{"id":1103432,"date":"2025-01-08T16:10:27","date_gmt":"2025-01-08T08:10:27","guid":{"rendered":""},"modified":"2025-01-08T16:10:35","modified_gmt":"2025-01-08T08:10:35","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1103432.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u8fdb\u884c\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\/25065242\/231e8cba-1366-498a-9df9-22b7c72fc6d0.webp\" alt=\"python\u4e2d\u5982\u4f55\u8fdb\u884c\u6570\u636e\u5206\u6790\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u6570\u636e\u5bfc\u5165\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u63a2\u7d22\u3001\u6570\u636e\u5efa\u6a21\u3001\u6570\u636e\u53ef\u89c6\u5316\u3002<\/strong> \u5176\u4e2d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u4e2d\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u73af\u8282\u3002\u6570\u636e\u6e05\u6d17\u4e3b\u8981\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u503c\u3001\u5f02\u5e38\u503c\u7b49\u6b65\u9aa4\u3002\u5728\u6570\u636e\u6e05\u6d17\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684\u51fd\u6570\u6765\u5904\u7406\u8fd9\u4e9b\u95ee\u9898\uff0c\u4f8b\u5982<code>dropna<\/code>\u3001<code>fillna<\/code>\u3001<code>drop_duplicates<\/code>\u7b49\u3002\u8be6\u7ec6\u7684\u63cf\u8ff0\u5c06\u8fdb\u4e00\u6b65\u5c55\u5f00\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u6570\u636e\u5bfc\u5165<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u5bfc\u5165\u662f\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65\uff0c\u5728Python\u4e2d\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u548c\u5e93\u6765\u5bfc\u5165\u6570\u636e\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u3001NumPy\u7b49\u3002<\/p>\n<\/p>\n<p><h4>1.1 \u4f7f\u7528Pandas\u5bfc\u5165\u6570\u636e<\/h4>\n<\/p>\n<p><p>Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\u7684\u5bfc\u5165\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u5bfc\u5165\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>CSV\u6587\u4ef6<\/strong>\uff1a<code>pd.read_csv(&#39;file.csv&#39;)<\/code><\/li>\n<li><strong>Excel\u6587\u4ef6<\/strong>\uff1a<code>pd.read_excel(&#39;file.xlsx&#39;)<\/code><\/li>\n<li><strong>SQL\u6570\u636e\u5e93<\/strong>\uff1a<code>pd.read_sql(&#39;SELECT * FROM table&#39;, connection)<\/code><\/li>\n<li><strong>JSON\u6587\u4ef6<\/strong>\uff1a<code>pd.read_json(&#39;file.json&#39;)<\/code><\/li>\n<\/ul>\n<p><p>\u4f8b\u5982\uff0c\u5bfc\u5165\u4e00\u4e2aCSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p>data = pd.read_csv(&#39;data.csv&#39;)<\/p>\n<p>print(data.head())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>1.2 \u4f7f\u7528NumPy\u5bfc\u5165\u6570\u636e<\/h4>\n<\/p>\n<p><p>NumPy\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u6570\u503c\u578b\u6570\u636e\uff0c\u53ef\u4ee5\u4ece\u6587\u672c\u6587\u4ef6\u4e2d\u5bfc\u5165\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>data = np.loadtxt(&#39;data.txt&#39;, delimiter=&#39;,&#39;)<\/p>\n<p>print(data)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\uff0c\u5b83\u76f4\u63a5\u5f71\u54cd\u540e\u7eed\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u53ef\u4fe1\u5ea6\u3002\u5e38\u89c1\u7684\u6570\u636e\u6e05\u6d17\u6b65\u9aa4\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u503c\u3001\u5f02\u5e38\u503c\u7b49\u3002<\/p>\n<\/p>\n<p><h4>2.1 \u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n<p><p>\u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u4e2d\u7684<code>dropna<\/code>\u548c<code>fillna<\/code>\u65b9\u6cd5\u6765\u5904\u7406\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u5220\u9664\u7f3a\u5931\u503c<\/strong>\uff1a<code>df.dropna()<\/code><\/li>\n<li><strong>\u586b\u8865\u7f3a\u5931\u503c<\/strong>\uff1a<code>df.fillna(value)<\/code><\/li>\n<\/ul>\n<p><p>\u4f8b\u5982\uff0c\u5220\u9664\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">df_clean = data.dropna()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u586b\u8865\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">df_filled = data.fillna(0)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2.2 \u5904\u7406\u91cd\u590d\u503c<\/h4>\n<\/p>\n<p><p>\u91cd\u590d\u503c\u4f1a\u5f71\u54cd\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\uff0c\u53ef\u4ee5\u4f7f\u7528<code>drop_duplicates<\/code>\u65b9\u6cd5\u5220\u9664\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">df_no_duplicates = data.drop_duplicates()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2.3 \u5904\u7406\u5f02\u5e38\u503c<\/h4>\n<\/p>\n<p><p>\u5f02\u5e38\u503c\u53ef\u4ee5\u901a\u8fc7\u63cf\u8ff0\u6027\u7edf\u8ba1\u3001\u7bb1\u7ebf\u56fe\u7b49\u65b9\u6cd5\u68c0\u6d4b\uff0c\u5e76\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u8fdb\u884c\u5904\u7406\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u5220\u9664\u3001\u66ff\u6362\u3001\u6216\u5bf9\u5f02\u5e38\u503c\u8fdb\u884c\u6807\u8bb0\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u6570\u636e\u63a2\u7d22<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u63a2\u7d22\u662f\u4e86\u89e3\u6570\u636e\u57fa\u672c\u60c5\u51b5\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u5305\u62ec\u7edf\u8ba1\u63cf\u8ff0\u3001\u6570\u636e\u5206\u5e03\u3001\u76f8\u5173\u6027\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n<p><h4>3.1 \u63cf\u8ff0\u6027\u7edf\u8ba1<\/h4>\n<\/p>\n<p><p>\u63cf\u8ff0\u6027\u7edf\u8ba1\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5feb\u901f\u4e86\u89e3\u6570\u636e\u7684\u57fa\u672c\u60c5\u51b5\uff0c\u5305\u62ec\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">print(data.describe())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3.2 \u6570\u636e\u5206\u5e03<\/h4>\n<\/p>\n<p><p>\u6570\u636e\u5206\u5e03\u53ef\u4ee5\u901a\u8fc7\u76f4\u65b9\u56fe\u3001\u5bc6\u5ea6\u56fe\u7b49\u53ef\u89c6\u5316\u65b9\u6cd5\u6765\u5c55\u793a\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>data[&#39;column&#39;].hist()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3.3 \u76f8\u5173\u6027\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u76f8\u5173\u6027\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u4e2d\u7684<code>corr<\/code>\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">print(data.corr())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u6570\u636e\u5efa\u6a21<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u5efa\u6a21\u662f\u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u6765\u6784\u5efa\u6a21\u578b\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecScikit-Learn\u3001TensorFlow\u3001Keras\u7b49\u3002<\/p>\n<\/p>\n<p><h4>4.1 \u4f7f\u7528Scikit-Learn\u8fdb\u884c\u6570\u636e\u5efa\u6a21<\/h4>\n<\/p>\n<p><p>Scikit-Learn\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u652f\u6301\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u793a\u4f8b\uff1a<\/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.linear_model import LinearRegression<\/p>\n<p>from sklearn.metrics import mean_squared_error<\/p>\n<h2><strong>\u6570\u636e\u51c6\u5907<\/strong><\/h2>\n<p>X = data[[&#39;feature1&#39;, &#39;feature2&#39;]]<\/p>\n<p>y = data[&#39;target&#39;]<\/p>\n<h2><strong>\u6570\u636e\u5206\u5272<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n<h2><strong>\u6a21\u578b\u8bad\u7ec3<\/strong><\/h2>\n<p>model = LinearRegression()<\/p>\n<p>model.fit(X_train, y_train)<\/p>\n<h2><strong>\u6a21\u578b\u9884\u6d4b<\/strong><\/h2>\n<p>y_pred = model.predict(X_test)<\/p>\n<h2><strong>\u6a21\u578b\u8bc4\u4f30<\/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>4.2 \u4f7f\u7528TensorFlow\u8fdb\u884c\u6570\u636e\u5efa\u6a21<\/h4>\n<\/p>\n<p><p>TensorFlow\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6df1\u5ea6\u5b66\u4e60\u5e93\uff0c\u9002\u7528\u4e8e\u6784\u5efa\u590d\u6742\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u795e\u7ecf\u7f51\u7edc\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import tensorflow as tf<\/p>\n<p>from tensorflow.keras.models import Sequential<\/p>\n<p>from tensorflow.keras.layers import Dense<\/p>\n<h2><strong>\u6570\u636e\u51c6\u5907<\/strong><\/h2>\n<p>X = data[[&#39;feature1&#39;, &#39;feature2&#39;]]<\/p>\n<p>y = data[&#39;target&#39;]<\/p>\n<h2><strong>\u6570\u636e\u5206\u5272<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n<h2><strong>\u6a21\u578b\u6784\u5efa<\/strong><\/h2>\n<p>model = Sequential([<\/p>\n<p>    Dense(64, activation=&#39;relu&#39;, input_shape=(X_train.shape[1],)),<\/p>\n<p>    Dense(64, activation=&#39;relu&#39;),<\/p>\n<p>    Dense(1)<\/p>\n<p>])<\/p>\n<h2><strong>\u6a21\u578b\u7f16\u8bd1<\/strong><\/h2>\n<p>model.compile(optimizer=&#39;adam&#39;, loss=&#39;mean_squared_error&#39;)<\/p>\n<h2><strong>\u6a21\u578b\u8bad\u7ec3<\/strong><\/h2>\n<p>model.fit(X_train, y_train, epochs=10, batch_size=32)<\/p>\n<h2><strong>\u6a21\u578b\u9884\u6d4b<\/strong><\/h2>\n<p>y_pred = model.predict(X_test)<\/p>\n<h2><strong>\u6a21\u578b\u8bc4\u4f30<\/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><h3>\u4e94\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n<p><p>\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u73af\u8282\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u7684\u7279\u5f81\u548c\u89c4\u5f8b\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u7b49\u3002<\/p>\n<\/p>\n<p><h4>5.1 \u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n<p><p>Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u56fe\u8868\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u6298\u7ebf\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n<p><pre><code class=\"language-python\">plt.plot(data[&#39;column&#39;])<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ul>\n<li><strong>\u6563\u70b9\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n<p><pre><code class=\"language-python\">plt.scatter(data[&#39;feature1&#39;], data[&#39;feature2&#39;])<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>5.2 \u4f7f\u7528Seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n<p><p>Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7f8e\u89c2\u548c\u7b80\u4fbf\u7684\u56fe\u8868\u7ed8\u5236\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u56fe\u8868\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u7bb1\u7ebf\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n<p><pre><code class=\"language-python\">import seaborn as sns<\/p>\n<p>sns.boxplot(x=data[&#39;column&#39;])<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ul>\n<li><strong>\u70ed\u529b\u56fe<\/strong>\uff1a<\/li>\n<\/ul>\n<p><pre><code class=\"language-python\">sns.heatmap(data.corr(), annot=True)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u6d41\u7a0b\u5305\u62ec\u6570\u636e\u5bfc\u5165\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u63a2\u7d22\u3001\u6570\u636e\u5efa\u6a21\u548c\u6570\u636e\u53ef\u89c6\u5316\u3002\u6bcf\u4e2a\u73af\u8282\u90fd\u6709\u5176\u91cd\u8981\u6027\u548c\u5177\u4f53\u7684\u65b9\u6cd5\u3002\u5728\u5b9e\u9645\u64cd\u4f5c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5de5\u5177\u3002\u901a\u8fc7\u4e0d\u65ad\u7684\u5b9e\u8df5\u548c\u7ecf\u9a8c\u79ef\u7d2f\uff0c\u6211\u4eec\u53ef\u4ee5\u63d0\u5347\u6570\u636e\u5206\u6790\u7684\u80fd\u529b\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5728Python\u4e2d\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff1f<\/strong><br \/>Python\u62e5\u6709\u4f17\u591a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u5305\u62ecPandas\u3001NumPy\u3001Matplotlib\u548cSeaborn\u3002Pandas\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0cNumPy\u5219\u4e3b\u8981\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u6570\u7ec4\u64cd\u4f5c\u3002Matplotlib\u548cSeaborn\u5219\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u7528\u6237\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u80cc\u540e\u7684\u8d8b\u52bf\u548c\u6a21\u5f0f\u3002<\/p>\n<p><strong>\u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u7f3a\u5931\u6570\u636e\uff1f<\/strong><br \/>\u5904\u7406\u7f3a\u5931\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u8bc6\u522b\u548c\u5904\u7406\u7f3a\u5931\u503c\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u586b\u5145\u7f3a\u5931\u503c\uff08\u5982\u4f7f\u7528\u5e73\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\u586b\u5145\uff09\uff0c\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\uff0c\u6216\u8005\u4f7f\u7528\u63d2\u503c\u6cd5\u6765<a href=\"https:\/\/docs.pingcode.com\/agile\/project-management\/estimation\" target=\"_blank\">\u4f30\u7b97<\/a>\u7f3a\u5931\u503c\u3002\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u901a\u8fc7Pandas\u7684\u5185\u7f6e\u51fd\u6570\u8f7b\u677e\u5b9e\u73b0\u3002<\/p>\n<p><strong>\u6570\u636e\u5206\u6790\u65f6\u5982\u4f55\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong><br \/>\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u4e00\u90e8\u5206\uff0c\u5b83\u80fd\u591f\u5e2e\u52a9\u5206\u6790\u5e08\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u3002Python\u4e2d\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cSeaborn\u7b49\u5e93\u6765\u521b\u5efa\u5404\u79cd\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u7b49\u3002\u901a\u8fc7\u8bbe\u7f6e\u56fe\u8868\u7684\u6807\u9898\u3001\u6807\u7b7e\u548c\u989c\u8272\u7b49\u5c5e\u6027\uff0c\u53ef\u4ee5\u4f7f\u56fe\u8868\u66f4\u52a0\u6e05\u6670\u548c\u6613\u4e8e\u89e3\u8bfb\uff0c\u4ece\u800c\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u5206\u6790\u7684\u7ed3\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u6570\u636e\u5bfc\u5165\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u63a2\u7d22\u3001\u6570\u636e\u5efa\u6a21\u3001\u6570\u636e\u53ef\u89c6\u5316\u3002 \u5176\u4e2d\uff0c\u6570\u636e\u6e05 [&hellip;]","protected":false},"author":3,"featured_media":1103453,"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\/1103432"}],"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=1103432"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1103432\/revisions"}],"predecessor-version":[{"id":1103454,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1103432\/revisions\/1103454"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1103453"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1103432"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1103432"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1103432"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}