{"id":1069734,"date":"2025-01-08T10:51:41","date_gmt":"2025-01-08T02:51:41","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1069734.html"},"modified":"2025-01-08T10:51:43","modified_gmt":"2025-01-08T02:51:43","slug":"python%e8%a6%81%e5%a6%82%e4%bd%95%e7%94%a8%e4%ba%8e%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1069734.html","title":{"rendered":"python\u8981\u5982\u4f55\u7528\u4e8e\u6570\u636e\u5206\u6790"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25100650\/aae50d10-8b4a-494f-be1a-8467e5deb2d2.webp\" alt=\"python\u8981\u5982\u4f55\u7528\u4e8e\u6570\u636e\u5206\u6790\" \/><\/p>\n<p><p> <strong>Python\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u4f18\u52bf\u5305\u62ec\uff1a\u5e93\u7684\u4e30\u5bcc\u6027\u3001\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528\u3001\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u3001\u53ef\u89c6\u5316\u529f\u80fd\u3001\u793e\u533a\u652f\u6301\u3002<\/strong>\u5176\u4e2d\uff0c\u5e93\u7684\u4e30\u5bcc\u6027\u5c24\u4e3a\u5173\u952e\u3002Python\u751f\u6001\u7cfb\u7edf\u4e2d\u6709\u8bb8\u591a\u4e13\u95e8\u4e3a\u6570\u636e\u5206\u6790\u8bbe\u8ba1\u7684\u5e93\uff0c\u5982Pandas\u3001NumPy\u3001Matplotlib\u548cSeaborn\u7b49\uff0c\u4f7f\u5f97\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u53d8\u5f97\u66f4\u52a0\u9ad8\u6548\u548c\u4fbf\u6377\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecdPython\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u5e93\u7684\u4e30\u5bcc\u6027<\/p>\n<\/p>\n<p><p>Python\u4e4b\u6240\u4ee5\u5728\u6570\u636e\u5206\u6790\u9886\u57df\u5e7f\u53d7\u6b22\u8fce\uff0c\u4e3b\u8981\u662f\u56e0\u4e3a\u5b83\u62e5\u6709\u4e30\u5bcc\u7684\u5e93\uff0c\u8fd9\u4e9b\u5e93\u4e3a\u6570\u636e\u5206\u6790\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>Pandas<\/strong>\uff1aPandas\u5e93\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5982DataFrame\u548cSeries\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u7406\u3001\u5904\u7406\u548c\u5206\u6790\u3002Pandas\u7684\u529f\u80fd\u5305\u62ec\u6570\u636e\u8bfb\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u8f6c\u6362\u3001\u6570\u636e\u805a\u5408\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>NumPy<\/strong>\uff1aNumPy\u5e93\u662fPython\u4e2d\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\uff0c\u4ee5\u53ca\u5bf9\u6570\u7ec4\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\u7684\u51fd\u6570\u3002NumPy\u5728\u6570\u636e\u5206\u6790\u4e2d\u5e38\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u77e9\u9635\u8fd0\u7b97\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>Matplotlib\u548cSeaborn<\/strong>\uff1a\u8fd9\u4e24\u4e2a\u5e93\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u51e0\u4e4e\u53ef\u4ee5\u7ed8\u5236\u6240\u6709\u7c7b\u578b\u7684\u56fe\u8868\u3002Seaborn\u5219\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e8c\u3001\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528<\/p>\n<\/p>\n<p><p>Python\u7684\u8bed\u6cd5\u7b80\u6d01\u660e\u4e86\uff0c\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528\u3002\u5373\u4f7f\u662f\u6570\u636e\u5206\u6790\u7684\u65b0\u624b\uff0c\u4e5f\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u5b66\u4e60\u5feb\u901f\u4e0a\u624b\u3002Python\u793e\u533a\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6559\u7a0b\u3001\u6587\u6863\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u4f7f\u5f97\u5b66\u4e60\u548c\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u5bb9\u6613\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b<\/p>\n<\/p>\n<p><p>Python\u5728\u6570\u636e\u5904\u7406\u65b9\u9762\u62e5\u6709\u5f3a\u5927\u7684\u80fd\u529b\uff0c\u80fd\u591f\u5904\u7406\u5404\u79cd\u683c\u5f0f\u548c\u7c7b\u578b\u7684\u6570\u636e\u3002\u65e0\u8bba\u662fCSV\u6587\u4ef6\u3001Excel\u8868\u683c\u3001SQL\u6570\u636e\u5e93\uff0c\u8fd8\u662fJSON\u3001XML\u7b49\u683c\u5f0f\u7684\u6570\u636e\uff0cPython\u90fd\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u548c\u5904\u7406\u3002\u6b64\u5916\uff0cPython\u8fd8\u652f\u6301\u5927\u89c4\u6a21\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\uff0c\u9002\u7528\u4e8e\u5927\u6570\u636e\u5206\u6790\u573a\u666f\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u53ef\u89c6\u5316\u529f\u80fd<\/p>\n<\/p>\n<p><p>\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u73af\u8282\u3002Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u70ed\u529b\u56fe\u7b49\u3002\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u7684\u89c4\u5f8b\u548c\u8d8b\u52bf\uff0c\u5e2e\u52a9\u5206\u6790\u5e08\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u793e\u533a\u652f\u6301<\/p>\n<\/p>\n<p><p>Python\u62e5\u6709\u5e9e\u5927\u7684\u793e\u533a\uff0c\u793e\u533a\u4e2d\u6709\u5927\u91cf\u7684\u6570\u636e\u5206\u6790\u4e13\u5bb6\u548c\u7231\u597d\u8005\uff0c\u4ed6\u4eec\u4e0d\u65ad\u5f00\u53d1\u548c\u7ef4\u62a4\u5404\u79cd\u6570\u636e\u5206\u6790\u5e93\u548c\u5de5\u5177\u3002\u65e0\u8bba\u662f\u5728\u4f7f\u7528\u8fc7\u7a0b\u4e2d\u9047\u5230\u4ec0\u4e48\u95ee\u9898\uff0c\u90fd\u53ef\u4ee5\u901a\u8fc7\u793e\u533a\u83b7\u5f97\u5e2e\u52a9\u3002\u6b64\u5916\uff0c\u793e\u533a\u8fd8\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u5f00\u6e90\u9879\u76ee\u548c\u8d44\u6e90\uff0c\u53ef\u4ee5\u53c2\u8003\u548c\u501f\u9274\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u5e93\u7684\u4e30\u5bcc\u6027<\/h3>\n<\/p>\n<p><p>Python\u62e5\u6709\u4e30\u5bcc\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u8fd9\u4e9b\u5e93\u4e3a\u6570\u636e\u5206\u6790\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u652f\u6301\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u51e0\u4e2a\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\u3002<\/p>\n<\/p>\n<p><h4>1. Pandas<\/h4>\n<\/p>\n<p><p>Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002Pandas\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u662fDataFrame\u548cSeries\u3002DataFrame\u7c7b\u4f3c\u4e8eExcel\u4e2d\u7684\u8868\u683c\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u7406\u3001\u5904\u7406\u548c\u5206\u6790\u3002Series\u662f\u4e00\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u5b58\u50a8\u4e00\u7ec4\u6570\u636e\u3002<\/p>\n<\/p>\n<p><p>Pandas\u7684\u529f\u80fd\u975e\u5e38\u5f3a\u5927\uff0c\u5305\u62ec\u6570\u636e\u8bfb\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u8f6c\u6362\u3001\u6570\u636e\u805a\u5408\u7b49\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6CSV\u6587\u4ef6\u3001Excel\u8868\u683c\u3001SQL\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u7b5b\u9009\u3001\u6392\u5e8f\u3001\u5206\u7ec4\u3001\u805a\u5408\u7b49\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Pandas\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n<p>df = pd.read_csv(&#39;data.csv&#39;)<\/p>\n<h2><strong>\u67e5\u770b\u6570\u636e<\/strong><\/h2>\n<p>print(df.head())<\/p>\n<h2><strong>\u6570\u636e\u6e05\u6d17<\/strong><\/h2>\n<p>df.dropna(inplace=True)<\/p>\n<h2><strong>\u6570\u636e\u8f6c\u6362<\/strong><\/h2>\n<p>df[&#39;column&#39;] = df[&#39;column&#39;].astype(&#39;int&#39;)<\/p>\n<h2><strong>\u6570\u636e\u805a\u5408<\/strong><\/h2>\n<p>grouped = df.groupby(&#39;category&#39;).sum()<\/p>\n<p>print(grouped)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. NumPy<\/h4>\n<\/p>\n<p><p>NumPy\u662fPython\u4e2d\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\uff0c\u4ee5\u53ca\u5bf9\u6570\u7ec4\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\u7684\u51fd\u6570\u3002NumPy\u5728\u6570\u636e\u5206\u6790\u4e2d\u5e38\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u77e9\u9635\u8fd0\u7b97\u3002<\/p>\n<\/p>\n<p><p>NumPy\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u662fndarray\uff0c\u5b83\u662f\u4e00\u4e2a\u591a\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u5b58\u50a8\u4e00\u7ec4\u6570\u636e\u3002NumPy\u8fd8\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6570\u5b66\u51fd\u6570\uff0c\u53ef\u4ee5\u5bf9\u6570\u7ec4\u8fdb\u884c\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\uff0c\u5982\u52a0\u51cf\u4e58\u9664\u3001\u77e9\u9635\u8fd0\u7b97\u3001\u7edf\u8ba1\u8ba1\u7b97\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684NumPy\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u521b\u5efa\u6570\u7ec4<\/strong><\/h2>\n<p>arr = np.array([1, 2, 3, 4, 5])<\/p>\n<h2><strong>\u6570\u5b66\u8fd0\u7b97<\/strong><\/h2>\n<p>print(arr + 1)<\/p>\n<p>print(arr * 2)<\/p>\n<h2><strong>\u77e9\u9635\u8fd0\u7b97<\/strong><\/h2>\n<p>matrix = np.array([[1, 2], [3, 4]])<\/p>\n<p>result = np.dot(matrix, matrix)<\/p>\n<p>print(result)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3. Matplotlib\u548cSeaborn<\/h4>\n<\/p>\n<p><p>Matplotlib\u548cSeaborn\u662fPython\u4e2d\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3002Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u51e0\u4e4e\u53ef\u4ee5\u7ed8\u5236\u6240\u6709\u7c7b\u578b\u7684\u56fe\u8868\u3002Seaborn\u5219\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n<p><p>Matplotlib\u7684\u529f\u80fd\u975e\u5e38\u5f3a\u5927\uff0c\u53ef\u4ee5\u521b\u5efa\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u997c\u56fe\u3001\u76f4\u65b9\u56fe\u7b49\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002Seaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u590d\u6742\u7684\u56fe\u8868\uff0c\u5982\u5206\u7c7b\u6563\u70b9\u56fe\u3001\u7bb1\u7ebf\u56fe\u3001\u70ed\u529b\u56fe\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Matplotlib\u548cSeaborn\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import seaborn as sns<\/p>\n<h2><strong>\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n<p>plt.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25])<\/p>\n<p>plt.title(&#39;Line Chart&#39;)<\/p>\n<p>plt.xlabel(&#39;X-axis&#39;)<\/p>\n<p>plt.ylabel(&#39;Y-axis&#39;)<\/p>\n<p>plt.show()<\/p>\n<h2><strong>\u521b\u5efa\u5206\u7c7b\u6563\u70b9\u56fe<\/strong><\/h2>\n<p>sns.scatterplot(x=&#39;x&#39;, y=&#39;y&#39;, hue=&#39;category&#39;, data=df)<\/p>\n<p>plt.title(&#39;Scatter Plot&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e8c\u3001\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528<\/h3>\n<\/p>\n<p><p>Python\u7684\u8bed\u6cd5\u7b80\u6d01\u660e\u4e86\uff0c\u6613\u4e8e\u5b66\u4e60\u548c\u4f7f\u7528\u3002\u5373\u4f7f\u662f\u6570\u636e\u5206\u6790\u7684\u65b0\u624b\uff0c\u4e5f\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u5b66\u4e60\u5feb\u901f\u4e0a\u624b\u3002Python\u793e\u533a\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6559\u7a0b\u3001\u6587\u6863\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u4f7f\u5f97\u5b66\u4e60\u548c\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u5bb9\u6613\u3002<\/p>\n<\/p>\n<p><h4>1. \u7b80\u6d01\u7684\u8bed\u6cd5<\/h4>\n<\/p>\n<p><p>Python\u7684\u8bed\u6cd5\u975e\u5e38\u7b80\u6d01\u660e\u4e86\uff0c\u4ee3\u7801\u53ef\u8bfb\u6027\u9ad8\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Python\u4ee3\u7801\uff0c\u7528\u4e8e\u8ba1\u7b97\u5217\u8868\u4e2d\u6240\u6709\u5143\u7d20\u7684\u548c\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">numbers = [1, 2, 3, 4, 5]<\/p>\n<p>total = sum(numbers)<\/p>\n<p>print(total)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u79cd\u7b80\u6d01\u7684\u8bed\u6cd5\u4f7f\u5f97\u7f16\u5199\u548c\u9605\u8bfb\u4ee3\u7801\u53d8\u5f97\u975e\u5e38\u5bb9\u6613\uff0c\u7279\u522b\u662f\u5bf9\u4e8e\u521d\u5b66\u8005\u6765\u8bf4\u3002<\/p>\n<\/p>\n<p><h4>2. \u4e30\u5bcc\u7684\u8d44\u6e90<\/h4>\n<\/p>\n<p><p>Python\u793e\u533a\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6559\u7a0b\u3001\u6587\u6863\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u4f7f\u5f97\u5b66\u4e60\u548c\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u5bb9\u6613\u3002\u4f8b\u5982\uff0cPandas\u548cNumPy\u90fd\u6709\u8be6\u7ec6\u7684\u5b98\u65b9\u6587\u6863\uff0c\u4ecb\u7ecd\u4e86\u5e93\u7684\u529f\u80fd\u548c\u4f7f\u7528\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u7f51\u4e0a\u8fd8\u6709\u5927\u91cf\u7684\u535a\u5ba2\u3001\u89c6\u9891\u6559\u7a0b\u548c\u5f00\u6e90\u9879\u76ee\uff0c\u53ef\u4ee5\u53c2\u8003\u548c\u5b66\u4e60\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b<\/h3>\n<\/p>\n<p><p>Python\u5728\u6570\u636e\u5904\u7406\u65b9\u9762\u62e5\u6709\u5f3a\u5927\u7684\u80fd\u529b\uff0c\u80fd\u591f\u5904\u7406\u5404\u79cd\u683c\u5f0f\u548c\u7c7b\u578b\u7684\u6570\u636e\u3002\u65e0\u8bba\u662fCSV\u6587\u4ef6\u3001Excel\u8868\u683c\u3001SQL\u6570\u636e\u5e93\uff0c\u8fd8\u662fJSON\u3001XML\u7b49\u683c\u5f0f\u7684\u6570\u636e\uff0cPython\u90fd\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u548c\u5904\u7406\u3002\u6b64\u5916\uff0cPython\u8fd8\u652f\u6301\u5927\u89c4\u6a21\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\uff0c\u9002\u7528\u4e8e\u5927\u6570\u636e\u5206\u6790\u573a\u666f\u3002<\/p>\n<\/p>\n<p><h4>1. \u5904\u7406\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e<\/h4>\n<\/p>\n<p><p>Python\u53ef\u4ee5\u5904\u7406\u5404\u79cd\u683c\u5f0f\u548c\u7c7b\u578b\u7684\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6CSV\u6587\u4ef6\u3001Excel\u8868\u683c\u3001SQL\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n<p>df = pd.read_csv(&#39;data.csv&#39;)<\/p>\n<h2><strong>\u67e5\u770b\u6570\u636e<\/strong><\/h2>\n<p>print(df.head())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u6b64\u5916\uff0cPython\u8fd8\u53ef\u4ee5\u5904\u7406JSON\u3001XML\u7b49\u683c\u5f0f\u7684\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528json\u5e93\u53ef\u4ee5\u8bfb\u53d6\u548c\u89e3\u6790JSON\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import json<\/p>\n<h2><strong>\u8bfb\u53d6JSON\u6587\u4ef6<\/strong><\/h2>\n<p>with open(&#39;data.json&#39;) as f:<\/p>\n<p>    data = json.load(f)<\/p>\n<h2><strong>\u67e5\u770b\u6570\u636e<\/strong><\/h2>\n<p>print(data)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u5927\u89c4\u6a21\u6570\u636e\u5904\u7406<\/h4>\n<\/p>\n<p><p>Python\u652f\u6301\u5927\u89c4\u6a21\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\uff0c\u9002\u7528\u4e8e\u5927\u6570\u636e\u5206\u6790\u573a\u666f\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Dask\u5e93\u53ef\u4ee5\u5728\u5206\u5e03\u5f0f\u73af\u5883\u4e2d\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002Dask\u662f\u4e00\u4e2a\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u5c06\u8ba1\u7b97\u4efb\u52a1\u5206\u89e3\u6210\u591a\u4e2a\u5c0f\u4efb\u52a1\uff0c\u5e76\u5728\u591a\u4e2a\u8ba1\u7b97\u8282\u70b9\u4e0a\u5e76\u884c\u6267\u884c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Dask\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import dask.dataframe as dd<\/p>\n<h2><strong>\u8bfb\u53d6\u5927\u89c4\u6a21CSV\u6587\u4ef6<\/strong><\/h2>\n<p>df = dd.read_csv(&#39;large_data.csv&#39;)<\/p>\n<h2><strong>\u8fdb\u884c\u6570\u636e\u5904\u7406<\/strong><\/h2>\n<p>result = df.groupby(&#39;category&#39;).sum().compute()<\/p>\n<p>print(result)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u53ef\u89c6\u5316\u529f\u80fd<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u73af\u8282\u3002Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u70ed\u529b\u56fe\u7b49\u3002\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u7684\u89c4\u5f8b\u548c\u8d8b\u52bf\uff0c\u5e2e\u52a9\u5206\u6790\u5e08\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h4>1. Matplotlib<\/h4>\n<\/p>\n<p><p>Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u51e0\u4e4e\u53ef\u4ee5\u7ed8\u5236\u6240\u6709\u7c7b\u578b\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u521b\u5efa\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u521b\u5efa\u6298\u7ebf\u56fe<\/strong><\/h2>\n<p>plt.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25])<\/p>\n<p>plt.title(&#39;Line Chart&#39;)<\/p>\n<p>plt.xlabel(&#39;X-axis&#39;)<\/p>\n<p>plt.ylabel(&#39;Y-axis&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Matplotlib\u7684\u529f\u80fd\u975e\u5e38\u5f3a\u5927\uff0c\u53ef\u4ee5\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u6837\u5f0f\u3001\u989c\u8272\u3001\u6807\u7b7e\u7b49\uff0c\u6ee1\u8db3\u5404\u79cd\u53ef\u89c6\u5316\u9700\u6c42\u3002<\/p>\n<\/p>\n<p><h4>2. Seaborn<\/h4>\n<\/p>\n<p><p>Seaborn\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u521b\u5efa\u5206\u7c7b\u6563\u70b9\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import seaborn as sns<\/p>\n<h2><strong>\u521b\u5efa\u5206\u7c7b\u6563\u70b9\u56fe<\/strong><\/h2>\n<p>sns.scatterplot(x=&#39;x&#39;, y=&#39;y&#39;, hue=&#39;category&#39;, data=df)<\/p>\n<p>plt.title(&#39;Scatter Plot&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Seaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u590d\u6742\u7684\u56fe\u8868\uff0c\u5982\u5206\u7c7b\u6563\u70b9\u56fe\u3001\u7bb1\u7ebf\u56fe\u3001\u70ed\u529b\u56fe\u7b49\u3002\u6b64\u5916\uff0cSeaborn\u8fd8\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\uff0c\u4f7f\u5f97\u56fe\u8868\u770b\u8d77\u6765\u66f4\u52a0\u4e13\u4e1a\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u793e\u533a\u652f\u6301<\/h3>\n<\/p>\n<p><p>Python\u62e5\u6709\u5e9e\u5927\u7684\u793e\u533a\uff0c\u793e\u533a\u4e2d\u6709\u5927\u91cf\u7684\u6570\u636e\u5206\u6790\u4e13\u5bb6\u548c\u7231\u597d\u8005\uff0c\u4ed6\u4eec\u4e0d\u65ad\u5f00\u53d1\u548c\u7ef4\u62a4\u5404\u79cd\u6570\u636e\u5206\u6790\u5e93\u548c\u5de5\u5177\u3002\u65e0\u8bba\u662f\u5728\u4f7f\u7528\u8fc7\u7a0b\u4e2d\u9047\u5230\u4ec0\u4e48\u95ee\u9898\uff0c\u90fd\u53ef\u4ee5\u901a\u8fc7\u793e\u533a\u83b7\u5f97\u5e2e\u52a9\u3002\u6b64\u5916\uff0c\u793e\u533a\u8fd8\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u5f00\u6e90\u9879\u76ee\u548c\u8d44\u6e90\uff0c\u53ef\u4ee5\u53c2\u8003\u548c\u501f\u9274\u3002<\/p>\n<\/p>\n<p><h4>1. \u5927\u91cf\u7684\u6559\u7a0b\u548c\u6587\u6863<\/h4>\n<\/p>\n<p><p>Python\u793e\u533a\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6559\u7a0b\u548c\u6587\u6863\uff0c\u4f7f\u5f97\u5b66\u4e60\u548c\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u5bb9\u6613\u3002\u4f8b\u5982\uff0cPandas\u548cNumPy\u90fd\u6709\u8be6\u7ec6\u7684\u5b98\u65b9\u6587\u6863\uff0c\u4ecb\u7ecd\u4e86\u5e93\u7684\u529f\u80fd\u548c\u4f7f\u7528\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u7f51\u4e0a\u8fd8\u6709\u5927\u91cf\u7684\u535a\u5ba2\u3001\u89c6\u9891\u6559\u7a0b\u548c\u5f00\u6e90\u9879\u76ee\uff0c\u53ef\u4ee5\u53c2\u8003\u548c\u5b66\u4e60\u3002<\/p>\n<\/p>\n<p><h4>2. \u6d3b\u8dc3\u7684\u793e\u533a\u8bba\u575b<\/h4>\n<\/p>\n<p><p>Python\u793e\u533a\u4e2d\u6709\u8bb8\u591a\u6d3b\u8dc3\u7684\u8bba\u575b\u548c\u8ba8\u8bba\u533a\uff0c\u4f8b\u5982Stack Overflow\u3001Reddit\u7b49\u3002\u5728\u8fd9\u4e9b\u8bba\u575b\u4e2d\uff0c\u6570\u636e\u5206\u6790\u4e13\u5bb6\u548c\u7231\u597d\u8005\u4eec\u4f1a\u5206\u4eab\u4ed6\u4eec\u7684\u7ecf\u9a8c\u548c\u6280\u5de7\uff0c\u56de\u7b54\u5176\u4ed6\u7528\u6237\u7684\u95ee\u9898\u3002\u5982\u679c\u5728\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u8fc7\u7a0b\u4e2d\u9047\u5230\u95ee\u9898\uff0c\u53ef\u4ee5\u5728\u8fd9\u4e9b\u8bba\u575b\u4e2d\u5bfb\u6c42\u5e2e\u52a9\u3002<\/p>\n<\/p>\n<p><h4>3. 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<strong>Python\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u57fa\u7840\u77e5\u8bc6\u662f\u4ec0\u4e48\uff1f<\/strong><br \/>Python\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u5206\u6790\u9886\u57df\u3002\u5176\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\u4f7f\u5f97\u6570\u636e\u5904\u7406\u3001\u6e05\u6d17\u548c\u53ef\u89c6\u5316\u53d8\u5f97\u66f4\u52a0\u9ad8\u6548\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\uff08\u7528\u4e8e\u6570\u636e\u5904\u7406\uff09\u3001NumPy\uff08\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff09\u3001Matplotlib\u548cSeaborn\uff08\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff09\u3002\u5b66\u4e60\u8fd9\u4e9b\u5e93\u7684\u57fa\u672c\u7528\u6cd5\u662f\u638c\u63e1Python\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65\u3002<\/p>\n<p><strong>\u521d\u5b66\u8005\u5982\u4f55\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\uff1f<\/strong><br \/>\u5bf9\u4e8e\u521d\u5b66\u8005\u6765\u8bf4\uff0c\u53ef\u4ee5\u4ece\u5b66\u4e60Python\u7684\u57fa\u7840\u8bed\u6cd5\u5f00\u59cb\uff0c\u63a5\u7740\u9010\u6b65\u6df1\u5165\u5230\u6570\u636e\u5206\u6790\u5e93\u7684\u4f7f\u7528\u3002\u5728\u7ebf\u8bfe\u7a0b\u3001\u6559\u7a0b\u548c\u4e66\u7c4d\u90fd\u662f\u5f88\u597d\u7684\u5b66\u4e60\u8d44\u6e90\u3002\u6b64\u5916\uff0c\u52a0\u5165\u6570\u636e\u5206\u6790\u76f8\u5173\u7684\u793e\u533a\u548c\u8bba\u575b\uff0c\u53c2\u4e0e\u8ba8\u8bba\u548c\u9879\u76ee\uff0c\u53ef\u4ee5\u5e2e\u52a9\u63d0\u5347\u6280\u80fd\u5e76\u83b7\u5f97\u5b9e\u8df5\u7ecf\u9a8c\u3002<\/p>\n<p><strong>Python\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\uff1f<\/strong><br 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