{"id":204046,"date":"2025-05-29T12:22:44","date_gmt":"2025-05-29T04:22:44","guid":{"rendered":"https:\/\/server.hk\/cnblog\/204046\/"},"modified":"2025-05-29T12:22:44","modified_gmt":"2025-05-29T04:22:44","slug":"%e4%bd%bf%e7%94%a8python%e5%88%86%e6%9e%90nba%e6%af%94%e8%b5%9b%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/server.hk\/cnblog\/204046\/","title":{"rendered":"\u4f7f\u7528Python\u5206\u6790NBA\u6bd4\u8d5b\u6570\u636e"},"content":{"rendered":"<p><b><\/b>     <\/p>\n<h1>\u4f7f\u7528Python\u5206\u6790NBA\u6bd4\u8d5b\u6570\u636e<\/h1>\n<p>\u6765\u5230\u7684\u5927\u5bb6\uff0c\u76f8\u4fe1\u90fd\u662f\u7f16\u7a0b\u5b66\u4e60\u7231\u597d\u8005\uff0c\u5e0c\u671b\u5728\u8fd9\u91cc\u5b66\u4e60\u6587\u7ae0\u76f8\u5173\u7f16\u7a0b\u77e5\u8bc6\u3002\u4e0b\u9762\u672c\u7bc7\u6587\u7ae0\u5c31\u6765\u5e26\u5927\u5bb6\u804a\u804a\u300a\u4f7f\u7528Python\u5206\u6790NBA\u6bd4\u8d5b\u6570\u636e\u300b\uff0c\u4ecb\u7ecd\u4e00\u4e0b\uff0c\u5e0c\u671b\u5bf9\u5927\u5bb6\u7684\u77e5\u8bc6\u79ef\u7d2f\u6709\u6240\u5e2e\u52a9\uff0c\u52a9\u529b\u5b9e\u6218\u5f00\u53d1\uff01<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.17golang.com\/uploads\/20241026\/1729901386671c334ae329d.jpg\" class=\"aligncenter\"><\/p>\n<p>\u7f8e\u56fd\u56fd\u5bb6\u7bee\u7403\u534f\u4f1a (NBA) \u662f\u5168\u7403\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u4f53\u80b2\u8d5b\u4e8b\u8054\u76df\u4e4b\u4e00\uff0c\u6bcf\u4e2a\u8d5b\u5b63\u90fd\u6709\u6570\u5341\u4e07\u7403\u8ff7\u6536\u770b\u6bd4\u8d5b\u3002\u5bf9\u4e8e\u540c\u65f6\u70ed\u7231\u7bee\u7403\u548c\u6570\u636e\u5206\u6790\u7684\u4eba\u6765\u8bf4\uff0cNBA \u6bd4\u8d5b\u7edf\u8ba1\u6570\u636e\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u89c1\u89e3\u3002\u4ece\u53c2\u4e0e\u8005\u7684\u6574\u4f53\u8868\u73b0\u5230\u961f\u5458\u4e8b\u5b9e\uff0cPython \u662f\u7814\u7a76\u548c\u89e3\u8bfb NBA \u4f53\u80b2\u6570\u636e\u7684\u9ad8\u8d28\u91cf\u5de5\u5177\u3002\u5728\u672c\u624b\u518c\u4e2d\uff0c\u6211\u4eec\u5c06\u63a2\u7d22\u5982\u4f55\u4f7f\u7528 Python \u6df1\u5165\u7814\u7a76 NBA \u7edf\u8ba1\u6570\u636e\u5e76\u5e2e\u52a9\u60a8\u5f00\u59cb\u81ea\u5df1\u7684\u8bc4\u4f30\u4efb\u52a1\u3002<\/p>\n<p>\ufeff\ufeffNBA \u8bb0\u5f55\u4e86\u5927\u91cf\u7684\u6570\u636e\uff0c\u5305\u62ec\u7403\u5458\u8bb0\u5f55\uff08\u5f97\u5206\u3001\u52a9\u653b\u3001\u7bee\u677f\uff09\u3001\u7403\u961f\u5178\u578b\u8868\u73b0\uff08\u80dc\u5229\u3001\u5931\u8d25\u3001\u5931\u8bef\uff09\u548c\u52aa\u529b\u6548\u679c\u3002\u901a\u8fc7\u9605\u8bfb\u8fd9\u4e9b\u7edf\u8ba1\u6570\u636e\uff0c\u60a8\u53ef\u4ee5\u6df1\u5165\u4e86\u89e3\u7403\u5458\u6548\u7387\u3001\u7403\u961f\u7b56\u7565\uff0c\u751a\u81f3\u9884\u6d4b\u8fd0\u52a8\u7ed3\u679c\u3002 Python \u662f\u4e00\u79cd\u529f\u80fd\u5f3a\u5927\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u5e7f\u6cdb\u7528\u4e8e\u4fe1\u606f\u8bc4\u4f30\uff0c\u5e76\u4e14\u975e\u5e38\u9002\u5408\u8fd0\u884c NBA \u4e8b\u5b9e\u3002<\/p>\n<p>\u5728\u6211\u4eec\u5f00\u59cb\u7f16\u7801\u4e4b\u524d\uff0c\u60a8\u9700\u8981\u4e00\u4e9b\u4e1c\u897f\uff1a<\/p>\n<p><strong>Python\uff1a<\/strong> \u786e\u4fdd\u60a8\u7684\u8ba1\u7b97\u673a\u4e0a\u5b89\u88c5\u4e86 Python\u3002<br \/><strong>\u5e93\uff1a<\/strong>\u6211\u4eec\u5c06\u4f7f\u7528\u4e00\u4e9b Python \u5e93\uff0c\u4f8b\u5982 Pandas\u3001Matplotlib \u548c Seaborn\u3002<br \/><strong>NBA\u6570\u636e\u6765\u6e90\uff1a<\/strong>\u60a8\u53ef\u4ee5\u4eceNBA\u5b98\u65b9\u7edf\u8ba1\u7f51\u7ad9\u7b49\u6765\u6e90\u6216Basketball Reference\u6216Kaggle\u7b49\u7b2c\u4e09\u65b9\u5e73\u53f0\u627e\u5230NBA\u6570\u636e\u3002<\/p>\n<p>\ufeff\u8981\u5f00\u59cb\u9605\u8bfb NBA \u6bd4\u8d5b\u4e8b\u5b9e\uff0c\u60a8\u9996\u5148\u9700\u8981\u8bbe\u7f6e Python \u73af\u5883\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528 Jupyter Notebook \u6216 Google Colab \u7b49\u5de5\u5177\u6765\u7f16\u5199\u548c\u8fd0\u884c Python \u4ee3\u7801\u3002<\/p>\n<p>\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\u5fc5\u8981\u7684Python\u5e93\uff1a<\/p>\n<p>pip \u5b89\u88c5 pandas<br \/> pip \u5b89\u88c5 matplotlib<br \/> pip \u5b89\u88c5seaborn<\/p>\n<ul>\n<li>Pandas \u6709\u52a9\u4e8e\u7ba1\u7406\u548c\u64cd\u4f5c\u5927\u578b\u6570\u636e\u96c6\u3002<\/li>\n<li>Matplotlib \u548c Seaborn \u7528\u4e8e\u53ef\u89c6\u5316\u6570\u636e\u3002<\/li>\n<\/ul>\n<p>\u5047\u8bbe\u60a8\u5df2\u7ecf\u4e0b\u8f7d\u4e86 CSV \u683c\u5f0f\u7684 NBA \u6570\u636e\u96c6\u3002\u7b2c\u4e00\u6b65\u662f\u4f7f\u7528 Pandas \u5c06\u6570\u636e\u96c6\u52a0\u8f7d\u5230 Python \u4e2d\u3002\u5177\u4f53\u65b9\u6cd5\u5982\u4e0b\uff1a<\/p>\n<p>\u5c06 pandas \u5bfc\u5165\u4e3a pd<\/p>\n<p>nba_data = pd.read_csv(&#8216;nba_game_data.csv&#8217;)<\/p>\n<p>\u6253\u5370(nba_data.head())<\/p>\n<p>head() \u51fd\u6570\u5c06\u663e\u793a\u6570\u636e\u7684\u524d\u4e94\u884c\uff0c\u8ba9\u60a8\u4e86\u89e3\u6570\u636e\u96c6\u5305\u542b\u54ea\u4e9b\u5217\u548c\u4fe1\u606f\u3002\u5e38\u89c1\u5217\u53ef\u80fd\u5305\u62ec\u7403\u5458\u59d3\u540d\u3001\u5f97\u5206\u3001\u52a9\u653b\u3001\u7bee\u677f\u548c\u6bd4\u8d5b\u65e5\u671f\u3002<\/p>\n<p>\u73b0\u5b9e\u4e16\u754c\u7684\u6570\u636e\u96c6\u901a\u5e38\u5305\u542b\u7f3a\u5931\u6216\u4e0d\u6b63\u786e\u7684\u6570\u636e\uff0c\u9700\u8981\u5728\u5206\u6790\u4e4b\u524d\u8fdb\u884c\u6e05\u7406\u3002\u8ba9\u6211\u4eec\u68c0\u67e5\u4e00\u4e0b\u6570\u636e\u96c6\u4e2d\u662f\u5426\u6709\u7f3a\u5931\u503c\uff1a<\/p>\n<p># \u68c0\u67e5\u662f\u5426\u6709\u7f3a\u5931\u503c<br \/> print(nba_data.isnull().sum())<br \/> \u5982\u679c\u60a8\u53d1\u73b0\u4efb\u4f55\u7f3a\u5931\u503c\uff0c\u60a8\u53ef\u4ee5\u7528\u5e73\u5747\u503c\u586b\u5145\u5b83\u4eec\u6216\u5220\u9664\u8fd9\u4e9b\u884c\uff1a<\/p>\n<p># \u7528\u5217\u5e73\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c<br \/> nba_data.fillna(nba_data.mean(), inplace=True)<br \/> \u73b0\u5728\u6570\u636e\u5df2\u6e05\u7406\u5b8c\u6bd5\uff0c\u60a8\u53ef\u4ee5\u5f00\u59cb\u5206\u6790\u4e86\uff01<\/p>\n<p>\u6211\u4eec\u5148\u6765\u7b80\u5355\u5206\u6790\u4e00\u4e0b\uff1a\u627e\u51fa\u6240\u6709\u73a9\u5bb6\u6bcf\u573a\u6bd4\u8d5b\u7684\u5e73\u5747\u5f97\u5206\u3002<\/p>\n<p># \u8ba1\u7b97\u6bcf\u573a\u6bd4\u8d5b\u7684\u5e73\u5747\u5206<br \/> average_points = nba_data[&#8216;points&#8217;].mean()<br \/> print(f&#8217;\u6bcf\u573a\u6bd4\u8d5b\u5e73\u5747\u5f97\u5206\uff1a{average_points}&#8217;)`<br \/> \u8fd9\u8ba9\u6211\u4eec\u53ef\u4ee5\u5feb\u901f\u4e86\u89e3\u73a9\u5bb6\u5728\u6570\u636e\u96c6\u4e2d\u7684\u5e73\u5747\u5f97\u5206\u3002<\/p>\n<p>\u73b0\u5728\uff0c\u5047\u8bbe\u60a8\u60f3\u8981\u5206\u6790\u67d0\u4e2a\u7279\u5b9a\u7403\u5458\uff08\u4f8b\u5982\u52d2\u5e03\u6717\u00b7\u8a79\u59c6\u65af\uff09\u6574\u4e2a\u8d5b\u5b63\u7684\u8868\u73b0\u3002\u60a8\u53ef\u4ee5\u8fc7\u6ee4\u6570\u636e\u96c6\u4ee5\u5173\u6ce8\u4ed6\u7684\u6bd4\u8d5b\uff1a<\/p>\n<p># \u8fc7\u6ee4\u52d2\u5e03\u6717\u00b7\u8a79\u59c6\u65af\u7684\u6570\u636e<br \/> lebron_data = nba_data[nba_data[&#8216;player&#8217;] == &#8216;\u52d2\u5e03\u6717\u00b7\u8a79\u59c6\u65af&#8217;]<\/p>\n<p>lebron_avg_points = lebron_data[&#8216;points&#8217;].mean()<br \/> print(f&#8217;\u52d2\u5e03\u6717\u00b7\u8a79\u59c6\u65af\u573a\u5747\u5f97\u5206\uff1a{lebron_avg_points}&#8217;)<\/p>\n<p>\u53ef\u89c6\u5316\u4f7f\u60a8\u66f4\u5bb9\u6613\u7406\u89e3\u548c\u5448\u73b0\u60a8\u7684\u53d1\u73b0\u3002\u8ba9\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7ed8\u56fe\u6765\u53ef\u89c6\u5316\u52d2\u5e03\u6717\u00b7\u8a79\u59c6\u65af\u6bcf\u573a\u6bd4\u8d5b\u7684\u5f97\u5206\uff1a<\/p>\n<p>\u5bfc\u5165 matplotlib.pyplot \u4f5c\u4e3a plt<\/p>\n<p>\u7ed8\u5236\u52d2\u5e03\u6717\u6bcf\u573a\u6bd4\u8d5b\u7684\u5f97\u5206<br \/> plt.plot(lebron_data[&#8216;\u6bd4\u8d5b\u65e5\u671f&#8217;], lebron_data[&#8216;\u70b9&#8217;], \u6807\u8bb0=&#8217;o&#8217;)<br \/> plt.title(&#8216;\u52d2\u5e03\u6717\u00b7\u8a79\u59c6\u65af\u573a\u5747\u5f97\u5206&#8217;)<br \/> plt.xlabel(&#8216;\u6bd4\u8d5b\u65e5\u671f&#8217;)<br \/> plt.ylabel(&#8216;\u5f97\u5206&#8217;)<br \/> plt.xticks(\u65cb\u8f6c=45)<br \/> plt.show()<br \/> \u8fd9\u5c06\u751f\u6210\u4e00\u4e2a\u7ebf\u56fe\uff0c\u663e\u793a\u52d2\u5e03\u6717\u5728\u6574\u4e2a\u8d5b\u5b63\u7684\u5f97\u5206\u8868\u73b0\uff0c\u6bcf\u4e2a\u70b9\u4ee3\u8868\u4ed6\u5728\u7279\u5b9a\u6bd4\u8d5b\u4e2d\u7684\u5f97\u5206\u3002<\/p>\n<p>\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528Python\u6765\u5206\u6790\u56e2\u961f\u7ee9\u6548\u3002\u6211\u4eec\u6765\u8ba1\u7b97\u4e00\u4e0b\u6d1b\u6749\u77f6\u6e56\u4eba\u961f\u6240\u6709\u6bd4\u8d5b\u7684\u5e73\u5747\u5f97\u5206\uff1a<\/p>\n<p># \u6d1b\u6749\u77f6\u6e56\u4eba\u961f\u7684\u7b5b\u9009\u6570\u636e<br \/> Lakers_data = nba_data[nba_data[&#8216;team&#8217;] == &#8216;\u6d1b\u6749\u77f6\u6e56\u4eba\u961f&#8217;]<\/p>\n<p>lakers_avg_points = Lakers_data[&#8216;points&#8217;].mean()<br \/> print(f&#8217;\u6d1b\u6749\u77f6\u6e56\u4eba\u961f\u573a\u5747\u5f97\u5206\uff1a{lakers_avg_points}&#8217;)<br \/> \u8fd9\u8ba9\u6211\u4eec\u4e86\u89e3\u4e86\u6e56\u4eba\u961f\u4f5c\u4e3a\u4e00\u4e2a\u56e2\u961f\u7684\u8868\u73b0\uff0c\u53ef\u4ee5\u4e0e\u5176\u4ed6\u7403\u961f\u6216\u8fc7\u53bb\u7684\u8d5b\u5b63\u8fdb\u884c\u6bd4\u8f83\u3002<\/p>\n<p>\u6709\u65f6\u60a8\u53ef\u80fd\u60f3\u770b\u770b\u4e24\u4e2a\u7edf\u8ba1\u6570\u636e\u4e4b\u95f4\u662f\u5426\u5b58\u5728\u76f8\u5173\u6027\u3002\u4f8b\u5982\uff0c\u5f97\u5206\u8d8a\u9ad8\u7684\u7403\u5458\u662f\u5426\u52a9\u653b\u4e5f\u8d8a\u591a\uff1f<\/p>\n<p># \u8ba1\u7b97\u5f97\u5206\u548c\u52a9\u653b\u4e4b\u95f4\u7684\u76f8\u5173\u6027<br \/> \u76f8\u5173\u6027 = nba_data[&#8216;points&#8217;].corr(nba_data[&#8216;assists&#8217;])<br \/> print(f&#8217;\u5f97\u5206\u4e0e\u52a9\u653b\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff1a{correlation}&#8217;)<br \/> \u6b63\u76f8\u5173\u8868\u660e\u5f97\u5206\u8f83\u9ad8\u7684\u73a9\u5bb6\u5f80\u5f80\u4f1a\u63d0\u4f9b\u66f4\u591a\u5e2e\u52a9\u3002<\/p>\n<p>\u5206\u6790\u5b8c\u6570\u636e\u540e\uff0c\u60a8\u53ef\u4ee5\u8fdb\u4e00\u6b65\u6784\u5efa\u673a\u5668\u5b66\u4e60\u6a21\u578b\u6765\u9884\u6d4b\u6e38\u620f\u7ed3\u679c\u3002\u867d\u7136\u8fd9\u9700\u8981\u66f4\u5148\u8fdb\u7684\u6280\u672f\uff0c\u4f46\u53ef\u4ee5\u4f7f\u7528 scikit-learn \u7b49 Python \u5e93\u6765\u57fa\u4e8e\u5386\u53f2\u6570\u636e\u8bad\u7ec3\u6a21\u578b\u3002<\/p>\n<p>\u8fd9\u662f\u4e00\u4e2a\u5206\u5272\u6570\u636e\u4ee5\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6a21\u578b\u7684\u7b80\u5355\u793a\u4f8b\uff1a<\/p>\n<p>\u4ece sklearn.model_selection \u5bfc\u5165 train_test_split<br \/> \u4ece sklearn.linear_model \u5bfc\u5165 LogisticRegression<\/p>\n<p>X = nba_data[[&#8216;\u5f97\u5206&#8217;, &#8216;\u52a9\u653b&#8217;, &#8216;\u7bee\u677f&#8217;]]<br \/> y = nba_data[&#8216;win_loss&#8217;] # \u5047\u8bbe win_loss \u5217\uff081 \u8868\u793a\u83b7\u80dc\uff0c0 \u8868\u793a\u5931\u8d25\uff09<br \/> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)<\/p>\n<p>\u6a21\u578b = LogisticRegression()<br \/> model.fit(X_train, y_train)<\/p>\n<p>\u51c6\u786e\u7387 = model.score(X_test, y_test)<br \/> print(f&#8217;\u6a21\u578b\u7cbe\u5ea6: {accuracy}&#8217;)<br \/> \u8fd9\u4e2a\u57fa\u672c\u6a21\u578b\u53ef\u4ee5\u901a\u8fc7\u66f4\u591a\u7684\u6570\u636e\u548c\u66f4\u597d\u7684\u7279\u5f81\u9009\u62e9\u6765\u5b8c\u5584\uff0c\u4ee5\u505a\u51fa\u66f4\u51c6\u786e\u7684\u9884\u6d4b\u3002<\/p>\n<p>\u4f7f\u7528 Python \u5206\u6790 NBA \u6bd4\u8d5b\u6570\u636e\u4e3a\u7bee\u7403\u8ff7\u548c\u6570\u636e\u7231\u597d\u8005\u6253\u5f00\u4e86\u4e00\u4e2a\u5145\u6ee1\u53ef\u80fd\u6027\u7684\u4e16\u754c\u3002\u4ece\u8ba1\u7b97\u73a9\u5bb6\u5e73\u5747\u503c\u5230\u9884\u6d4b\u6e38\u620f\u7ed3\u679c\uff0cPython \u53ef\u4ee5\u8ba9\u60a8\u53d1\u73b0\u6e38\u620f\u4e2d\u9690\u85cf\u7684\u6a21\u5f0f\u3002\u53ea\u9700\u51e0\u4e2a\u5e93\u548c\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u60a8\u5c31\u53ef\u4ee5\u5f00\u59cb\u81ea\u5df1\u7684\u5206\u6790\u9879\u76ee\uff0c\u5e76\u53d1\u73b0\u6709\u5173\u60a8\u6700\u559c\u6b22\u7684\u7403\u961f\u548c\u7403\u5458\u7684\u65b0\u89c1\u89e3\u3002\u60a8\u63a2\u7d22\u5f97\u8d8a\u591a\uff0c\u60a8\u5c31\u8d8a\u4f1a\u610f\u8bc6\u5230\u6570\u636e\u5bf9\u4e8e\u7406\u89e3\u7bee\u7403\u6bd4\u8d5b\u6709\u591a\u4e48\u5f3a\u5927\u3002<\/p>\n<p>Q1\uff1a\u54ea\u91cc\u53ef\u4ee5\u627e\u5230NBA\u6bd4\u8d5b\u6570\u636e\u8fdb\u884c\u5206\u6790\uff1f\u60a8\u53ef\u4ee5\u5728 NBA Stats\u3001Basketball Reference \u7b49\u7f51\u7ad9\u6216 Kaggle \u7b49\u6570\u636e\u5171\u4eab\u5e73\u53f0\u4e0a\u627e\u5230 NBA \u6bd4\u8d5b\u6570\u636e\u3002<\/p>\n<p>Q2\uff1a\u54ea\u4e9b Python \u5e93\u6700\u9002\u5408 NBA \u6570\u636e\u5206\u6790\uff1f Pandas\u3001Matplotlib \u548c Seaborn \u975e\u5e38\u9002\u5408\u6570\u636e\u64cd\u4f5c\u548c\u53ef\u89c6\u5316\u3002\u5bf9\u4e8e\u673a\u5668\u5b66\u4e60\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528 scikit-learn \u7b49\u5e93\u3002<\/p>\n<p>Q3\uff1a\u6211\u53ef\u4ee5\u4f7f\u7528Python\u6765\u9884\u6d4bNBA\u6bd4\u8d5b\u7ed3\u679c\u5417\uff1f\u662f\u7684\uff01\u901a\u8fc7\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u5386\u53f2\u6e38\u620f\u6570\u636e\u6784\u5efa\u9884\u6d4b\u6a21\u578b\u3002<\/p>\n<p>\u95ee\u98984\uff1a\u5982\u4f55\u6e05\u7406NBA\u6570\u636e\u8fdb\u884c\u5206\u6790\uff1f\u60a8\u53ef\u4ee5\u4f7f\u7528 fillna() \u7b49\u51fd\u6570\u5904\u7406\u4e22\u5931\u7684\u6570\u636e\uff0c\u6216\u4f7f\u7528 dropna() \u5220\u9664\u6709\u95ee\u9898\u7684\u884c\u3002\u5728\u5206\u6790\u4e4b\u524d\u6e05\u7406\u6570\u636e\u975e\u5e38\u91cd\u8981\u3002<\/p>\n<p>\u95ee\u98985\uff1a\u6211\u53ef\u4ee5\u4f7f\u7528Python \u5206\u6790\u54ea\u4e9b\u7c7b\u578b\u7684NBA \u7edf\u8ba1\u6570\u636e\uff1f\u60a8\u53ef\u4ee5\u5206\u6790\u7403\u5458\u7edf\u8ba1\u6570\u636e\uff08\u5f97\u5206\u3001\u52a9\u653b\u3001\u7bee\u677f\uff09\u3001\u7403\u961f\u7edf\u8ba1\u6570\u636e\uff08\u80dc\u5229\u3001\u5931\u5229\u3001\u5931\u8bef\uff09\uff0c\u751a\u81f3\u662f\u7403\u5458\u6548\u7387\u8bc4\u5206 (PER) \u7b49\u9ad8\u7ea7\u6307\u6807\u3002<\/p>\n<p>Q6\uff1a\u5b66\u4e60Python\u8fdb\u884cNBA\u6570\u636e\u5206\u6790\u6709\u591a\u96be\uff1f Python \u88ab\u8ba4\u4e3a\u662f\u6700\u5bb9\u6613\u5b66\u4e60\u7684\u7f16\u7a0b\u8bed\u8a00\u4e4b\u4e00\u3002\u901a\u8fc7\u4e00\u4e9b\u57fa\u7840\u6559\u7a0b\uff0c\u60a8\u53ef\u4ee5\u5feb\u901f\u5f00\u59cb\u5206\u6790 NBA 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