-26T20:40:15","slug":"python%e5%a6%82%e4%bd%95%e5%af%bc%e5%85%a5%e7%9f%a9%e9%98%b5matrix","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/965661.html","title":{"rendered":"python\u5982\u4f55\u5bfc\u5165\u77e9\u9635matrix"},"content":{"rendered":"

\"python\u5982\u4f55\u5bfc\u5165\u77e9\u9635matrix\"<\/p>\n

\u5728Python\u4e2d\u5bfc\u5165\u77e9\u9635\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528NumPy\u5e93\u3001\u4f7f\u7528\u5217\u8868\u5217\u8868\u7684\u5f62\u5f0f\u3001\u4f7f\u7528Pandas\u5e93\u3001\u4ee5\u53caSciPy\u5e93\u5b9e\u73b0\u3002<\/strong> \u5176\u4e2d\uff0cNumPy\u5e93\u662f\u6700\u5e38\u7528\u7684\u65b9\u5f0f\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u77e9\u9635\u8fd0\u7b97\u529f\u80fd\u3002\u4e0b\u9762\u6211\u4eec\u8be6\u7ec6\u63a2\u8ba8\u4f7f\u7528NumPy\u5e93\u5bfc\u5165\u77e9\u9635\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

NumPy\u5e93\u662fPython\u4e2d\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\u3001\u5404\u79cd\u6d3e\u751f\u5bf9\u8c61\uff08\u5982\u63a9\u7801\u6570\u7ec4\u3001\u77e9\u9635\uff09\u4ee5\u53ca\u7528\u4e8e\u5feb\u901f\u64cd\u4f5c\u6570\u7ec4\u7684\u591a\u79cd\u4f8b\u7a0b\u3002\u8981\u4f7f\u7528NumPy\u5e93\u4e2d\u7684\u77e9\u9635\u529f\u80fd\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165NumPy\u5e93\u3002\u5b89\u88c5NumPy\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b8c\u6210\uff1a<\/p>\n<\/p>\n

pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165NumPy\u5e93\uff0c\u5e76\u521b\u5efa\u4e00\u4e2a\u77e9\u9635\u3002NumPy\u4e2d\u6570\u7ec4\u5bf9\u8c61\u7684\u7c7b\u578b\u662fndarray<\/code>\uff0c\u8fd9\u662f\u4e00\u79cd\u5177\u6709\u77e2\u91cf\u5316\u6570\u5b66\u8fd0\u7b97\u80fd\u529b\u7684\u591a\u7ef4\u6570\u7ec4\u3002\u521b\u5efa\u77e9\u9635\u53ef\u4ee5\u901a\u8fc7numpy.array()<\/code>\u51fd\u6570\u5b9e\u73b0\u3002\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a2x3\u7684\u77e9\u9635<\/strong><\/h2>\n
matrix = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
print(matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e00\u3001NUMPY\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
NumPy\u5e93\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\u6765\u521b\u5efa\u548c\u64cd\u4f5c\u77e9\u9635\u3002\u901a\u8fc7NumPy\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u6267\u884c\u77e9\u9635\u7684\u57fa\u672c\u8fd0\u7b97\uff0c\u5982\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u8f6c\u7f6e\u7b49\u3002<\/p>\n<\/p>\n
1. \u521b\u5efa\u77e9\u9635<\/h4>\n<\/p>\n
\u521b\u5efa\u77e9\u9635\u53ef\u4ee5\u4f7f\u7528numpy.array()<\/code>\u51fd\u6570\u3002\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u5d4c\u5957\u5217\u8868\u6765\u5b9a\u4e49\u77e9\u9635\u3002\u4ee5\u4e0b\u662f\u521b\u5efa\u4e00\u4e2a2×3\u77e9\u9635\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
print("\u77e9\u9635\uff1a\\n", matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u77e9\u9635\u8fd0\u7b97<\/h4>\n<\/p>\n
NumPy\u5e93\u652f\u6301\u591a\u79cd\u77e9\u9635\u8fd0\u7b97\uff0c\u5305\u62ec\u77e9\u9635\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u8f6c\u7f6e\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u8fd0\u7b97\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u77e9\u9635\u52a0\u6cd5<\/p>\n
matrix1 = np.array([[1, 2], [3, 4]])<\/p>\n
matrix2 = np.array([[5, 6], [7, 8]])<\/p>\n
sum_matrix = matrix1 + matrix2<\/p>\n
print("\u77e9\u9635\u52a0\u6cd5\uff1a\\n", sum_matrix)<\/p>\n
\u77e9\u9635\u4e58\u6cd5<\/strong><\/h2>\n
product_matrix = np.dot(matrix1, matrix2)<\/p>\n
print("\u77e9\u9635\u4e58\u6cd5\uff1a\\n", product_matrix)<\/p>\n
\u77e9\u9635\u8f6c\u7f6e<\/strong><\/h2>\n
transpose_matrix = np.transpose(matrix1)<\/p>\n
print("\u77e9\u9635\u8f6c\u7f6e\uff1a\\n", transpose_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u5217\u8868\u5217\u8868\u7684\u5f62\u5f0f<\/h3>\n<\/p>\n
\u9664\u4e86\u4f7f\u7528NumPy\u5e93\uff0cPython\u4e2d\u8fd8\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u7684\u5f62\u5f0f\u6765\u8868\u793a\u77e9\u9635\u3002\u8fd9\u79cd\u65b9\u5f0f\u7b80\u5355\u76f4\u89c2\uff0c\u4f46\u7f3a\u4e4fNumPy\u63d0\u4f9b\u7684\u4e30\u5bcc\u77e9\u9635\u8fd0\u7b97\u529f\u80fd\u3002<\/p>\n<\/p>\n
1. \u521b\u5efa\u77e9\u9635<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u5b9a\u4e49\u5217\u8868\u5217\u8868\u7684\u65b9\u5f0f\u521b\u5efa\u77e9\u9635\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a2x3\u7684\u77e9\u9635<\/p>\n
matrix = [[1, 2, 3], [4, 5, 6]]<\/p>\n
print("\u77e9\u9635\uff1a", matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u624b\u52a8\u5b9e\u73b0\u77e9\u9635\u8fd0\u7b97<\/h4>\n<\/p>\n
\u4f7f\u7528\u5217\u8868\u5217\u8868\u7684\u65b9\u5f0f\u65f6\uff0c\u9700\u8981\u624b\u52a8\u5b9e\u73b0\u77e9\u9635\u8fd0\u7b97\u529f\u80fd\u3002\u8fd9\u76f8\u5bf9\u590d\u6742\u4e14\u5bb9\u6613\u51fa\u9519\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u77e9\u9635\u52a0\u6cd5\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u77e9\u9635\u52a0\u6cd5<\/p>\n
matrix1 = [[1, 2], [3, 4]]<\/p>\n
matrix2 = [[5, 6], [7, 8]]<\/p>\n
sum_matrix = [[matrix1[i][j] + matrix2[i][j] for j in range(len(matrix1[0]))] for i in range(len(matrix1))]<\/p>\n
print("\u77e9\u9635\u52a0\u6cd5\uff1a", sum_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u5e93\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u5206\u6790\uff0c\u4f46\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u77e9\u9635\u3002\u5b83\u7684DataFrame<\/code>\u5bf9\u8c61\u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u8fdb\u884c\u57fa\u672c\u7684\u77e9\u9635\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
1. \u521b\u5efaDataFrame<\/h4>\n<\/p>\n
\u8981\u4f7f\u7528Pandas\u5e93\u5904\u7406\u77e9\u9635\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Pandas\u5e93\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2aDataFrame<\/code>\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame<\/strong><\/h2>\n
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])<\/p>\n
print("DataFrame\uff1a\\n", df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u57fa\u672c\u8fd0\u7b97<\/h4>\n<\/p>\n
Pandas\u7684DataFrame<\/code>\u5bf9\u8c61\u652f\u6301\u57fa\u672c\u7684\u77e9\u9635\u8fd0\u7b97\u3002\u4ee5\u4e0b\u662f\u77e9\u9635\u52a0\u6cd5\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])<\/p>\n
df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])<\/p>\n
\u77e9\u9635\u52a0\u6cd5<\/strong><\/h2>\n
sum_df = df1 + df2<\/p>\n
print("\u77e9\u9635\u52a0\u6cd5\uff1a\\n", sum_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528SciPy\u5e93<\/h3>\n<\/p>\n
SciPy\u5e93\u662f\u4e00\u4e2a\u5f00\u6e90\u7684Python\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u6570\u5b66\u3001\u79d1\u5b66\u548c\u5de5\u7a0b\u9886\u57df\u7684\u6280\u672f\u8ba1\u7b97\u3002\u5b83\u5728NumPy\u7684\u57fa\u7840\u4e0a\u589e\u52a0\u4e86\u8bb8\u591a\u6709\u7528\u7684\u51fd\u6570\u3002SciPy\u5e93\u63d0\u4f9b\u4e86scipy.sparse<\/code>\u6a21\u5757\u7528\u4e8e\u521b\u5efa\u7a00\u758f\u77e9\u9635\u3002<\/p>\n<\/p>\n
1. \u521b\u5efa\u7a00\u758f\u77e9\u9635<\/h4>\n<\/p>\n
\u8981\u4f7f\u7528SciPy\u5e93\u5904\u7406\u7a00\u758f\u77e9\u9635\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5SciPy\u5e93\uff1a<\/p>\n<\/p>\n
pip install scipy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u7a00\u758f\u77e9\u9635\uff1a<\/p>\n<\/p>\n
from scipy.sparse import csr_matrix<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7a00\u758f\u77e9\u9635<\/strong><\/h2>\n
sparse_matrix = csr_matrix([[0, 0, 1], [1, 0, 0], [0, 1, 0]])<\/p>\n
print("\u7a00\u758f\u77e9\u9635\uff1a\\n", sparse_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u7a00\u758f\u77e9\u9635\u8fd0\u7b97<\/h4>\n<\/p>\n
SciPy\u5e93\u652f\u6301\u7a00\u758f\u77e9\u9635\u7684\u57fa\u672c\u8fd0\u7b97\u3002\u4ee5\u4e0b\u662f\u7a00\u758f\u77e9\u9635\u52a0\u6cd5\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from scipy.sparse import csr_matrix<\/p>\n
\u521b\u5efa\u4e24\u4e2a\u7a00\u758f\u77e9\u9635<\/strong><\/h2>\n
sparse_matrix1 = csr_matrix([[0, 0, 1], [1, 0, 0], [0, 1, 0]])<\/p>\n
sparse_matrix2 = csr_matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])<\/p>\n
\u7a00\u758f\u77e9\u9635\u52a0\u6cd5<\/strong><\/h2>\n
sum_sparse_matrix = sparse_matrix1 + sparse_matrix2<\/p>\n
print("\u7a00\u758f\u77e9\u9635\u52a0\u6cd5\uff1a\\n", sum_sparse_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u51e0\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u65b9\u4fbf\u5730\u5bfc\u5165\u548c\u64cd\u4f5c\u77e9\u9635\u3002NumPy\u5e93\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u9002\u5408\u5927\u591a\u6570\u9700\u8981\u77e9\u9635\u8fd0\u7b97\u7684\u573a\u666f\uff0c\u800cSciPy\u5e93\u5219\u9002\u7528\u4e8e\u5904\u7406\u7a00\u758f\u77e9\u9635\u7684\u60c5\u51b5\u3002Pandas\u5e93\u7684DataFrame<\/code>\u5bf9\u8c61\u867d\u7136\u4e0d\u662f\u4e13\u95e8\u4e3a\u77e9\u9635\u8bbe\u8ba1\u7684\uff0c\u4f46\u5728\u6570\u636e\u5206\u6790\u4e2d\u4e5f\u80fd\u8d77\u5230\u7c7b\u4f3c\u7684\u4f5c\u7528\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u5e76\u7b80\u5316\u4ee3\u7801\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u4e00\u4e2a\u77e9\u9635\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efa\u77e9\u9635\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5NumPy\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4pip install numpy<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u521b\u5efa\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u53ef\u4ee5\u4f7f\u7528numpy.array()<\/code>\u51fd\u6570\uff0c\u5c06\u4e00\u4e2a\u5d4c\u5957\u5217\u8868\u4f20\u5165\u6765\u521b\u5efa\u4e00\u4e2a\u77e9\u9635\u3002\u4f8b\u5982\uff1a  <\/p>\n
import numpy as np  \nmatrix = np.array([[1, 2], [3, 4]])  \nprint(matrix)  \n<\/code><\/pre>\n
\u8fd9\u6837\u5c31\u53ef\u4ee5\u751f\u6210\u4e00\u4e2a2×2\u7684\u77e9\u9635\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5bfc\u5165\u73b0\u6709\u7684\u77e9\u9635\u6570\u636e\uff1f<\/strong>
\u5982\u679c\u60a8\u6709\u4e00\u4e2a\u5b58\u50a8\u5728\u6587\u4ef6\u4e2d\u7684\u77e9\u9635\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u7684numpy.loadtxt()<\/code>\u6216numpy.genfromtxt()<\/code>\u51fd\u6570\u5bfc\u5165\u6570\u636e\u3002\u5047\u8bbe\u60a8\u7684\u6570\u636e\u4fdd\u5b58\u5728\u4e00\u4e2a\u540d\u4e3adata.txt<\/code>\u7684\u6587\u4ef6\u4e2d\uff0c\u683c\u5f0f\u5982\u4e0b\uff1a  <\/p>\n
1 2\n3 4\n<\/code><\/pre>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5c06\u5176\u5bfc\u5165\u4e3a\u77e9\u9635\uff1a  <\/p>\n
import numpy as np  \nmatrix = np.loadtxt('data.txt')  \nprint(matrix)  \n<\/code><\/pre>\n
\u8fd9\u5c06\u8bfb\u53d6\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff0c\u5e76\u521b\u5efa\u4e00\u4e2aNumPy\u77e9\u9635\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u5bf9\u77e9\u9635\u8fdb\u884c\u57fa\u672c\u8fd0\u7b97\uff1f<\/strong>
\u4f7f\u7528NumPy\u5e93\uff0c\u60a8\u53ef\u4ee5\u5bf9\u77e9\u9635\u8fdb\u884c\u591a\u79cd\u8fd0\u7b97\uff0c\u6bd4\u5982\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u7b49\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6709\u4e24\u4e2a\u77e9\u9635\uff1a  <\/p>\n
import numpy as np  \nA = np.array([[1, 2], [3, 4]])  \nB = np.array([[5, 6], [7, 8]])  \n<\/code><\/pre>\n
\u53ef\u4ee5\u8fdb\u884c\u52a0\u6cd5\uff1a  <\/p>\n
C = A + B  \nprint(C)  # \u8f93\u51fa\uff1a[[ 6  8]\n           #        [10 12]]\n<\/code><\/pre>\n
\u7c7b\u4f3c\u5730\uff0c\u60a8\u8fd8\u53ef\u4ee5\u8fdb\u884c\u5176\u4ed6\u8fd0\u7b97\uff0c\u4f8b\u5982A - B<\/code>\u8fdb\u884c\u51cf\u6cd5\uff0cA * B<\/code>\u8fdb\u884c\u9010\u5143\u7d20\u4e58\u6cd5\uff0c\u6216\u4f7f\u7528np.dot(A, B)<\/code>\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u5bfc\u5165\u77e9\u9635\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528NumPy\u5e93\u3001\u4f7f\u7528\u5217\u8868\u5217\u8868\u7684\u5f62\u5f0f\u3001\u4f7f\u7528Pandas\u5e93\u3001\u4ee5\u53caSciPy\u5e93\u5b9e\u73b0 […]","protected":false},"author":3,"featured_media":965673,"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\/965661"}],"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=965661"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/965661\/revisions"}],"predecessor-version":[{"id":965677,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/965661\/revisions\/965677"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/965673"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=965661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=965661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=965661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}