{"id":995823,"date":"2024-12-27T09:08:05","date_gmt":"2024-12-27T01:08:05","guid":{"rendered":""},"modified":"2024-12-27T09:08:07","modified_gmt":"2024-12-27T01:08:07","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e7%ae%b1%e7%ba%bf%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/995823.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u7bb1\u7ebf\u56fe"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25072255\/d7e18b27-5dfd-4dfb-98f1-d2a8dae04176.webp\" alt=\"python\u5982\u4f55\u7ed8\u5236\u7bb1\u7ebf\u56fe\" \/><\/p>\n<p><p> \u5f00\u5934\u6bb5\u843d\uff1a<br \/>\u7ed8\u5236\u7bb1\u7ebf\u56fe\u662f\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u8fc7\u7a0b\u4e2d\u5e38\u7528\u7684\u6280\u672f\uff0c<strong>\u5728Python\u4e2d\uff0c\u7ed8\u5236\u7bb1\u7ebf\u56fe\u53ef\u4ee5\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Pandas\u7b49\u5e93\uff0c\u6b65\u9aa4\u5305\u62ec\u51c6\u5907\u6570\u636e\u3001\u9009\u62e9\u5e93\u3001\u521b\u5efa\u56fe\u5f62\u5bf9\u8c61\u3001\u914d\u7f6e\u56fe\u5f62\u5c5e\u6027\u3001\u5c55\u793a\u56fe\u5f62<\/strong>\u3002\u5176\u4e2d\uff0cSeaborn\u5e93\u5728\u7ed8\u5236\u7bb1\u7ebf\u56fe\u65b9\u9762\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u548c\u9ad8\u5c42\u7684\u63a5\u53e3\uff0c\u5e76\u4e14\u53ef\u4ee5\u8f7b\u677e\u4e0ePandas\u6570\u636e\u6846\u7ed3\u5408\u4f7f\u7528\uff0c\u4f7f\u5f97\u6570\u636e\u53ef\u89c6\u5316\u66f4\u52a0\u76f4\u89c2\u3001\u6613\u4e8e\u7406\u89e3\u3002\u4ee5Seaborn\u4e3a\u4f8b\uff0c\u7528\u6237\u53ea\u9700\u51e0\u884c\u4ee3\u7801\u5373\u53ef\u751f\u6210\u7f8e\u89c2\u7684\u7bb1\u7ebf\u56fe\uff0c\u4e14\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570\u8c03\u6574\u56fe\u5f62\u7684\u7ec6\u8282\uff0c\u5982\u989c\u8272\u3001\u6837\u5f0f\u3001\u7edf\u8ba1\u91cf\u663e\u793a\u7b49\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u7ed8\u5236\u7bb1\u7ebf\u56fe\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001MATPLOTLIB\u5e93\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/p>\n<\/p>\n<p><p>Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u548c\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u867d\u7136\u5b83\u9700\u8981\u66f4\u591a\u7684\u4ee3\u7801\u6765\u8bbe\u7f6e\u7ec6\u8282\uff0c\u4f46\u5b83\u63d0\u4f9b\u4e86\u975e\u5e38\u7075\u6d3b\u7684\u5b9a\u5236\u80fd\u529b\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5b89\u88c5\u548c\u5bfc\u5165Matplotlib<\/li>\n<\/ol>\n<p><p>\u5728\u5f00\u59cb\u7ed8\u5236\u7bb1\u7ebf\u56fe\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install matplotlib<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165\u8be5\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u51c6\u5907\u6570\u636e<\/li>\n<\/ol>\n<p><p>\u6570\u636e\u53ef\u4ee5\u662f\u4efb\u4f55\u5f62\u5f0f\u7684\u6570\u503c\u5217\u8868\u6216\u6570\u7ec4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u6570\u636e\u96c6\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = [20, 23, 22, 19, 22, 18, 20, 21, 23, 22, 24, 25, 27, 29, 30]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/li>\n<\/ol>\n<p><p>\u4f7f\u7528Matplotlib\u7ed8\u5236\u7bb1\u7ebf\u56fe\u4e3b\u8981\u901a\u8fc7<code>boxplot<\/code>\u51fd\u6570\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.boxplot(data)<\/p>\n<p>plt.title(&#39;Boxplot using Matplotlib&#39;)<\/p>\n<p>plt.xlabel(&#39;Sample&#39;)<\/p>\n<p>plt.ylabel(&#39;Values&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"4\">\n<li>\u81ea\u5b9a\u4e49\u7bb1\u7ebf\u56fe<\/li>\n<\/ol>\n<p><p>Matplotlib\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u8bb8\u591a\u53c2\u6570\u81ea\u5b9a\u4e49\u56fe\u5f62\uff0c\u5982\u8bbe\u7f6e\u989c\u8272\u3001\u663e\u793a\u7f51\u683c\u7b49\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">plt.boxplot(data, patch_artist=True, boxprops=dict(facecolor=&#39;skyblue&#39;))<\/p>\n<p>plt.grid(True)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e8c\u3001SEABORN\u5e93\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/p>\n<\/p>\n<p><p>Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u5b83\u7b80\u5316\u4e86\u590d\u6742\u56fe\u5f62\u7684\u7ed8\u5236\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/li>\n<\/ol>\n<p><p>\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Seaborn\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install seaborn<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u7136\u540e\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165\u5b83\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import seaborn as sns<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u4f7f\u7528Seaborn\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/li>\n<\/ol>\n<p><p>Seaborn\u53ef\u4ee5\u4e0ePandas DataFrame\u65e0\u7f1d\u96c6\u6210\uff0c\u8fd9\u4f7f\u5f97\u7ed8\u5236\u56fe\u5f62\u66f4\u52a0\u4fbf\u6377\u3002\u4ee5\u4e0b\u662fSeaborn\u7ed8\u5236\u7bb1\u7ebf\u56fe\u7684\u57fa\u672c\u7528\u6cd5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame<\/strong><\/h2>\n<p>df = pd.DataFrame({&#39;Values&#39;: data})<\/p>\n<p>sns.boxplot(x=df[&#39;Values&#39;])<\/p>\n<p>plt.title(&#39;Boxplot using Seaborn&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u9ad8\u7ea7\u81ea\u5b9a\u4e49<\/li>\n<\/ol>\n<p><p>Seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u9009\u9879\u4ee5\u589e\u5f3a\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u6dfb\u52a0\u8272\u5e26\u3001\u8c03\u6574\u56fe\u5f62\u5927\u5c0f\u7b49\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">sns.boxplot(x=&#39;Values&#39;, data=df, palette=&#39;pastel&#39;)<\/p>\n<p>sns.despine(offset=10, trim=True)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e09\u3001PANDAS\u5e93\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/p>\n<\/p>\n<p><p>Pandas\u4e5f\u5177\u5907\u76f4\u63a5\u7ed8\u5236\u56fe\u5f62\u7684\u80fd\u529b\uff0c\u9002\u5408\u5feb\u901f\u63a2\u7d22\u6570\u636e\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5bfc\u5165Pandas<\/li>\n<\/ol>\n<p><p>\u786e\u4fdd\u5df2\u5b89\u88c5Pandas\uff0c\u7136\u540e\u5728\u811a\u672c\u4e2d\u5bfc\u5165\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u4f7f\u7528Pandas\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/li>\n<\/ol>\n<p><p>Pandas\u7684DataFrame\u5bf9\u8c61\u81ea\u5e26\u7ed8\u56fe\u65b9\u6cd5\uff0c\u53ef\u4ee5\u76f4\u63a5\u751f\u6210\u7bb1\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">df = pd.DataFrame(data, columns=[&#39;Values&#39;])<\/p>\n<p>df.plot.box()<\/p>\n<p>plt.title(&#39;Boxplot using Pandas&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u81ea\u5b9a\u4e49Pandas\u7bb1\u7ebf\u56fe<\/li>\n<\/ol>\n<p><p>\u867d\u7136Pandas\u7684\u7ed8\u56fe\u529f\u80fd\u4e0d\u5982Matplotlib\u548cSeaborn\u5f3a\u5927\uff0c\u4f46\u4ecd\u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684\u53c2\u6570\u8fdb\u884c\u4e00\u4e9b\u57fa\u672c\u7684\u81ea\u5b9a\u4e49\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">df.plot.box(color=dict(boxes=&#39;DarkGreen&#39;, whiskers=&#39;DarkOrange&#39;, medians=&#39;DarkBlue&#39;, caps=&#39;Gray&#39;))<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u56db\u3001\u7bb1\u7ebf\u56fe\u7684\u5b9e\u9645\u5e94\u7528<\/p>\n<\/p>\n<ol>\n<li>\u5f02\u5e38\u503c\u68c0\u6d4b<\/li>\n<\/ol>\n<p><p>\u7bb1\u7ebf\u56fe\u53ef\u4ee5\u6709\u6548\u5730\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u7684\u5f02\u5e38\u503c\u3002\u901a\u8fc7\u89c2\u5bdf\u56fe\u4e2d\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u548c\u5f02\u5e38\u70b9\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u66f4\u6df1\u5165\u7684\u5206\u6790\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u6570\u636e\u5206\u5e03\u6bd4\u8f83<\/li>\n<\/ol>\n<p><p>\u7bb1\u7ebf\u56fe\u9002\u5408\u6bd4\u8f83\u591a\u4e2a\u6570\u636e\u96c6\u7684\u5206\u5e03\u60c5\u51b5\u3002\u901a\u8fc7\u5e76\u6392\u7ed8\u5236\u591a\u4e2a\u7bb1\u7ebf\u56fe\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u4e0d\u540c\u6570\u636e\u96c6\u7684\u4e2d\u4f4d\u6570\u3001\u56db\u5206\u4f4d\u6570\u548c\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u51e0\u4e2a\u90e8\u5206\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPandas\u5e93\u7ed8\u5236\u7bb1\u7ebf\u56fe\u7684\u65b9\u6cd5\u3002<strong>\u9009\u62e9\u5408\u9002\u7684\u5e93\u53d6\u51b3\u4e8e\u5177\u4f53\u9700\u6c42\uff0c\u5982\u9700\u8981\u9ad8\u7ea7\u5b9a\u5236\u65f6\u53ef\u9009\u62e9Matplotlib\uff0c\u9700\u8981\u5feb\u901f\u7ed8\u56fe\u65f6\u53ef\u9009\u62e9Pandas\uff0c\u800c\u5bf9\u4e8e\u7f8e\u89c2\u4e14\u6613\u7528\u7684\u56fe\u5f62\u7ed8\u5236\u5219\u63a8\u8350\u4f7f\u7528Seaborn<\/strong>\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u5de5\u5177\uff0c\u7406\u89e3\u7bb1\u7ebf\u56fe\u7684\u57fa\u672c\u539f\u7406\u548c\u5e94\u7528\u573a\u666f\u90fd\u662f\u5341\u5206\u91cd\u8981\u7684\uff0c\u53ea\u6709\u8fd9\u6837\u624d\u80fd\u66f4\u597d\u5730\u5229\u7528\u8fd9\u4e9b\u5de5\u5177\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> 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