{"id":1104328,"date":"2025-01-08T16:19:42","date_gmt":"2025-01-08T08:19:42","guid":{"rendered":""},"modified":"2025-01-08T16:19:45","modified_gmt":"2025-01-08T08:19:45","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e5%88%97%e6%8f%90%e5%8f%96%e5%87%ba%e6%9d%a5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1104328.html","title":{"rendered":"python\u5982\u4f55\u5c06\u5217\u63d0\u53d6\u51fa\u6765"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25065736\/8e7270d2-7e77-46b0-b1df-392e0aeb8ca5.webp\" alt=\"python\u5982\u4f55\u5c06\u5217\u63d0\u53d6\u51fa\u6765\" \/><\/p>\n<p><p> <strong>Python\u63d0\u53d6\u5217\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5982\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4ee5\u53ca\u4f7f\u7528\u6807\u51c6\u7684Python\u5217\u8868\u5904\u7406\u65b9\u6cd5\u7b49\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4f7f\u7528\u6807\u51c6\u7684Python\u5217\u8868\u5904\u7406\u65b9\u6cd5<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u4f7f\u7528Pandas\u5e93\u6765\u63d0\u53d6\u5217\u3002<\/p>\n<\/p>\n<p><p>\u4f7f\u7528Pandas\u5e93\u63d0\u53d6\u5217\u662f\u6700\u5e38\u89c1\u548c\u65b9\u4fbf\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u7ed3\u6784\u548c\u51fd\u6570\u6765\u7b80\u5316\u6570\u636e\u5904\u7406\u8fc7\u7a0b\u3002\u901a\u8fc7\u4f7f\u7528Pandas\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u4ece\u6570\u636e\u6846\u4e2d\u63d0\u53d6\u4e00\u5217\u6216\u591a\u5217\u6570\u636e\uff0c\u5e76\u5bf9\u5176\u8fdb\u884c\u5404\u79cd\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u4f7f\u7528Pandas\u63d0\u53d6\u5217<\/p>\n<\/p>\n<p><p>Pandas\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u7279\u522b\u662f\u5bf9\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\u3002Pandas\u63d0\u4f9b\u4e86DataFrame\u548cSeries\u4e24\u79cd\u6570\u636e\u7ed3\u6784\uff0cDataFrame\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u8868\u683c\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u8868\u683c\uff0c\u800cSeries\u662f\u4e00\u7ef4\u7684\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u5217\u6570\u636e\u3002\u901a\u8fc7\u8fd9\u4e9b\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u63d0\u53d6\u548c\u64cd\u4f5c\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h3>1.1\u3001\u5b89\u88c5Pandas<\/h3>\n<\/p>\n<p><p>\u5728\u4f7f\u7528Pandas\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\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-sh\">pip install pandas<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>1.2\u3001\u5bfc\u5165Pandas\u5e93<\/h3>\n<\/p>\n<p><p>\u5728\u5b89\u88c5\u4e86Pandas\u5e93\u4e4b\u540e\uff0c\u9700\u8981\u5148\u5bfc\u5165Pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>1.3\u3001\u8bfb\u53d6\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u5728\u63d0\u53d6\u5217\u4e4b\u524d\uff0c\u9700\u8981\u5148\u8bfb\u53d6\u6570\u636e\u3002Pandas\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u4ee5\u4e0b\u662f\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">df = pd.read_csv(&#39;data.csv&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>1.4\u3001\u63d0\u53d6\u5355\u5217<\/h3>\n<\/p>\n<p><p>\u63d0\u53d6\u5355\u5217\u6570\u636e\u975e\u5e38\u7b80\u5355\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame\u7684\u5217\u540d\u4f5c\u4e3a\u7d22\u5f15\u6765\u63d0\u53d6\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6709\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684\u6570\u636e\u6846\uff0c\u6570\u636e\u6846\u7684\u5217\u540d\u5206\u522b\u4e3a&quot;Name&quot;\u3001&quot;Math&quot;\u3001&quot;English&quot;\u3001&quot;Science&quot;\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u63d0\u53d6&quot;Math&quot;\u5217\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">math_scores = df[&#39;Math&#39;]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u63d0\u53d6\u51fa\u6765\u7684<code>math_scores<\/code>\u662f\u4e00\u4e2aSeries\u5bf9\u8c61\uff0c\u5305\u542b\u4e86\u6240\u6709\u5b66\u751f\u7684\u6570\u5b66\u6210\u7ee9\u3002<\/p>\n<\/p>\n<p><h3>1.5\u3001\u63d0\u53d6\u591a\u5217<\/h3>\n<\/p>\n<p><p>\u5982\u679c\u9700\u8981\u63d0\u53d6\u591a\u5217\u6570\u636e\uff0c\u53ef\u4ee5\u5c06\u5217\u540d\u653e\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u5e76\u5c06\u5217\u8868\u4f20\u9012\u7ed9DataFrame\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u63d0\u53d6&quot;Math&quot;\u548c&quot;English&quot;\u4e24\u5217\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">math_english_scores = df[[&#39;Math&#39;, &#39;English&#39;]]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u63d0\u53d6\u51fa\u6765\u7684<code>math_english_scores<\/code>\u662f\u4e00\u4e2aDataFrame\u5bf9\u8c61\uff0c\u5305\u542b\u4e86\u6240\u6709\u5b66\u751f\u7684\u6570\u5b66\u548c\u82f1\u8bed\u6210\u7ee9\u3002<\/p>\n<\/p>\n<p><h3>1.6\u3001\u4f7f\u7528iloc\u548cloc\u65b9\u6cd5<\/h3>\n<\/p>\n<p><p>\u9664\u4e86\u76f4\u63a5\u4f7f\u7528\u5217\u540d\u4f5c\u4e3a\u7d22\u5f15\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528<code>iloc<\/code>\u548c<code>loc<\/code>\u65b9\u6cd5\u6765\u63d0\u53d6\u5217\u6570\u636e\u3002<code>iloc<\/code>\u65b9\u6cd5\u662f\u57fa\u4e8e\u4f4d\u7f6e\u7d22\u5f15\u6765\u63d0\u53d6\u6570\u636e\uff0c\u800c<code>loc<\/code>\u65b9\u6cd5\u662f\u57fa\u4e8e\u6807\u7b7e\u7d22\u5f15\u6765\u63d0\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n<p><p>\u4f8b\u5982\uff0c\u4f7f\u7528<code>iloc<\/code>\u65b9\u6cd5\u63d0\u53d6\u7b2c1\u5217\uff08\u4ece0\u5f00\u59cb\u8ba1\u6570\uff09\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">math_scores = df.iloc[:, 1]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4f7f\u7528<code>loc<\/code>\u65b9\u6cd5\u63d0\u53d6&quot;Math&quot;\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">math_scores = df.loc[:, &#39;Math&#39;]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528NumPy\u63d0\u53d6\u5217<\/p>\n<\/p>\n<p><p>NumPy\u662fPython\u4e2d\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u7279\u522b\u662f\u5bf9\u4e8e\u5904\u7406\u6570\u503c\u6570\u636e\u3002NumPy\u63d0\u4f9b\u4e86\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61ndarray\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><h3>2.1\u3001\u5b89\u88c5NumPy<\/h3>\n<\/p>\n<p><p>\u5728\u4f7f\u7528NumPy\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86NumPy\u5e93\u3002\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-sh\">pip install numpy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2.2\u3001\u5bfc\u5165NumPy\u5e93<\/h3>\n<\/p>\n<p><p>\u5728\u5b89\u88c5\u4e86NumPy\u5e93\u4e4b\u540e\uff0c\u9700\u8981\u5148\u5bfc\u5165NumPy\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5bfc\u5165NumPy\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2.3\u3001\u521b\u5efaNumPy\u6570\u7ec4<\/h3>\n<\/p>\n<p><p>\u5728\u63d0\u53d6\u5217\u4e4b\u524d\uff0c\u9700\u8981\u5148\u521b\u5efa\u4e00\u4e2aNumPy\u6570\u7ec4\u3002\u53ef\u4ee5\u4f7f\u7528NumPy\u7684array\u51fd\u6570\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684\u4e8c\u7ef4\u6570\u7ec4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = np.array([<\/p>\n<p>    [&#39;Alice&#39;, 85, 90, 95],<\/p>\n<p>    [&#39;Bob&#39;, 75, 80, 85],<\/p>\n<p>    [&#39;Charlie&#39;, 95, 85, 90]<\/p>\n<p>])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2.4\u3001\u63d0\u53d6\u5217<\/h3>\n<\/p>\n<p><p>\u63d0\u53d6\u5217\u6570\u636e\u53ef\u4ee5\u4f7f\u7528NumPy\u6570\u7ec4\u7684\u5207\u7247\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u63d0\u53d6\u7b2c2\u5217\uff08\u4ece0\u5f00\u59cb\u8ba1\u6570\uff09\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">math_scores = data[:, 1]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u63d0\u53d6\u51fa\u6765\u7684<code>math_scores<\/code>\u662f\u4e00\u4e2aNumPy\u6570\u7ec4\uff0c\u5305\u542b\u4e86\u6240\u6709\u5b66\u751f\u7684\u6570\u5b66\u6210\u7ee9\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u4f7f\u7528\u6807\u51c6Python\u5217\u8868\u63d0\u53d6\u5217<\/p>\n<\/p>\n<p><p>\u9664\u4e86\u4f7f\u7528Pandas\u548cNumPy\u5e93\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528\u6807\u51c6\u7684Python\u5217\u8868\u6765\u63d0\u53d6\u5217\u6570\u636e\u3002\u867d\u7136\u8fd9\u79cd\u65b9\u6cd5\u76f8\u5bf9\u8f83\u7e41\u7410\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4ecd\u7136\u6709\u6548\u3002<\/p>\n<\/p>\n<p><h3>3.1\u3001\u521b\u5efa\u5217\u8868<\/h3>\n<\/p>\n<p><p>\u5728\u63d0\u53d6\u5217\u4e4b\u524d\uff0c\u9700\u8981\u5148\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u6570\u636e\u7684\u5217\u8868\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684\u5217\u8868\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = [<\/p>\n<p>    [&#39;Alice&#39;, 85, 90, 95],<\/p>\n<p>    [&#39;Bob&#39;, 75, 80, 85],<\/p>\n<p>    [&#39;Charlie&#39;, 95, 85, 90]<\/p>\n<p>]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3.2\u3001\u63d0\u53d6\u5217<\/h3>\n<\/p>\n<p><p>\u63d0\u53d6\u5217\u6570\u636e\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u63d0\u53d6\u7b2c2\u5217\uff08\u4ece0\u5f00\u59cb\u8ba1\u6570\uff09\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">math_scores = [row[1] for row in data]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u63d0\u53d6\u51fa\u6765\u7684<code>math_scores<\/code>\u662f\u4e00\u4e2a\u5217\u8868\uff0c\u5305\u542b\u4e86\u6240\u6709\u5b66\u751f\u7684\u6570\u5b66\u6210\u7ee9\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u603b\u7ed3<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u53ef\u4ee5\u770b\u5230\u63d0\u53d6\u5217\u6570\u636e\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528NumPy\u5e93\u4ee5\u53ca\u4f7f\u7528\u6807\u51c6\u7684Python\u5217\u8868\u5904\u7406\u65b9\u6cd5\u3002<strong>\u5176\u4e2d\uff0cPandas\u5e93\u662f\u6700\u5e38\u7528\u548c\u65b9\u4fbf\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\uff0c\u800cNumPy\u5e93\u9002\u7528\u4e8e\u5904\u7406\u6570\u503c\u6570\u636e<\/strong>\u3002\u6807\u51c6\u7684Python\u5217\u8868\u5904\u7406\u65b9\u6cd5\u867d\u7136\u76f8\u5bf9\u7e41\u7410\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4ecd\u7136\u6709\u6548\u3002\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6570\u636e\u683c\u5f0f\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n<p><p>\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u90fd\u9700\u8981\u6ce8\u610f\u6570\u636e\u7684\u683c\u5f0f\u548c\u7ed3\u6784\uff0c\u786e\u4fdd\u63d0\u53d6\u7684\u6570\u636e\u51c6\u786e\u65e0\u8bef\u3002\u540c\u65f6\uff0c\u5408\u7406\u4f7f\u7528\u6570\u636e\u5904\u7406\u5e93\u548c\u51fd\u6570\uff0c\u53ef\u4ee5\u7b80\u5316\u4ee3\u7801\uff0c\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u7ef4\u62a4\u6027\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6570\u636e\u7279\u70b9\u7075\u6d3b\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u4ee5\u8fbe\u5230\u6700\u4f73\u7684\u6570\u636e\u5904\u7406\u6548\u679c\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u6570\u636e\uff1f<\/strong><br \/>\u8981\u5728Python\u4e2d\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3002\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u3002\u7136\u540e\uff0c\u4f7f\u7528<code>read_csv()<\/code>\u51fd\u6570\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\uff0c\u5e76\u901a\u8fc7\u5217\u540d\u6216\u5217\u7d22\u5f15\u9009\u62e9\u6240\u9700\u7684\u5217\u3002\u4f8b\u5982\uff0c<code>df[&#39;\u5217\u540d&#39;]<\/code>\u53ef\u4ee5\u63d0\u53d6\u540d\u4e3a\u201c\u5217\u540d\u201d\u7684\u5217\uff0c\u800c<code>df.iloc[:, [\u7d22\u5f15]]<\/code>\u5219\u53ef\u4ee5\u6839\u636e\u7d22\u5f15\u63d0\u53d6\u5217\u3002<\/p>\n<p><strong>\u5728\u63d0\u53d6\u5217\u65f6\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong><br \/>\u5728\u63d0\u53d6\u5217\u65f6\uff0c\u5904\u7406\u7f3a\u5931\u503c\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u8003\u8651\u56e0\u7d20\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u7684<code>dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528<code>fillna()<\/code>\u65b9\u6cd5\u66ff\u6362\u7f3a\u5931\u503c\u4e3a\u7279\u5b9a\u503c\uff08\u59820\u6216\u5747\u503c\uff09\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n<p><strong>\u662f\u5426\u53ef\u4ee5\u63d0\u53d6\u591a\u5217\u7684\u6570\u636e\uff1f\u5982\u679c\u53ef\u4ee5\uff0c\u5e94\u8be5\u5982\u4f55\u64cd\u4f5c\uff1f<\/strong><br \/>\u63d0\u53d6\u591a\u5217\u6570\u636e\u975e\u5e38\u7b80\u5355\u3002\u5728Pandas\u4e2d\uff0c\u53ef\u4ee5\u5c06\u5217\u540d\u653e\u5165\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u5982<code>df[[&#39;\u5217\u540d1&#39;, &#39;\u5217\u540d2&#39;]]<\/code>\uff0c\u8fd9\u6837\u5c31\u80fd\u540c\u65f6\u63d0\u53d6\u201c\u5217\u540d1\u201d\u548c\u201c\u5217\u540d2\u201d\u8fd9\u4e24\u5217\u7684\u6570\u636e\u3002\u5982\u679c\u9700\u8981\u63d0\u53d6\u8fde\u7eed\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528<code>iloc<\/code>\u65b9\u6cd5\uff0c\u4f8b\u5982<code>df.iloc[:, 1:3]<\/code>\u5c06\u63d0\u53d6\u4ece\u7d22\u5f151\u52302\u7684\u5217\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u63d0\u53d6\u5217\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5982\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4ee5\u53ca\u4f7f\u7528\u6807\u51c6\u7684Python\u5217\u8868\u5904\u7406\u65b9 [&hellip;]","protected":false},"author":3,"featured_media":1104335,"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\/1104328"}],"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=1104328"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1104328\/revisions"}],"predecessor-version":[{"id":1104338,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1104328\/revisions\/1104338"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1104335"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1104328"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1104328"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1104328"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}