{"id":1162223,"date":"2025-01-13T19:25:54","date_gmt":"2025-01-13T11:25:54","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1162223.html"},"modified":"2025-01-13T19:25:57","modified_gmt":"2025-01-13T11:25:57","slug":"%e5%a6%82%e4%bd%95%e5%af%b9%e6%88%90%e7%bb%a9%e8%bf%9b%e8%a1%8c%e6%8c%96%e6%8e%98python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1162223.html","title":{"rendered":"\u5982\u4f55\u5bf9\u6210\u7ee9\u8fdb\u884c\u6316\u6398python"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25203320\/bec39555-4c75-4e68-9b2d-f86511252996.webp\" alt=\"\u5982\u4f55\u5bf9\u6210\u7ee9\u8fdb\u884c\u6316\u6398python\" \/><\/p>\n<p><p> \u5728Python\u4e2d\uff0c\u5bf9\u6210\u7ee9\u8fdb\u884c\u6316\u6398\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\uff0c\u5982<strong>\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u7edf\u8ba1\u5206\u6790\u3001<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a><\/strong>\u7b49\u3002\u9996\u5148\uff0c\u901a\u8fc7\u6570\u636e\u6e05\u6d17\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\uff0c\u7136\u540e\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u6765\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u548c\u8d8b\u52bf\uff0c\u63a5\u7740\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\u6765\u53d1\u73b0\u6210\u7ee9\u4e2d\u7684\u6f5c\u5728\u6a21\u5f0f\uff0c\u6700\u540e\u53ef\u4ee5\u8fd0\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u8fdb\u884c\u9884\u6d4b\u548c\u5206\u7c7b\u3002<strong>\u6570\u636e\u6e05\u6d17\u662f\u81f3\u5173\u91cd\u8981\u7684\u4e00\u6b65<\/strong>\uff0c\u56e0\u4e3a\u5b83\u786e\u4fdd\u4e86\u540e\u7eed\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n<p><p>\u5728\u5bf9\u6210\u7ee9\u6570\u636e\u8fdb\u884c\u6316\u6398\u4e4b\u524d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u81f3\u5173\u91cd\u8981\u7684\u4e00\u6b65\u3002\u6570\u636e\u6e05\u6d17\u7684\u4e3b\u8981\u76ee\u6807\u662f\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u3001\u91cd\u590d\u503c\u4ee5\u53ca\u683c\u5f0f\u95ee\u9898\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u6570\u636e\u4e2d\uff0c\u7ecf\u5e38\u4f1a\u9047\u5230\u7f3a\u5931\u503c\u3002\u7f3a\u5931\u503c\u4f1a\u5f71\u54cd\u6570\u636e\u5206\u6790\u7684\u7ed3\u679c\uff0c\u56e0\u6b64\u9700\u8981\u8fdb\u884c\u5904\u7406\u3002\u901a\u5e38\u6709\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n<ul>\n<li>\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u8bb0\u5f55<\/li>\n<li>\u4f7f\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\u586b\u8865\u7f3a\u5931\u503c<\/li>\n<li>\u4f7f\u7528\u63d2\u503c\u6cd5\u586b\u8865\u7f3a\u5931\u503c<\/li>\n<\/ul>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u8bfb\u53d6\u6210\u7ee9\u6570\u636e<\/strong><\/h2>\n<p>data = pd.read_csv(&#39;scores.csv&#39;)<\/p>\n<h2><strong>\u67e5\u770b\u6570\u636e\u4e2d\u662f\u5426\u5b58\u5728\u7f3a\u5931\u503c<\/strong><\/h2>\n<p>print(data.isnull().sum())<\/p>\n<h2><strong>\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u8bb0\u5f55<\/strong><\/h2>\n<p>data_cleaned = data.dropna()<\/p>\n<h2><strong>\u4f7f\u7528\u5747\u503c\u586b\u8865\u7f3a\u5931\u503c<\/strong><\/h2>\n<p>data_filled = data.fillna(data.mean())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u5904\u7406\u5f02\u5e38\u503c<\/h4>\n<\/p>\n<p><p>\u5f02\u5e38\u503c\u662f\u6307\u4e0e\u6570\u636e\u96c6\u4e2d\u7684\u5176\u4ed6\u6570\u636e\u70b9\u660e\u663e\u4e0d\u540c\u7684\u6570\u636e\u70b9\uff0c\u901a\u5e38\u662f\u7531\u4e8e\u6570\u636e\u5f55\u5165\u9519\u8bef\u6216\u5176\u4ed6\u5f02\u5e38\u60c5\u51b5\u5bfc\u81f4\u7684\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u5904\u7406\u5f02\u5e38\u503c\uff1a<\/p>\n<\/p>\n<ul>\n<li>\u4f7f\u7528\u7bb1\u7ebf\u56fe\u6216\u6807\u51c6\u5dee\u8bc6\u522b\u5f02\u5e38\u503c<\/li>\n<li>\u5220\u9664\u5f02\u5e38\u503c<\/li>\n<li>\u4f7f\u7528\u5408\u7406\u7684\u6570\u503c\u66ff\u6362\u5f02\u5e38\u503c<\/li>\n<\/ul>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u4f7f\u7528\u7bb1\u7ebf\u56fe\u8bc6\u522b\u5f02\u5e38\u503c<\/strong><\/h2>\n<p>import matplotlib.pyplot as plt<\/p>\n<p>plt.boxplot(data[&#39;score&#39;])<\/p>\n<p>plt.show()<\/p>\n<h2><strong>\u5220\u9664\u5f02\u5e38\u503c<\/strong><\/h2>\n<p>Q1 = data[&#39;score&#39;].quantile(0.25)<\/p>\n<p>Q3 = data[&#39;score&#39;].quantile(0.75)<\/p>\n<p>IQR = Q3 - Q1<\/p>\n<p>data_no_outliers = data[~((data[&#39;score&#39;] &lt; (Q1 - 1.5 * IQR)) | (data[&#39;score&#39;] &gt; (Q3 + 1.5 * IQR)))]<\/p>\n<h2><strong>\u4f7f\u7528\u5408\u7406\u7684\u6570\u503c\u66ff\u6362\u5f02\u5e38\u503c<\/strong><\/h2>\n<p>data[&#39;score&#39;] = np.where(data[&#39;score&#39;] &gt; (Q3 + 1.5 * IQR), Q3 + 1.5 * IQR, data[&#39;score&#39;])<\/p>\n<p>data[&#39;score&#39;] = np.where(data[&#39;score&#39;] &lt; (Q1 - 1.5 * IQR), Q1 - 1.5 * IQR, data[&#39;score&#39;])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e8c\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u53ef\u89c6\u5316\u662f\u7406\u89e3\u6570\u636e\u5206\u5e03\u548c\u8d8b\u52bf\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u6210\u7ee9\u6570\u636e\u7684\u5206\u5e03\u3001\u53d8\u5316\u8d8b\u52bf\u4ee5\u53ca\u6f5c\u5728\u7684\u6a21\u5f0f\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u7ed8\u5236\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n<p><p>\u76f4\u65b9\u56fe\u53ef\u4ee5\u5c55\u793a\u6210\u7ee9\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6570\u636e\u7684\u96c6\u4e2d\u8d8b\u52bf\u548c\u5206\u6563\u7a0b\u5ea6\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import seaborn as sns<\/p>\n<h2><strong>\u7ed8\u5236\u6210\u7ee9\u6570\u636e\u7684\u76f4\u65b9\u56fe<\/strong><\/h2>\n<p>sns.histplot(data[&#39;score&#39;], kde=True)<\/p>\n<p>plt.xlabel(&#39;Score&#39;)<\/p>\n<p>plt.ylabel(&#39;Frequency&#39;)<\/p>\n<p>plt.title(&#39;Distribution of Scores&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/h4>\n<\/p>\n<p><p>\u7bb1\u7ebf\u56fe\u53ef\u4ee5\u5c55\u793a\u6210\u7ee9\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u4ee5\u53ca\u6f5c\u5728\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u7ed8\u5236\u6210\u7ee9\u6570\u636e\u7684\u7bb1\u7ebf\u56fe<\/p>\n<p>sns.boxplot(x=data[&#39;score&#39;])<\/p>\n<p>plt.xlabel(&#39;Score&#39;)<\/p>\n<p>plt.title(&#39;Box Plot of Scores&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u7ed8\u5236\u6563\u70b9\u56fe<\/h4>\n<\/p>\n<p><p>\u6563\u70b9\u56fe\u53ef\u4ee5\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5e2e\u52a9\u6211\u4eec\u53d1\u73b0\u6210\u7ee9\u6570\u636e\u4e2d\u7684\u6f5c\u5728\u6a21\u5f0f\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u7ed8\u5236\u6210\u7ee9\u548c\u5b66\u4e60\u65f6\u95f4\u7684\u6563\u70b9\u56fe<\/p>\n<p>sns.scatterplot(x=data[&#39;study_time&#39;], y=data[&#39;score&#39;])<\/p>\n<p>plt.xlabel(&#39;Study Time&#39;)<\/p>\n<p>plt.ylabel(&#39;Score&#39;)<\/p>\n<p>plt.title(&#39;Scatter Plot of Study Time and Score&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n<p><p>\u7edf\u8ba1\u5206\u6790\u662f\u6316\u6398\u6210\u7ee9\u6570\u636e\u4e2d\u6f5c\u5728\u6a21\u5f0f\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\uff0c\u53ef\u4ee5\u91cf\u5316\u6570\u636e\u7684\u7279\u5f81\uff0c\u53d1\u73b0\u6210\u7ee9\u6570\u636e\u4e2d\u7684\u89c4\u5f8b\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u5305\u62ec\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\u7edf\u8ba1\u91cf\u7684\u8ba1\u7b97\uff0c\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6570\u636e\u7684\u57fa\u672c\u7279\u5f81\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf<\/p>\n<p>descriptive_stats = data.describe()<\/p>\n<p>print(descriptive_stats)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u76f8\u5173\u6027\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u76f8\u5173\u6027\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53d1\u73b0\u6210\u7ee9\u6570\u636e\u4e2d\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u91cf\u5316\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u8054\u7a0b\u5ea6\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8ba1\u7b97\u76f8\u5173\u7cfb\u6570<\/p>\n<p>correlation_matrix = data.corr()<\/p>\n<p>print(correlation_matrix)<\/p>\n<h2><strong>\u53ef\u89c6\u5316\u76f8\u5173\u6027\u77e9\u9635<\/strong><\/h2>\n<p>sns.heatmap(correlation_matrix, annot=True, cmap=&#39;coolwarm&#39;)<\/p>\n<p>plt.title(&#39;Correlation Matrix&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u5047\u8bbe\u68c0\u9a8c<\/h4>\n<\/p>\n<p><p>\u5047\u8bbe\u68c0\u9a8c\u662f\u7edf\u8ba1\u5206\u6790\u4e2d\u7684\u91cd\u8981\u5de5\u5177\uff0c\u7528\u4e8e\u9a8c\u8bc1\u6570\u636e\u4e2d\u7684\u5047\u8bbe\u662f\u5426\u6210\u7acb\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528t\u68c0\u9a8c\u6bd4\u8f83\u4e0d\u540c\u7ec4\u522b\u7684\u6210\u7ee9\u662f\u5426\u5b58\u5728\u663e\u8457\u5dee\u5f02\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from scipy.stats import ttest_ind<\/p>\n<h2><strong>\u6bd4\u8f83\u7537\u751f\u548c\u5973\u751f\u7684\u6210\u7ee9\u662f\u5426\u5b58\u5728\u663e\u8457\u5dee\u5f02<\/strong><\/h2>\n<p>male_scores = data[data[&#39;gender&#39;] == &#39;male&#39;][&#39;score&#39;]<\/p>\n<p>female_scores = data[data[&#39;gender&#39;] == &#39;female&#39;][&#39;score&#39;]<\/p>\n<p>t_stat, p_value = ttest_ind(male_scores, female_scores)<\/p>\n<p>print(f&#39;T-statistic: {t_stat}, P-value: {p_value}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u673a\u5668\u5b66\u4e60<\/h3>\n<\/p>\n<p><p>\u673a\u5668\u5b66\u4e60\u662f\u6316\u6398\u6210\u7ee9\u6570\u636e\u4e2d\u6f5c\u5728\u6a21\u5f0f\u548c\u8fdb\u884c\u9884\u6d4b\u7684\u91cd\u8981\u5de5\u5177\u3002\u901a\u8fc7\u8bad\u7ec3\u6a21\u578b\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u6210\u7ee9\u7684\u5206\u7c7b\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u6570\u636e\u9884\u5904\u7406<\/h4>\n<\/p>\n<p><p>\u5728\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4e4b\u524d\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5305\u62ec\u7279\u5f81\u9009\u62e9\u3001\u7279\u5f81\u7f29\u653e\u548c\u6570\u636e\u5206\u5272\u7b49\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import tr<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n_test_split<\/p>\n<p>from sklearn.preprocessing import StandardScaler<\/p>\n<h2><strong>\u9009\u62e9\u7279\u5f81\u548c\u6807\u7b7e<\/strong><\/h2>\n<p>features = data.drop(columns=[&#39;score&#39;])<\/p>\n<p>labels = data[&#39;score&#39;]<\/p>\n<h2><strong>\u5206\u5272\u6570\u636e\u96c6\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)<\/p>\n<h2><strong>\u7279\u5f81\u7f29\u653e<\/strong><\/h2>\n<p>scaler = StandardScaler()<\/p>\n<p>X_train_scaled = scaler.fit_transform(X_train)<\/p>\n<p>X_test_scaled = scaler.transform(X_test)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u56de\u5f52\u6a21\u578b<\/h4>\n<\/p>\n<p><p>\u56de\u5f52\u6a21\u578b\u7528\u4e8e\u9884\u6d4b\u8fde\u7eed\u503c\u7684\u6210\u7ee9\u3002\u5e38\u7528\u7684\u56de\u5f52\u6a21\u578b\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u5cad\u56de\u5f52\u548c\u51b3\u7b56\u6811\u56de\u5f52\u7b49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.linear_model import LinearRegression<\/p>\n<p>from sklearn.metrics import mean_squared_error<\/p>\n<h2><strong>\u8bad\u7ec3\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n<p>model = LinearRegression()<\/p>\n<p>model.fit(X_train_scaled, y_train)<\/p>\n<h2><strong>\u9884\u6d4b\u6d4b\u8bd5\u96c6\u6210\u7ee9<\/strong><\/h2>\n<p>y_pred = model.predict(X_test_scaled)<\/p>\n<h2><strong>\u8ba1\u7b97\u5747\u65b9\u8bef\u5dee<\/strong><\/h2>\n<p>mse = mean_squared_error(y_test, y_pred)<\/p>\n<p>print(f&#39;Mean Squared Error: {mse}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u5206\u7c7b\u6a21\u578b<\/h4>\n<\/p>\n<p><p>\u5206\u7c7b\u6a21\u578b\u7528\u4e8e\u5c06\u6210\u7ee9\u5206\u7c7b\u5230\u4e0d\u540c\u7c7b\u522b\u4e2d\u3002\u5e38\u7528\u7684\u5206\u7c7b\u6a21\u578b\u5305\u62ec\u903b\u8f91\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\u548c\u968f\u673a\u68ee\u6797\u7b49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.ensemble import RandomForestClassifier<\/p>\n<p>from sklearn.metrics import accuracy_score<\/p>\n<h2><strong>\u8bad\u7ec3\u968f\u673a\u68ee\u6797\u5206\u7c7b\u6a21\u578b<\/strong><\/h2>\n<p>classifier = RandomForestClassifier()<\/p>\n<p>classifier.fit(X_train_scaled, y_train)<\/p>\n<h2><strong>\u9884\u6d4b\u6d4b\u8bd5\u96c6\u6210\u7ee9\u7c7b\u522b<\/strong><\/h2>\n<p>y_pred = classifier.predict(X_test_scaled)<\/p>\n<h2><strong>\u8ba1\u7b97\u51c6\u786e\u7387<\/strong><\/h2>\n<p>accuracy = accuracy_score(y_test, y_pred)<\/p>\n<p>print(f&#39;Accuracy: {accuracy}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5168\u9762\u5730\u5bf9\u6210\u7ee9\u6570\u636e\u8fdb\u884c\u6316\u6398\u3002\u9996\u5148\uff0c\u901a\u8fc7\u6570\u636e\u6e05\u6d17\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\uff0c\u7136\u540e\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u76f4\u89c2\u5c55\u793a\u6570\u636e\u5206\u5e03\u548c\u8d8b\u52bf\uff0c\u63a5\u7740\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\u91cf\u5316\u6570\u636e\u7279\u5f81\u548c\u53d1\u73b0\u6f5c\u5728\u6a21\u5f0f\uff0c\u6700\u540e\u8fd0\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u5206\u7c7b\u548c\u9884\u6d4b\u3002<strong>\u6570\u636e\u6e05\u6d17\u662f\u786e\u4fdd\u540e\u7eed\u5206\u6790\u51c6\u786e\u6027\u7684\u5173\u952e\u6b65\u9aa4<\/strong>\uff0c\u901a\u8fc7\u5904\u7406\u7f3a\u5931\u503c\u548c\u5f02\u5e38\u503c\uff0c\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u5206\u6790\u5b66\u751f\u6210\u7ee9\u6570\u636e\uff1f<\/strong><br \/>\u4f7f\u7528Python\u5206\u6790\u5b66\u751f\u6210\u7ee9\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u591a\u4e2a\u6b65\u9aa4\u5b8c\u6210\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u6536\u96c6\u548c\u6e05\u7406\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002\u63a5\u4e0b\u6765\uff0c\u53ef\u4ee5\u5229\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\uff0c\u4f8b\u5982\u8ba1\u7b97\u5e73\u5747\u5206\u3001\u53ca\u683c\u7387\u7b49\u3002\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u5982Matplotlib\u548cSeaborn\u53ef\u4ee5\u5e2e\u52a9\u60a8\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u6210\u7ee9\u5206\u5e03\u548c\u8d8b\u52bf\u3002\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u5e94\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u63a2\u7d22\u5f71\u54cd\u6210\u7ee9\u7684\u56e0\u7d20\uff0c\u4e3a\u6559\u80b2\u51b3\u7b56\u63d0\u4f9b\u6570\u636e\u652f\u6301\u3002<\/p>\n<p><strong>Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u6210\u7ee9\u6570\u636e\u7684\u6316\u6398\uff1f<\/strong><br \/>\u5728\u6210\u7ee9\u6570\u636e\u6316\u6398\u4e2d\uff0c\u51e0\u79cd\u6d41\u884c\u7684Python\u5e93\u975e\u5e38\u6709\u7528\u3002Pandas\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u6846\u67b6\u529f\u80fd\u3002NumPy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u9002\u5408\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\u3002Matplotlib\u548cSeaborn\u4e13\u6ce8\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u60a8\u521b\u5efa\u56fe\u8868\u548c\u56fe\u5f62\u3002\u800cScikit-learn\u5219\u662f\u673a\u5668\u5b66\u4e60\u7684\u9996\u9009\u5e93\uff0c\u9002\u5408\u8fdb\u884c\u5206\u7c7b\u3001\u56de\u5f52\u548c\u805a\u7c7b\u7b49\u4efb\u52a1\u3002\u8fd9\u4e9b\u5e93\u7684\u7ec4\u5408\u53ef\u4ee5\u4f7f\u5f97\u6570\u636e\u6316\u6398\u66f4\u52a0\u9ad8\u6548\u548c\u76f4\u89c2\u3002<\/p>\n<p><strong>\u5982\u4f55\u5904\u7406\u7f3a\u5931\u7684\u6210\u7ee9\u6570\u636e\uff1f<\/strong><br \/>\u5904\u7406\u7f3a\u5931\u7684\u6210\u7ee9\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u73af\u8282\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u3001\u7528\u5e73\u5747\u503c\u6216\u4e2d\u4f4d\u6570\u586b\u5145\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528\u63d2\u503c\u6cd5\u8fdb\u884c<a href=\"https:\/\/docs.pingcode.com\/agile\/project-management\/estimation\" target=\"_blank\">\u4f30\u7b97<\/a>\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u5904\u7406\u7f3a\u5931\u503c\u7684\u51fd\u6570\uff0c\u4f8b\u5982<code>dropna()<\/code>\u548c<code>fillna()<\/code>\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u7684\u5206\u6790\u9700\u6c42\u7075\u6d3b\u9009\u62e9\u3002\u6b64\u5916\uff0c\u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0c\u5efa\u8bae\u8bb0\u5f55\u7f3a\u5931\u6570\u636e\u7684\u6a21\u5f0f\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5b8c\u6574\u6027\u53ca\u5176\u5bf9\u7ed3\u679c\u7684\u6f5c\u5728\u5f71\u54cd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u5bf9\u6210\u7ee9\u8fdb\u884c\u6316\u6398\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\uff0c\u5982\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u7edf\u8ba1\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u7b49\u3002\u9996\u5148\uff0c\u901a [&hellip;]","protected":false},"author":3,"featured_media":1162225,"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\/1162223"}],"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=1162223"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1162223\/revisions"}],"predecessor-version":[{"id":1162228,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1162223\/revisions\/1162228"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1162225"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1162223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1162223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1162223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}