{"id":1167476,"date":"2025-01-15T15:45:10","date_gmt":"2025-01-15T07:45:10","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1167476.html"},"modified":"2025-01-15T15:45:13","modified_gmt":"2025-01-15T07:45:13","slug":"%e5%a6%82%e4%bd%95%e8%8e%b7%e5%8f%96%e7%9f%a9%e9%98%b5%e7%9a%84%e5%88%97python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1167476.html","title":{"rendered":"\u5982\u4f55\u83b7\u53d6\u77e9\u9635\u7684\u5217python"},"content":{"rendered":"

\"\u5982\u4f55\u83b7\u53d6\u77e9\u9635\u7684\u5217python\"<\/p>\n

\u8981\u5728Python\u4e2d\u83b7\u53d6\u77e9\u9635\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\uff1a\u5217\u8868\u89e3\u6790\u3001NumPy\u5e93\u3001Pandas\u5e93<\/strong>\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5217\u8868\u89e3\u6790<\/h3>\n<\/p>\n

\u5217\u8868\u89e3\u6790\u662f\u4e00\u79cd\u975e\u5e38\u7075\u6d3b\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u83b7\u53d6\u77e9\u9635\u7684\u5217\u3002\u901a\u8fc7\u5217\u8868\u89e3\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u4ece\u5d4c\u5957\u5217\u8868\uff08\u77e9\u9635\uff09\u4e2d\u63d0\u53d6\u51fa\u67d0\u4e00\u5217\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u4e8c\u7ef4\u5217\u8868\uff08\u77e9\u9635\uff09\uff1a<\/p>\n<\/p>\n

matrix = [<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8981\u83b7\u53d6\u7b2ci<\/code>\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
column_i = [row[i] for row in matrix]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528NumPy\u5e93<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u7684\u4e00\u4e2a\u91cd\u8981\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u5bf9\u8c61\u548c\u4e30\u5bcc\u7684\u51fd\u6570\u5e93\u3002\u4f7f\u7528NumPy\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u83b7\u53d6\u77e9\u9635\u7684\u5217\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5NumPy\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
column_i = matrix[:, i]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u7684\u4e00\u4e2a\u91cd\u8981\u5e93\uff0c\u63d0\u4f9b\u4e86DataFrame\u5bf9\u8c61\uff0c\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5Pandas\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
matrix = pd.DataFrame([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
column_i = matrix.iloc[:, i]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5176\u4ed6\u65b9\u6cd5<\/h3>\n<\/p>\n
\u9664\u4e86\u4e0a\u8ff0\u65b9\u6cd5\u5916\uff0c\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u83b7\u53d6\u77e9\u9635\u7684\u5217\uff0c\u6bd4\u5982\u4f7f\u7528zip<\/code>\u51fd\u6570\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u4f60\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u6570\u636e\u7ed3\u6784\u3002<\/p>\n<\/p>\n
\u5217\u8868\u89e3\u6790<\/h3>\n<\/p>\n
\u5217\u8868\u89e3\u6790\u662f\u4e00\u79cd\u7b80\u6d01\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u4ece\u5d4c\u5957\u5217\u8868\u4e2d\u63d0\u53d6\u67d0\u4e00\u5217\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u4e8c\u7ef4\u5217\u8868matrix<\/code>\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u89e3\u6790\u6765\u83b7\u53d6\u67d0\u4e00\u5217\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u5217\u8868\u89e3\u6790\u83b7\u53d6\u77e9\u9635\u7684\u5217<\/h3>\n<\/p>\n
\u5217\u8868\u89e3\u6790\u662f\u4e00\u79cd\u7b80\u6d01\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u4ece\u5d4c\u5957\u5217\u8868\u4e2d\u63d0\u53d6\u67d0\u4e00\u5217\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u4e8c\u7ef4\u5217\u8868matrix<\/code>\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u89e3\u6790\u6765\u83b7\u53d6\u67d0\u4e00\u5217\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
matrix = [<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
]<\/p>\n
\u83b7\u53d6\u7b2c1\u5217<\/strong><\/h2>\n
column_1 = [row[0] for row in matrix]<\/p>\n
print("Column 1:", column_1)<\/p>\n
\u83b7\u53d6\u7b2c2\u5217<\/strong><\/h2>\n
column_2 = [row[1] for row in matrix]<\/p>\n
print("Column 2:", column_2)<\/p>\n
\u83b7\u53d6\u7b2c3\u5217<\/strong><\/h2>\n
column_3 = [row[2] for row in matrix]<\/p>\n
print("Column 3:", column_3)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u5217\u8868\u89e3\u6790 [row[i] for row in matrix]<\/code> \u6765\u83b7\u53d6\u77e9\u9635\u7684\u7b2c i<\/code> \u5217\u3002row[i]<\/code> \u8868\u793a\u6bcf\u4e00\u884c\u7684\u7b2c i<\/code> \u4e2a\u5143\u7d20\uff0cfor row in matrix<\/code> \u8868\u793a\u904d\u5386\u77e9\u9635\u7684\u6bcf\u4e00\u884c\uff0c\u4ece\u800c\u63d0\u53d6\u51fa\u6bcf\u4e00\u884c\u7684\u7b2c i<\/code> \u4e2a\u5143\u7d20\u7ec4\u6210\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u3002<\/p>\n<\/p>\n
NumPy\u5e93<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u7684\u4e00\u4e2a\u91cd\u8981\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u5bf9\u8c61\u548c\u4e30\u5bcc\u7684\u51fd\u6570\u5e93\u3002\u4f7f\u7528NumPy\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u83b7\u53d6\u77e9\u9635\u7684\u5217\u3002<\/p>\n<\/p>\n
\u5b89\u88c5NumPy\u5e93<\/h3>\n<\/p>\n
\u5728\u4f7f\u7528NumPy\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5NumPy\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528NumPy\u83b7\u53d6\u77e9\u9635\u7684\u5217<\/h3>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aNumPy\u6570\u7ec4matrix<\/code>\uff0c\u53ef\u4ee5\u901a\u8fc7\u5207\u7247\u64cd\u4f5c\u6765\u83b7\u53d6\u67d0\u4e00\u5217\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
\u83b7\u53d6\u7b2c1\u5217<\/strong><\/h2>\n
column_1 = matrix[:, 0]<\/p>\n
print("Column 1:", column_1)<\/p>\n
\u83b7\u53d6\u7b2c2\u5217<\/strong><\/h2>\n
column_2 = matrix[:, 1]<\/p>\n
print("Column 2:", column_2)<\/p>\n
\u83b7\u53d6\u7b2c3\u5217<\/strong><\/h2>\n
column_3 = matrix[:, 2]<\/p>\n
print("Column 3:", column_3)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cmatrix[:, i]<\/code> \u8868\u793a\u83b7\u53d6\u77e9\u9635\u7684\u7b2c i<\/code> \u5217\u3002:<\/code> \u8868\u793a\u9009\u62e9\u6240\u6709\u884c\uff0ci<\/code> \u8868\u793a\u9009\u62e9\u7b2c i<\/code> \u5217\u3002<\/p>\n<\/p>\n
Pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u7684\u4e00\u4e2a\u91cd\u8981\u5e93\uff0c\u63d0\u4f9b\u4e86DataFrame\u5bf9\u8c61\uff0c\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u5b89\u88c5Pandas\u5e93<\/h3>\n<\/p>\n
\u5728\u4f7f\u7528Pandas\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5Pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Pandas\u83b7\u53d6\u77e9\u9635\u7684\u5217<\/h3>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aPandas DataFrame matrix<\/code>\uff0c\u53ef\u4ee5\u901a\u8fc7 .iloc<\/code> \u5c5e\u6027\u6765\u83b7\u53d6\u67d0\u4e00\u5217\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
matrix = pd.DataFrame([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
\u83b7\u53d6\u7b2c1\u5217<\/strong><\/h2>\n
column_1 = matrix.iloc[:, 0]<\/p>\n
print("Column 1:")<\/p>\n
print(column_1)<\/p>\n
\u83b7\u53d6\u7b2c2\u5217<\/strong><\/h2>\n
column_2 = matrix.iloc[:, 1]<\/p>\n
print("Column 2:")<\/p>\n
print(column_2)<\/p>\n
\u83b7\u53d6\u7b2c3\u5217<\/strong><\/h2>\n
column_3 = matrix.iloc[:, 2]<\/p>\n
print("Column 3:")<\/p>\n
print(column_3)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cmatrix.iloc[:, i]<\/code> \u8868\u793a\u83b7\u53d6\u77e9\u9635\u7684\u7b2c i<\/code> \u5217\u3002:<\/code> \u8868\u793a\u9009\u62e9\u6240\u6709\u884c\uff0ci<\/code> \u8868\u793a\u9009\u62e9\u7b2c i<\/code> \u5217\u3002<\/p>\n<\/p>\n
\u5176\u4ed6\u65b9\u6cd5<\/h3>\n<\/p>\n
\u9664\u4e86\u4e0a\u8ff0\u65b9\u6cd5\u5916\uff0c\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u83b7\u53d6\u77e9\u9635\u7684\u5217\uff0c\u6bd4\u5982\u4f7f\u7528 zip<\/code> \u51fd\u6570\u7b49\u3002<\/p>\n<\/p>\n
\u4f7f\u7528zip\u51fd\u6570\u83b7\u53d6\u77e9\u9635\u7684\u5217<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528 zip<\/code> \u51fd\u6570\u6765\u83b7\u53d6\u77e9\u9635\u7684\u5217\u3002zip<\/code> \u51fd\u6570\u53ef\u4ee5\u5c06\u591a\u4e2a\u53ef\u8fed\u4ee3\u5bf9\u8c61\u6253\u5305\u6210\u4e00\u4e2a\u5143\u7ec4\u7684\u8fed\u4ee3\u5668\uff0c\u7136\u540e\u53ef\u4ee5\u4f7f\u7528 *<\/code> \u64cd\u4f5c\u7b26\u5c06\u77e9\u9635\u89e3\u5305\u4f20\u9012\u7ed9 zip<\/code> \u51fd\u6570\uff0c\u4ece\u800c\u83b7\u53d6\u77e9\u9635\u7684\u5217\u3002<\/p>\n<\/p>\n
matrix = [<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
]<\/p>\n
\u83b7\u53d6\u77e9\u9635\u7684\u5217<\/strong><\/h2>\n
columns = list(zip(*matrix))<\/p>\n
\u83b7\u53d6\u7b2c1\u5217<\/strong><\/h2>\n
column_1 = columns[0]<\/p>\n
print("Column 1:", column_1)<\/p>\n
\u83b7\u53d6\u7b2c2\u5217<\/strong><\/h2>\n
column_2 = columns[1]<\/p>\n
print("Column 2:", column_2)<\/p>\n
\u83b7\u53d6\u7b2c3\u5217<\/strong><\/h2>\n
column_3 = columns[2]<\/p>\n
print("Column 3:", column_3)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0czip(*matrix)<\/code> \u5c06\u77e9\u9635\u7684\u884c\u89e3\u5305\u5e76\u4f20\u9012\u7ed9 zip<\/code> \u51fd\u6570\uff0c\u4ece\u800c\u5c06\u884c\u8f6c\u7f6e\u6210\u5217\uff0c\u7136\u540e\u901a\u8fc7\u7d22\u5f15\u83b7\u53d6\u76f8\u5e94\u7684\u5217\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u83b7\u53d6\u77e9\u9635\u7684\u5217\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u5217\u8868\u89e3\u6790\u3001NumPy\u5e93\u3001Pandas\u5e93\u548c zip<\/code> \u51fd\u6570\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u6570\u636e\u7ed3\u6784\u3002<\/p>\n<\/p>\n
\u5217\u8868\u89e3\u6790<\/strong>\uff1a\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5d4c\u5957\u5217\u8868\u77e9\u9635\uff0c\u64cd\u4f5c\u7b80\u6d01\u4e14\u9ad8\u6548\u3002<\/p>\n<\/p>\n
NumPy\u5e93<\/strong>\uff1a\u9002\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u9700\u6c42\u8f83\u9ad8\u7684\u573a\u666f\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u5bf9\u8c61\u548c\u4e30\u5bcc\u7684\u51fd\u6570\u5e93\u3002<\/p>\n<\/p>\n
Pandas\u5e93<\/strong>\uff1a\u9002\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u9700\u6c42\u8f83\u9ad8\u7684\u573a\u666f\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684DataFrame\u5bf9\u8c61\u548c\u4e30\u5bcc\u7684\u51fd\u6570\u5e93\u3002<\/p>\n<\/p>\n
zip\u51fd\u6570<\/strong>\uff1a\u9002\u7528\u4e8e\u7b80\u5355\u77e9\u9635\u7684\u884c\u5217\u8f6c\u6362\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u83b7\u53d6\u77e9\u9635\u7684\u5217\u5e76\u8fdb\u884c\u540e\u7eed\u7684\u64cd\u4f5c\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
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