{"id":1145456,"date":"2025-01-08T23:08:22","date_gmt":"2025-01-08T15:08:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1145456.html"},"modified":"2025-01-08T23:08:25","modified_gmt":"2025-01-08T15:08:25","slug":"python%e4%ba%8c%e4%bd%8d%e6%95%b0%e7%bb%84%e5%a6%82%e4%bd%95%e5%8f%96%e8%a1%8c%e6%95%b0%e6%8d%ae%e5%ba%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1145456.html","title":{"rendered":"python\u4e8c\u4f4d\u6570\u7ec4\u5982\u4f55\u53d6\u884c\u6570\u636e\u5e93"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24182027\/e818338b-a90a-46fe-9436-52b8761871e7.webp\" alt=\"python\u4e8c\u4f4d\u6570\u7ec4\u5982\u4f55\u53d6\u884c\u6570\u636e\u5e93\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u4ece\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\u4e2d\u63d0\u53d6\u884c\u6570\u636e\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528\u7d22\u5f15\u3001\u5207\u7247\u3001numpy\u5e93\u3001pandas\u5e93\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u793a\u4f8b\u4ee3\u7801\u3002<\/strong><\/p>\n<\/p>\n<p><h2>\u4e00\u3001\u4f7f\u7528\u7d22\u5f15\u548c\u5207\u7247<\/h2>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u7d22\u5f15\u548c\u5207\u7247\u6765\u63d0\u53d6\u4e8c\u7ef4\u6570\u7ec4\u7684\u884c\u6570\u636e\u3002\u4e8c\u7ef4\u6570\u7ec4\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u5d4c\u5957\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<p><h3>1.1 \u4f7f\u7528\u7d22\u5f15\u63d0\u53d6\u5355\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u901a\u8fc7\u7d22\u5f15\u6765\u63d0\u53d6\u4e8c\u7ef4\u6570\u7ec4\u4e2d\u7684\u5355\u884c\u6570\u636e\u662f\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/p>\n<p>array = [<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9]<\/p>\n<p>]<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e8c\u884c\u6570\u636e<\/strong><\/h2>\n<p>second_row = array[1]<\/p>\n<p>print(second_row)  # \u8f93\u51fa: [4, 5, 6]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>1.2 \u4f7f\u7528\u5207\u7247\u63d0\u53d6\u591a\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u5207\u7247\u64cd\u4f5c\u53ef\u4ee5\u7528\u4e8e\u63d0\u53d6\u591a\u4e2a\u8fde\u7eed\u7684\u884c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/p>\n<p>array = [<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9],<\/p>\n<p>    [10, 11, 12]<\/p>\n<p>]<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e00\u884c\u5230\u7b2c\u4e09\u884c\u7684\u6570\u636e<\/strong><\/h2>\n<p>subset = array[0:3]<\/p>\n<p>print(subset)  # \u8f93\u51fa: [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e8c\u3001\u4f7f\u7528Numpy\u5e93<\/h2>\n<\/p>\n<p><p>Numpy\u662fPython\u4e2d\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u4f7f\u7528Numpy\u53ef\u4ee5\u66f4\u52a0\u65b9\u4fbf\u548c\u9ad8\u6548\u5730\u5904\u7406\u4e8c\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n<p><h3>2.1 \u5b89\u88c5Numpy<\/h3>\n<\/p>\n<p><p>\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5Numpy\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\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><h3>2.2 \u4f7f\u7528Numpy\u63d0\u53d6\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u4f7f\u7528Numpy\u6570\u7ec4\u53ef\u4ee5\u65b9\u4fbf\u5730\u63d0\u53d6\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n<p>array = np.array([<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9]<\/p>\n<p>])<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e8c\u884c\u6570\u636e<\/strong><\/h2>\n<p>second_row = array[1, :]<\/p>\n<p>print(second_row)  # \u8f93\u51fa: [4 5 6]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2.3 \u4f7f\u7528Numpy\u5207\u7247\u63d0\u53d6\u591a\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>Numpy\u6570\u7ec4\u652f\u6301\u5207\u7247\u64cd\u4f5c\uff0c\u53ef\u4ee5\u63d0\u53d6\u591a\u4e2a\u8fde\u7eed\u7684\u884c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n<p>array = np.array([<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9],<\/p>\n<p>    [10, 11, 12]<\/p>\n<p>])<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e00\u884c\u5230\u7b2c\u4e09\u884c\u7684\u6570\u636e<\/strong><\/h2>\n<p>subset = array[0:3, :]<\/p>\n<p>print(subset)  # \u8f93\u51fa: [[ 1  2  3]<\/p>\n<p>               #       [ 4  5  6]<\/p>\n<p>               #       [ 7  8  9]]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e09\u3001\u4f7f\u7528Pandas\u5e93<\/h2>\n<\/p>\n<p><p>Pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86DataFrame\u7ed3\u6784\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u64cd\u4f5c\u4e8c\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n<p><h3>3.1 \u5b89\u88c5Pandas<\/h3>\n<\/p>\n<p><p>\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5Pandas\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install pandas<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3.2 \u4f7f\u7528Pandas\u63d0\u53d6\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u4f7f\u7528Pandas\u7684DataFrame\u7ed3\u6784\u53ef\u4ee5\u65b9\u4fbf\u5730\u63d0\u53d6\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n<p>array = [<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9]<\/p>\n<p>]<\/p>\n<h2><strong>\u5c06\u4e8c\u7ef4\u6570\u7ec4\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n<p>df = pd.DataFrame(array)<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e8c\u884c\u6570\u636e<\/strong><\/h2>\n<p>second_row = df.iloc[1]<\/p>\n<p>print(second_row)  # \u8f93\u51fa: 0    4<\/p>\n<p>                   #       1    5<\/p>\n<p>                   #       2    6<\/p>\n<p>                   #       Name: 1, dtype: int64<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3.3 \u4f7f\u7528Pandas\u5207\u7247\u63d0\u53d6\u591a\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>Pandas\u652f\u6301\u901a\u8fc7\u5207\u7247\u64cd\u4f5c\u63d0\u53d6\u591a\u4e2a\u8fde\u7eed\u7684\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n<p>array = [<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9],<\/p>\n<p>    [10, 11, 12]<\/p>\n<p>]<\/p>\n<h2><strong>\u5c06\u4e8c\u7ef4\u6570\u7ec4\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n<p>df = pd.DataFrame(array)<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e00\u884c\u5230\u7b2c\u4e09\u884c\u7684\u6570\u636e<\/strong><\/h2>\n<p>subset = df.iloc[0:3]<\/p>\n<p>print(subset)  # \u8f93\u51fa:    0  1  2<\/p>\n<p>               #       0  1  2  3<\/p>\n<p>               #       1  4  5  6<\/p>\n<p>               #       2  7  8  9<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u56db\u3001\u901a\u8fc7\u5217\u8868\u89e3\u6790\u63d0\u53d6\u884c\u6570\u636e<\/h2>\n<\/p>\n<p><p>\u5217\u8868\u89e3\u6790\u662f\u4e00\u79cd\u7b80\u6d01\u7684\u65b9\u5f0f\u6765\u63d0\u53d6\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h3>4.1 \u63d0\u53d6\u5355\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u901a\u8fc7\u5217\u8868\u89e3\u6790\u53ef\u4ee5\u63d0\u53d6\u7279\u5b9a\u7684\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/p>\n<p>array = [<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9]<\/p>\n<p>]<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e8c\u884c\u6570\u636e<\/strong><\/h2>\n<p>second_row = [row for i, row in enumerate(array) if i == 1]<\/p>\n<p>print(second_row)  # \u8f93\u51fa: [[4, 5, 6]]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>4.2 \u63d0\u53d6\u591a\u884c\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u5217\u8868\u89e3\u6790\u4e5f\u53ef\u4ee5\u7528\u4e8e\u63d0\u53d6\u591a\u4e2a\u8fde\u7eed\u7684\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u793a\u4f8b\u4e8c\u7ef4\u6570\u7ec4<\/p>\n<p>array = [<\/p>\n<p>    [1, 2, 3],<\/p>\n<p>    [4, 5, 6],<\/p>\n<p>    [7, 8, 9],<\/p>\n<p>    [10, 11, 12]<\/p>\n<p>]<\/p>\n<h2><strong>\u63d0\u53d6\u7b2c\u4e00\u884c\u5230\u7b2c\u4e09\u884c\u7684\u6570\u636e<\/strong><\/h2>\n<p>subset = [row for i, row in enumerate(array) if 0 &lt;= i &lt; 3]<\/p>\n<p>print(subset)  # \u8f93\u51fa: [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e94\u3001\u603b\u7ed3<\/h2>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u4ece\u4e8c\u7ef4\u6570\u7ec4\u4e2d\u63d0\u53d6\u884c\u6570\u636e\uff0c\u5305\u62ec\u4f7f\u7528\u7d22\u5f15\u548c\u5207\u7247\u3001Numpy\u5e93\u3001Pandas\u5e93\u548c\u5217\u8868\u89e3\u6790\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u7f3a\u70b9\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u7b80\u6d01\u6027\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n<p><h3>5.1 \u7d22\u5f15\u548c\u5207\u7247<\/h3>\n<\/p>\n<p><p><strong>\u4f18\u70b9\uff1a<\/strong> \u7b80\u5355\u76f4\u89c2\uff0c\u9002\u5408\u5c0f\u89c4\u6a21\u6570\u636e\u3002<\/p>\n<p><strong>\u7f3a\u70b9\uff1a<\/strong> \u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u548c\u590d\u6742\u64cd\u4f5c\u4e0d\u591f\u9ad8\u6548\u3002<\/p>\n<\/p>\n<p><h3>5.2 Numpy\u5e93<\/h3>\n<\/p>\n<p><p><strong>\u4f18\u70b9\uff1a<\/strong> \u9ad8\u6548\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\uff0c\u63d0\u4f9b\u4e30\u5bcc\u7684\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\u3002<\/p>\n<p><strong>\u7f3a\u70b9\uff1a<\/strong> \u9700\u8981\u989d\u5916\u5b89\u88c5\u5e93\uff0c\u4ee3\u7801\u590d\u6742\u5ea6\u7a0d\u9ad8\u3002<\/p>\n<\/p>\n<p><h3>5.3 Pandas\u5e93<\/h3>\n<\/p>\n<p><p><strong>\u4f18\u70b9\uff1a<\/strong> \u9002\u5408\u6570\u636e\u5206\u6790\u548c\u5904\u7406\uff0c\u63d0\u4f9b\u5f3a\u5927\u7684DataFrame\u7ed3\u6784\u3002<\/p>\n<p><strong>\u7f3a\u70b9\uff1a<\/strong> \u9700\u8981\u989d\u5916\u5b89\u88c5\u5e93\uff0c\u9002\u5408\u7279\u5b9a\u573a\u666f\u3002<\/p>\n<\/p>\n<p><h3>5.4 \u5217\u8868\u89e3\u6790<\/h3>\n<\/p>\n<p><p><strong>\u4f18\u70b9\uff1a<\/strong> \u7b80\u6d01\u4f18\u96c5\uff0c\u9002\u5408\u4e2d\u5c0f\u89c4\u6a21\u6570\u636e\u3002<\/p>\n<p><strong>\u7f3a\u70b9\uff1a<\/strong> \u5bf9\u4e8e\u590d\u6742\u64cd\u4f5c\u548c\u5927\u89c4\u6a21\u6570\u636e\u6548\u7387\u4e0d\u9ad8\u3002<\/p>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u5982\u679c\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u8fdb\u884c\u590d\u6742\u7684\u6570\u7ec4\u64cd\u4f5c\uff0c\u63a8\u8350\u4f7f\u7528Numpy\u6216Pandas\u5e93\uff1b\u5982\u679c\u53ea\u662f\u8fdb\u884c\u7b80\u5355\u7684\u884c\u6570\u636e\u63d0\u53d6\uff0c\u53ef\u4ee5\u4f7f\u7528\u7d22\u5f15\u3001\u5207\u7247\u6216\u5217\u8868\u89e3\u6790\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u548c\u8bbf\u95ee\u4e8c\u7ef4\u6570\u7ec4\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\uff08list\uff09\u6765\u521b\u5efa\u4e8c\u7ef4\u6570\u7ec4\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5b9a\u4e49\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u5217\u8868\u7684\u5217\u8868\uff0c\u6bcf\u4e2a\u5185\u90e8\u5217\u8868\u4ee3\u8868\u4e00\u884c\u3002\u8981\u8bbf\u95ee\u67d0\u4e00\u884c\uff0c\u53ef\u4ee5\u4f7f\u7528\u7d22\u5f15\uff0c\u4f8b\u5982<code>array[row_index]<\/code>\uff0c\u5176\u4e2d<code>array<\/code>\u662f\u4e8c\u7ef4\u6570\u7ec4\u7684\u540d\u79f0\uff0c<code>row_index<\/code>\u662f\u884c\u7684\u7d22\u5f15\u503c\u3002<\/p>\n<p><strong>\u5982\u4f55\u5c06Python\u4e8c\u7ef4\u6570\u7ec4\u4e2d\u7684\u6570\u636e\u63d2\u5165\u5230\u6570\u636e\u5e93\u4e2d\uff1f<\/strong><br 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