{"id":1135559,"date":"2025-01-08T21:26:49","date_gmt":"2025-01-08T13:26:49","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1135559.html"},"modified":"2025-01-08T21:26:52","modified_gmt":"2025-01-08T13:26:52","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e8%be%93%e5%85%a5%e4%b8%80%e4%b8%aan%e9%98%b6%e7%9f%a9%e9%98%b5-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1135559.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u8f93\u5165\u4e00\u4e2an\u9636\u77e9\u9635"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u8f93\u5165\u4e00\u4e2an\u9636\u77e9\u9635\"<\/p>\n

\u5728Python\u4e2d\u8f93\u5165\u4e00\u4e2an\u9636\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u3001NumPy\u5e93\u4ee5\u53ca\u7528\u6237\u8f93\u5165\u6765\u6784\u9020\u77e9\u9635\u3002<\/strong> \u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u7f3a\u70b9\uff0c\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u5e94\u7528\u573a\u666f\u6765\u51b3\u5b9a\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u6df1\u5165\u63a2\u8ba8\u5176\u5e94\u7528\u53ca\u6ce8\u610f\u4e8b\u9879\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5d4c\u5957\u5217\u8868<\/h3>\n<\/p>\n

\u5d4c\u5957\u5217\u8868\u662fPython\u4e2d\u6700\u57fa\u672c\u7684\u77e9\u9635\u8868\u793a\u65b9\u6cd5\u3002\u5b83\u7b80\u5355\u3001\u76f4\u89c2\uff0c\u975e\u5e38\u9002\u5408\u5c0f\u89c4\u6a21\u77e9\u9635\u7684\u5904\u7406\u3002<\/p>\n<\/p>\n

1.1 \u521b\u5efa\u5d4c\u5957\u5217\u8868<\/h4>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u6765\u521b\u5efa\u4e00\u4e2an\u9636\u77e9\u9635\u3002\u5d4c\u5957\u5217\u8868\u5c31\u662f\u4e00\u4e2a\u5217\u8868\u7684\u5143\u7d20\u4e5f\u662f\u5217\u8868\u3002\u4f8b\u5982\uff0c\u521b\u5efa\u4e00\u4e2a2×2\u77e9\u9635\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n<\/p>\n

matrix = [<\/p>\n
    [1, 2],<\/p>\n
    [3, 4]<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u52a8\u6001\u521b\u5efan\u9636\u77e9\u9635<\/h4>\n<\/p>\n
\u4e3a\u4e86\u52a8\u6001\u521b\u5efa\u4e00\u4e2an\u9636\u77e9\u9635\uff0c\u53ef\u4ee5\u4f7f\u7528\u5d4c\u5957\u7684\u5faa\u73af\u3002\u4f8b\u5982\uff0c\u521b\u5efa\u4e00\u4e2a3×3\u7684\u96f6\u77e9\u9635\uff1a<\/p>\n<\/p>\n
n = 3<\/p>\n
matrix = [[0 for _ in range(n)] for _ in range(n)]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u65b9\u6cd5\u4f7f\u7528\u4e86\u5217\u8868\u63a8\u5bfc\u5f0f\uff0c\u7b80\u6d01\u800c\u9ad8\u6548\u3002<\/p>\n<\/p>\n
1.3 \u7528\u6237\u8f93\u5165\u521b\u5efan\u9636\u77e9\u9635<\/h4>\n<\/p>\n
\u5982\u679c\u9700\u8981\u6839\u636e\u7528\u6237\u8f93\u5165\u6765\u521b\u5efa\u77e9\u9635\uff0c\u53ef\u4ee5\u4f7f\u7528\u5d4c\u5957\u7684input()<\/code>\u51fd\u6570\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
n = int(input("Enter the order of the matrix: "))<\/p>\n
matrix = []<\/p>\n
for i in range(n):<\/p>\n
    row = list(map(int, input(f"Enter row {i+1} (space-separated): ").split()))<\/p>\n
    matrix.append(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u7528\u6237\u4ea4\u4e92\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528NumPy\u5e93<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u7684\u6838\u5fc3\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u548c\u77e9\u9635\u64cd\u4f5c\u3002\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efa\u548c\u64cd\u4f5c\u77e9\u9635\u66f4\u52a0\u9ad8\u6548\u548c\u4fbf\u6377\u3002<\/p>\n<\/p>\n
2.1 \u5b89\u88c5NumPy<\/h4>\n<\/p>\n
\u5982\u679c\u8fd8\u672a\u5b89\u88c5NumPy\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u521b\u5efaNumPy\u6570\u7ec4<\/h4>\n<\/p>\n
\u4f7f\u7528NumPy\u5e93\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u521b\u5efa\u77e9\u9635\u3002\u4f8b\u5982\uff0c\u521b\u5efa\u4e00\u4e2a3×3\u7684\u96f6\u77e9\u9635\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
n = 3<\/p>\n
matrix = np.zeros((n, n))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3 \u7528\u6237\u8f93\u5165\u521b\u5efaNumPy\u77e9\u9635<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u7528\u6237\u8f93\u5165\u6765\u521b\u5efaNumPy\u77e9\u9635\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
n = int(input("Enter the order of the matrix: "))<\/p>\n
matrix = np.zeros((n, n))<\/p>\n
for i in range(n):<\/p>\n
    row = list(map(float, input(f"Enter row {i+1} (space-separated): ").split()))<\/p>\n
    matrix[i] = row<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u7ed3\u5408\u4e86\u7528\u6237\u8f93\u5165\u548cNumPy\u7684\u9ad8\u6548\u6570\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7ed3\u5408Pandas\u5e93\u8fdb\u884c\u77e9\u9635\u64cd\u4f5c<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u77e9\u9635\u3002\u4f7f\u7528Pandas\u53ef\u4ee5\u66f4\u52a0\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
3.1 \u5b89\u88c5Pandas<\/h4>\n<\/p>\n
\u5982\u679c\u8fd8\u672a\u5b89\u88c5Pandas\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u521b\u5efaPandas DataFrame<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528Pandas\u7684DataFrame\u6765\u521b\u5efa\u548c\u64cd\u4f5c\u77e9\u9635\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
n = 3<\/p>\n
data = np.zeros((n, n))<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.3 \u7528\u6237\u8f93\u5165\u521b\u5efaPandas\u77e9\u9635<\/h4>\n<\/p>\n
\u7ed3\u5408\u7528\u6237\u8f93\u5165\u548cPandas DataFrame\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u521b\u5efa\u77e9\u9635\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
n = int(input("Enter the order of the matrix: "))<\/p>\n
data = []<\/p>\n
for i in range(n):<\/p>\n
    row = list(map(float, input(f"Enter row {i+1} (space-separated): ").split()))<\/p>\n
    data.append(row)<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u77e9\u9635\u64cd\u4f5c\u548c\u5e94\u7528<\/h3>\n<\/p>\n
\u521b\u5efa\u77e9\u9635\u53ea\u662f\u7b2c\u4e00\u6b65\uff0c\u540e\u7eed\u8fd8\u53ef\u4ee5\u8fdb\u884c\u5404\u79cd\u77e9\u9635\u64cd\u4f5c\u548c\u5e94\u7528\u3002<\/p>\n<\/p>\n
4.1 \u77e9\u9635\u8f6c\u7f6e<\/h4>\n<\/p>\n
\u77e9\u9635\u8f6c\u7f6e\u662f\u5c06\u77e9\u9635\u7684\u884c\u548c\u5217\u4e92\u6362\u3002\u4f8b\u5982\uff0c\u4f7f\u7528NumPy\u8fdb\u884c\u77e9\u9635\u8f6c\u7f6e\uff1a<\/p>\n<\/p>\n
transposed_matrix = np.transpose(matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u77e9\u9635\u4e58\u6cd5<\/h4>\n<\/p>\n
\u77e9\u9635\u4e58\u6cd5\u662f\u5e38\u89c1\u7684\u77e9\u9635\u64cd\u4f5c\u4e4b\u4e00\u3002\u4f8b\u5982\uff0c\u4f7f\u7528NumPy\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5\uff1a<\/p>\n<\/p>\n
matrix_a = np.array([[1, 2], [3, 4]])<\/p>\n
matrix_b = np.array([[5, 6], [7, 8]])<\/p>\n
result = np.dot(matrix_a, matrix_b)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3 \u77e9\u9635\u6c42\u9006<\/h4>\n<\/p>\n
\u77e9\u9635\u6c42\u9006\u5728\u67d0\u4e9b\u5e94\u7528\u4e2d\u975e\u5e38\u91cd\u8981\u3002\u4f8b\u5982\uff0c\u4f7f\u7528NumPy\u8fdb\u884c\u77e9\u9635\u6c42\u9006\uff1a<\/p>\n<\/p>\n
matrix = np.array([[1, 2], [3, 4]])<\/p>\n
inverse_matrix = np.linalg.inv(matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.4 \u77e9\u9635\u7684\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf<\/h4>\n<\/p>\n
\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\u5728\u8bb8\u591a\u79d1\u5b66\u548c\u5de5\u7a0b\u5e94\u7528\u4e2d\u975e\u5e38\u91cd\u8981\u3002\u4f8b\u5982\uff0c\u4f7f\u7528NumPy\u8ba1\u7b97\u77e9\u9635\u7684\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\uff1a<\/p>\n<\/p>\n
matrix = np.array([[1, 2], [3, 4]])<\/p>\n
eigenvalues, eigenvectors = np.linalg.eig(matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6ce8\u610f\u4e8b\u9879\u548c\u6700\u4f73\u5b9e\u8df5<\/h3>\n<\/p>\n
\u5728\u4f7f\u7528Python\u8fdb\u884c\u77e9\u9635\u64cd\u4f5c\u65f6\uff0c\u6709\u4e00\u4e9b\u6ce8\u610f\u4e8b\u9879\u548c\u6700\u4f73\u5b9e\u8df5\u53ef\u4ee5\u63d0\u9ad8\u6548\u7387\u548c\u4ee3\u7801\u8d28\u91cf\u3002<\/p>\n<\/p>\n
5.1 \u6570\u636e\u7c7b\u578b\u548c\u7cbe\u5ea6<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u77e9\u9635\u8ba1\u7b97\u65f6\uff0c\u6570\u636e\u7c7b\u578b\u548c\u7cbe\u5ea6\u975e\u5e38\u91cd\u8981\u3002NumPy\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u7c7b\u578b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
matrix = np.array([[1, 2], [3, 4]], dtype=np.float64)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u907f\u514d\u4f7f\u7528for\u5faa\u73af<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u77e9\u9635\u65f6\uff0c\u5c3d\u91cf\u907f\u514d\u4f7f\u7528for\u5faa\u73af\uff0c\u800c\u5e94\u4f7f\u7528NumPy\u7684\u5411\u91cf\u5316\u64cd\u4f5c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
# \u4e0d\u63a8\u8350\u7684\u505a\u6cd5<\/p>\n
result = np.zeros((n, n))<\/p>\n
for i in range(n):<\/p>\n
    for j in range(n):<\/p>\n
        result[i, j] = matrix_a[i, j] + matrix_b[i, j]<\/p>\n
\u63a8\u8350\u7684\u505a\u6cd5<\/strong><\/h2>\n
result = matrix_a + matrix_b<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.3 \u4f7f\u7528\u51fd\u6570\u548c\u6a21\u5757\u5316\u7f16\u7a0b<\/h4>\n<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u53ef\u7ef4\u62a4\u6027\uff0c\u5efa\u8bae\u5c06\u77e9\u9635\u64cd\u4f5c\u5c01\u88c5\u6210\u51fd\u6570\uff0c\u5e76\u8fdb\u884c\u6a21\u5757\u5316\u7f16\u7a0b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def create_matrix(n):<\/p>\n
    return np.zeros((n, n))<\/p>\n
def transpose_matrix(matrix):<\/p>\n
    return np.transpose(matrix)<\/p>\n
def multiply_matrices(matrix_a, matrix_b):<\/p>\n
    return np.dot(matrix_a, matrix_b)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5728Python\u4e2d\u8f93\u5165n\u9636\u77e9\u9635\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u3001NumPy\u5e93\u4ee5\u53caPandas\u5e93\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u9002\u7528\u7684\u573a\u666f\u548c\u4f18\u7f3a\u70b9\u3002\u9664\u4e86\u521b\u5efa\u77e9\u9635\uff0c\u8fd8\u4ecb\u7ecd\u4e86\u77e9\u9635\u7684\u5e38\u89c1\u64cd\u4f5c\u548c\u5e94\u7528\uff0c\u5305\u62ec\u77e9\u9635\u8f6c\u7f6e\u3001\u77e9\u9635\u4e58\u6cd5\u3001\u77e9\u9635\u6c42\u9006\u4ee5\u53ca\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\u7684\u8ba1\u7b97\u3002\u6700\u540e\uff0c\u672c\u6587\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u6ce8\u610f\u4e8b\u9879\u548c\u6700\u4f73\u5b9e\u8df5\uff0c\u4ee5\u5e2e\u52a9\u63d0\u9ad8\u4ee3\u7801\u7684\u6548\u7387\u548c\u8d28\u91cf\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u8bfb\u8005\u80fd\u591f\u66f4\u597d\u5730\u638c\u63e1Python\u4e2d\u7684\u77e9\u9635\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u4e00\u4e2an\u9636\u77e9\u9635\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u6216\u8005NumPy\u5e93\u6765\u521b\u5efan\u9636\u77e9\u9635\u3002\u901a\u8fc7\u5d4c\u5957\u5217\u8868\uff0c\u53ef\u4ee5\u624b\u52a8\u8f93\u5165\u6bcf\u4e00\u884c\u7684\u5143\u7d20\u3002\u4f7f\u7528NumPy\u5e93\u5219\u53ef\u4ee5\u65b9\u4fbf\u5730\u521b\u5efa\u548c\u64cd\u4f5c\u77e9\u9635\u3002\u4f8b\u5982\uff0c\u4f7f\u7528numpy.array()<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06\u5217\u8868\u8f6c\u6362\u4e3a\u77e9\u9635\u3002\u8fd9\u6837\u505a\u4e0d\u4ec5\u7b80\u5316\u4e86\u77e9\u9635\u7684\u521b\u5efa\u8fc7\u7a0b\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u77e9\u9635\u8fd0\u7b97\u529f\u80fd\u3002<\/p>\n
\u5982\u4f55\u4ece\u7528\u6237\u8f93\u5165\u4e2d\u8bfb\u53d6n\u9636\u77e9\u9635\u7684\u5143\u7d20\uff1f<\/strong>
\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528input()<\/code>\u51fd\u6570\u5728\u547d\u4ee4\u884c\u4e2d\u8bfb\u53d6\u7528\u6237\u8f93\u5165\u3002\u5efa\u8bae\u5148\u8bfb\u53d6n\u7684\u503c\uff0c\u7136\u540e\u6839\u636en\u7684\u5927\u5c0f\u5faa\u73af\u8f93\u5165\u6bcf\u4e00\u884c\u7684\u5143\u7d20\u3002\u5728\u8f93\u5165\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528split()<\/code>\u65b9\u6cd5\u5c06\u5b57\u7b26\u4e32\u5206\u5272\u6210\u591a\u4e2a\u5143\u7d20\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u9002\u5f53\u7684\u6570\u636e\u7c7b\u578b\uff0c\u5982\u6574\u6570\u6216\u6d6e\u70b9\u6570\u3002<\/p>\n
\u4f7f\u7528NumPy\u5e93\u8f93\u5165n\u9636\u77e9\u9635\u65f6\u6709\u54ea\u4e9b\u4fbf\u6377\u7684\u65b9\u6cd5\uff1f<\/strong>
NumPy\u63d0\u4f9b\u4e86numpy.zeros()<\/code>\u3001numpy.ones()<\/code>\u548cnumpy.random.rand()<\/code>\u7b49\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u521b\u5efa\u4e00\u4e2a\u586b\u5145\u7279\u5b9a\u503c\u7684n\u9636\u77e9\u9635\u3002\u901a\u8fc7\u8fd9\u4e9b\u51fd\u6570\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u521d\u59cb\u77e9\u9635\uff0c\u4ece\u800c\u8fdb\u884c\u540e\u7eed\u7684\u8ba1\u7b97\u548c\u64cd\u4f5c\u3002\u6b64\u5916\uff0cNumPy\u8fd8\u652f\u6301\u901a\u8fc7\u5207\u7247\u548c\u7d22\u5f15\u8f7b\u677e\u8bbf\u95ee\u548c\u4fee\u6539\u77e9\u9635\u4e2d\u7684\u5143\u7d20\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8f93\u5165\u4e00\u4e2an\u9636\u77e9\u9635\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u3001NumPy\u5e93\u4ee5\u53ca\u7528\u6237\u8f93\u5165\u6765\u6784\u9020\u77e9\u9635 […]","protected":false},"author":3,"featured_media":1135568,"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\/1135559"}],"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=1135559"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1135559\/revisions"}],"predecessor-version":[{"id":1135570,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1135559\/revisions\/1135570"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1135568"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1135559"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1135559"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1135559"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}