{"id":3878,"date":"2025-09-13T08:48:51","date_gmt":"2025-09-13T00:48:51","guid":{"rendered":"http:\/\/viplao.com\/?p=3878"},"modified":"2025-09-13T08:48:53","modified_gmt":"2025-09-13T00:48:53","slug":"%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5","status":"publish","type":"post","link":"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/","title":{"rendered":"\u3010Python\u5b9e\u8df5\u6848\u4f8b\u3011\u7535\u5546\u5e73\u53f0\u6570\u636e\u5206\u6790\u548c\u6316\u6398 -\u5206\u6790\u5458\u5de5\u51fa\u52e4"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_71 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u6587\u7ae0\u76ee\u5f55<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path 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ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/#%F0%9F%93%88_%E7%AC%AC%E4%BA%8C%E6%AD%A5%EF%BC%9A%E5%8F%AF%E8%A7%86%E5%8C%96%E5%88%86%E6%9E%90%E5%BC%80%E5%A7%8B%EF%BC%81\" title=\"\ud83d\udcc8 \u7b2c\u4e8c\u6b65\uff1a\u53ef\u89c6\u5316\u5206\u6790\u5f00\u59cb\uff01\">\ud83d\udcc8 \u7b2c\u4e8c\u6b65\uff1a\u53ef\u89c6\u5316\u5206\u6790\u5f00\u59cb\uff01<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/#1_%E5%91%98%E5%B7%A5%E5%B9%B3%E5%9D%87%E5%B7%A5%E4%BD%9C%E6%97%B6%E9%95%BF%E6%8E%92%E5%90%8D\" title=\"1. \u5458\u5de5\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\u6392\u540d\">1. \u5458\u5de5\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\u6392\u540d<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/#2_%E6%80%BB%E5%B7%A5%E4%BD%9C%E6%97%B6%E9%95%BF%EF%BC%9A%E8%B0%81%E6%98%AF%E2%80%9C%E5%8A%B3%E6%A8%A1%E2%80%9D%EF%BC%9F\" title=\"2. \u603b\u5de5\u4f5c\u65f6\u957f\uff1a\u8c01\u662f\u201c\u52b3\u6a21\u201d\uff1f\">2. \u603b\u5de5\u4f5c\u65f6\u957f\uff1a\u8c01\u662f\u201c\u52b3\u6a21\u201d\uff1f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/#3_%E6%AF%8F%E5%91%A8_%E6%AF%8F%E6%9C%88%E5%B7%A5%E4%BD%9C%E5%88%86%E5%B8%83%EF%BC%9A%E5%A0%86%E5%8F%A0%E6%9D%A1%E5%BD%A2%E5%9B%BE%E7%99%BB%E5%9C%BA\" title=\"3. \u6bcf\u5468 \/ \u6bcf\u6708\u5de5\u4f5c\u5206\u5e03\uff1a\u5806\u53e0\u6761\u5f62\u56fe\u767b\u573a\">3. \u6bcf\u5468 \/ \u6bcf\u6708\u5de5\u4f5c\u5206\u5e03\uff1a\u5806\u53e0\u6761\u5f62\u56fe\u767b\u573a<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/#4_%E5%B7%A5%E4%BD%9C%E8%B6%8B%E5%8A%BF%E5%88%86%E6%9E%90%EF%BC%9A%E6%8A%98%E7%BA%BF%E5%9B%BE%E6%8F%AD%E7%A4%BA%E5%8F%98%E5%8C%96\" title=\"4. \u5de5\u4f5c\u8d8b\u52bf\u5206\u6790\uff1a\u6298\u7ebf\u56fe\u63ed\u793a\u53d8\u5316\">4. \u5de5\u4f5c\u8d8b\u52bf\u5206\u6790\uff1a\u6298\u7ebf\u56fe\u63ed\u793a\u53d8\u5316<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/#5_%E7%83%AD%E5%8A%9B%E5%9B%BE%EF%BC%9A%E4%B8%80%E5%9B%BE%E8%83%9C%E5%8D%83%E8%A8%80\" title=\"5. \u70ed\u529b\u56fe\uff1a\u4e00\u56fe\u80dc\u5343\u8a00\">5. \u70ed\u529b\u56fe\uff1a\u4e00\u56fe\u80dc\u5343\u8a00<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E7%AC%AC%E4%B8%80%E6%AD%A5%EF%BC%9A%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E4%B8%8E%E6%B8%85%E6%B4%97\"><\/span>\u7b2c\u4e00\u6b65\uff1a\u6570\u636e\u52a0\u8f7d\u4e0e\u6e05\u6d17<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>\u9996\u5148\u6570\u636e\u662f\u4e00\u4efd Excel \u6587\u4ef6\uff0c\u6211\u4eec\u9700\u8981\u9884\u5904\u7406\uff0c\u5305\u542b\u4e86\u5458\u5de5\u7684\u6253\u5361\u8bb0\u5f55\uff0c\u5305\u62ec&nbsp;<code>CheckIn<\/code>\uff08\u4e0a\u73ed\u65f6\u95f4\uff09\u548c&nbsp;<code>CheckOut<\/code>\uff08\u4e0b\u73ed\u65f6\u95f4\uff09\u3002\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>df = pd.read_excel(\"employee_attendance.xlsx\")<br>df&#91;'CheckIn'] = pd.to_datetime(df&#91;'CheckIn'], errors='coerce')<br>df&#91;'CheckOut'] = pd.to_datetime(df&#91;'CheckOut'], errors='coerce')<br>df.dropna(subset=&#91;'CheckIn',&nbsp;'CheckOut'], inplace=True)<\/code><\/pre>\n\n\n\n<p>\u7136\u540e\u6211\u8ba1\u7b97\u4e86\u6bcf\u4f4d\u5458\u5de5\u7684<strong>\u6bcf\u65e5\u5de5\u4f5c\u65f6\u957f<\/strong>\uff0c\u5e76\u63d0\u53d6\u4e86\u65f6\u95f4\u7ef4\u5ea6\uff0c\u6bd4\u5982\u5468\u3001\u6708\u3001\u661f\u671f\u51e0\u7b49\uff0c\u4e3a\u540e\u7eed\u5206\u6790\u505a\u51c6\u5907\u3002<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%93%88_%E7%AC%AC%E4%BA%8C%E6%AD%A5%EF%BC%9A%E5%8F%AF%E8%A7%86%E5%8C%96%E5%88%86%E6%9E%90%E5%BC%80%E5%A7%8B%EF%BC%81\"><\/span>\ud83d\udcc8 \u7b2c\u4e8c\u6b65\uff1a\u53ef\u89c6\u5316\u5206\u6790\u5f00\u59cb\uff01<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u5f00\u59cb\u7528\u56fe\u8868\u6765\u201c\u8bb2\u6545\u4e8b\u201d\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_%E5%91%98%E5%B7%A5%E5%B9%B3%E5%9D%87%E5%B7%A5%E4%BD%9C%E6%97%B6%E9%95%BF%E6%8E%92%E5%90%8D\"><\/span>1. \u5458\u5de5\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\u6392\u540d<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>\u9996\u5148\uff0c\u6211\u7ed8\u5236\u4e86\u6bcf\u4e2a\u5458\u5de5\u7684\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\uff0c\u770b\u770b\u8c01\u662f\u201c\u52a0\u73ed\u72c2\u9b54\u201d\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>avg_work = df.groupby('Name')&#91;'Work Duration'].mean().sort_values(ascending=False)<br>plot_bar(avg_work,&nbsp;'\u5458\u5de5\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\u6392\u540d',&nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;'\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_%E6%80%BB%E5%B7%A5%E4%BD%9C%E6%97%B6%E9%95%BF%EF%BC%9A%E8%B0%81%E6%98%AF%E2%80%9C%E5%8A%B3%E6%A8%A1%E2%80%9D%EF%BC%9F\"><\/span>2. \u603b\u5de5\u4f5c\u65f6\u957f\uff1a\u8c01\u662f\u201c\u52b3\u6a21\u201d\uff1f<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>\u7edf\u8ba1\u4e86\u6bcf\u4e2a\u4eba\u7684\u603b\u5de5\u4f5c\u65f6\u957f\uff0c\u770b\u770b\u8c01\u662f\u771f\u6b63\u7684\u201c\u52b3\u6a21\u201d\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>total_work = df.groupby('Name')&#91;'Work Duration'].sum().sort_values(ascending=False)<br>plot_bar(total_work,&nbsp;'\u5458\u5de5\u603b\u5de5\u4f5c\u65f6\u957f\u6392\u540d',&nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')<\/code><\/pre>\n\n\n\n<p>\u56fe\u8868\u663e\u793a\uff0c\u603b\u65f6\u957f\u4e0e\u5e73\u5747\u65f6\u957f\u5e76\u4e0d\u5b8c\u5168\u4e00\u81f4\uff0c\u8bf4\u660e\u6709\u4e9b\u4eba\u201c\u9760\u65f6\u95f4\u201d\uff0c\u6709\u4e9b\u4eba\u201c\u9760\u6548\u7387\u201d\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_%E6%AF%8F%E5%91%A8_%E6%AF%8F%E6%9C%88%E5%B7%A5%E4%BD%9C%E5%88%86%E5%B8%83%EF%BC%9A%E5%A0%86%E5%8F%A0%E6%9D%A1%E5%BD%A2%E5%9B%BE%E7%99%BB%E5%9C%BA\"><\/span>3. \u6bcf\u5468 \/ \u6bcf\u6708\u5de5\u4f5c\u5206\u5e03\uff1a\u5806\u53e0\u6761\u5f62\u56fe\u767b\u573a<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>\u4e3a\u4e86\u8fdb\u4e00\u6b65\u5206\u6790\uff0c\u7528\u5806\u53e0\u6761\u5f62\u56fe\u5c55\u793a\u4e86\u6bcf\u4f4d\u5458\u5de5\u6bcf\u5468\u548c\u6bcf\u6708\u7684\u5de5\u4f5c\u5206\u5e03\u60c5\u51b5\u3002\u8fd9\u79cd\u56fe\u8868\u975e\u5e38\u9002\u5408\u89c2\u5bdf\u8d8b\u52bf\u53d8\u5316\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>weekly_work = df.groupby(&#91;'Name',&nbsp;'Week'])&#91;'Work Duration'].sum().unstack(fill_value=0)<br>plot_stacked_bar(weekly_work,&nbsp;'\u5458\u5de5\u6bcf\u5468\u5de5\u4f5c\u65f6\u957f\u5206\u5e03',&nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')<\/code><\/pre>\n\n\n\n<p>\u901a\u8fc7\u8fd9\u4e2a\u56fe\uff0c\u53ef\u4ee5\u8f7b\u677e\u53d1\u73b0\u67d0\u4e9b\u5458\u5de5\u5728\u7279\u5b9a\u5468\u6b21\u5de5\u4f5c\u5f3a\u5ea6\u7279\u522b\u9ad8\uff0c\u8fd9\u53ef\u80fd\u4e0e\u9879\u76ee\u5468\u671f\u6709\u5173\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_%E5%B7%A5%E4%BD%9C%E8%B6%8B%E5%8A%BF%E5%88%86%E6%9E%90%EF%BC%9A%E6%8A%98%E7%BA%BF%E5%9B%BE%E6%8F%AD%E7%A4%BA%E5%8F%98%E5%8C%96\"><\/span>4. \u5de5\u4f5c\u8d8b\u52bf\u5206\u6790\uff1a\u6298\u7ebf\u56fe\u63ed\u793a\u53d8\u5316<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>\u4e3a\u4e86\u89c2\u5bdf\u6bcf\u4f4d\u5458\u5de5\u7684\u5de5\u4f5c\u8d8b\u52bf\uff0c\u7ed8\u5236\u4e86\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>monthly_trends = {name: group.groupby('Month')&#91;'Work Duration'].sum()<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;for&nbsp;name, group&nbsp;in&nbsp;df.groupby('Name')}<br>plot_line(monthly_trends,&nbsp;'\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u8d8b\u52bf\u5206\u6790',&nbsp;'\u6708\u4efd',&nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')<\/code><\/pre>\n\n\n\n<p>\u6709\u4e9b\u5458\u5de5\u5728\u5e74\u521d\u8868\u73b0\u79ef\u6781\uff0c\u5230\u4e86\u5e74\u5e95\u53cd\u800c\u201c\u8eba\u5e73\u201d\u4e86\uff0c\u8fd9\u4e5f\u8bb8\u80fd\u4e3a HR \u63d0\u4f9b\u4e00\u4e9b\u7ba1\u7406\u4e0a\u7684\u542f\u53d1\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_%E7%83%AD%E5%8A%9B%E5%9B%BE%EF%BC%9A%E4%B8%80%E5%9B%BE%E8%83%9C%E5%8D%83%E8%A8%80\"><\/span>5. \u70ed\u529b\u56fe\uff1a\u4e00\u56fe\u80dc\u5343\u8a00<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>\u70ed\u529b\u56fe\u662f\u6211\u6700\u559c\u6b22\u7684\u4e00\u79cd\u53ef\u89c6\u5316\u65b9\u5f0f\uff0c\u5b83\u80fd\u76f4\u89c2\u5730\u5c55\u793a\u4e8c\u7ef4\u6570\u636e\u7684\u5bc6\u96c6\u7a0b\u5ea6\u3002\u5206\u522b\u7ed8\u5236\u4e86\u6bcf\u5468\u548c\u6bcf\u6708\u7684\u5de5\u4f5c\u70ed\u529b\u56fe\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pivot_weekly = df.pivot_table(index='Name', columns='Week',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; values='Work Duration', aggfunc='sum', fill_value=0)\nplot_heatmap(pivot_weekly,&nbsp;'\u5458\u5de5\u6bcf\u5468\u5de5\u4f5c\u65f6\u957f\u70ed\u529b\u56fe',&nbsp;'\u5468\u6b21',&nbsp;'\u5458\u5de5\u59d3\u540d')\n\n6. \u997c\u56fe\uff1a\u5de5\u4f5c\u65e5\u5206\u5e03\u5206\u6790\u6700\u540e\uff0c\u6211\u8fd8\u7528\u997c\u56fe\u5c55\u793a\u4e86\u5458\u5de5\u7684\u5de5\u4f5c\u65e5\u5206\u5e03\u60c5\u51b5\uff0c\u770b\u770b\u5927\u5bb6\u66f4\u559c\u6b22\u54ea\u4e00\u5929\u4e0a\u73ed\uff1aweekday_counts = df&#91;'Weekday'].value_counts()\nplt.pie(weekday_counts, autopct='%1.1f%%', startangle=90)\n\n\ud83d\udccc \u5982\u679c\u4f60\u4e5f\u60f3\u5c1d\u8bd5\u4f60\u53ef\u4ee5\u8f7b\u677e\u590d\u7528\u6211\u7684\u4ee3\u7801\uff0c\u53ea\u9700\u8981\uff1a\u51c6\u5907\u4e00\u4efd\u5305\u542b\u5458\u5de5\u6253\u5361\u65f6\u95f4\u7684 Excel \u6587\u4ef6\uff1b\u786e\u4fdd\u5217\u540d\u5305\u542b&nbsp;CheckIn&nbsp;\u548c&nbsp;CheckOut\uff1b\u8fd0\u884c\u4ee3\u7801\uff0c\u751f\u6210\u5c5e\u4e8e\u4f60\u7684\u53ef\u89c6\u5316\u5206\u6790\u62a5\u544a\uff01\ud83d\udce5 \u83b7\u53d6\u5b8c\u6574\u4ee3\u7801\u5982\u679c\u4f60\u5bf9\u8fd9\u4e2a\u9879\u76ee\u611f\u5174\u8da3\uff0c\u53ef\u4ee5\u590d\u5236\u4e0b\u5217\u5b8c\u6574\u4ee3\u7801\u548c\u793a\u4f8b\u6570\u636e\u3002\u4e5f\u6b22\u8fce\u4f60\u5206\u4eab\u81ea\u5df1\u7684\u5206\u6790\u7ed3\u679c\uff0c\u6211\u4eec\u4e00\u8d77\u63a2\u8ba8\u6570\u636e\u7684\u9b45\u529b\uff01\n\n\u6570\u636e\uff1aDateEmployeeIDNameDepartmentCheckInCheckOut\n2025\/10\/2E001\u5f20\u4e09\u6280\u672f\u90e8<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>import&nbsp;pandas&nbsp;as&nbsp;pd\nimport&nbsp;matplotlib.pyplot&nbsp;as&nbsp;plt\nimport&nbsp;seaborn&nbsp;as&nbsp;sns\nimport&nbsp;numpy&nbsp;as&nbsp;np\nfrom&nbsp;matplotlib.ticker&nbsp;import&nbsp;FuncFormatter\n\n# \u8bbe\u7f6e\u5168\u5c40\u6837\u5f0f\u548c\u5b57\u4f53\nplt.style.use('seaborn-v0_8-whitegrid')\nsns.set_palette(\"husl\") &nbsp;# \u4f7f\u7528\u66f4\u534f\u8c03\u7684\u914d\u8272\u65b9\u6848\nplt.rcParams&#91;'font.sans-serif'] = &#91;'Microsoft YaHei',&nbsp;'SimHei',&nbsp;'Arial Unicode MS']\nplt.rcParams&#91;'axes.unicode_minus'] =&nbsp;False\nplt.rcParams&#91;'font.size'] =&nbsp;12\nplt.rcParams&#91;'axes.titlesize'] =&nbsp;16\nplt.rcParams&#91;'axes.labelsize'] =&nbsp;14\nplt.rcParams&#91;'xtick.labelsize'] =&nbsp;12\nplt.rcParams&#91;'ytick.labelsize'] =&nbsp;12\nplt.rcParams&#91;'legend.fontsize'] =&nbsp;12\n\ndef&nbsp;load_and_preprocess_data(file_path):\n&nbsp; &nbsp;&nbsp;\"\"\"\u52a0\u8f7d\u5e76\u9884\u5904\u7406\u6570\u636e\"\"\"\n&nbsp; &nbsp; df = pd.read_excel(file_path)\n&nbsp; &nbsp; df&#91;'CheckIn'] = pd.to_datetime(df&#91;'CheckIn'], errors='coerce')\n&nbsp; &nbsp; df&#91;'CheckOut'] = pd.to_datetime(df&#91;'CheckOut'], errors='coerce')\n&nbsp; &nbsp; df.dropna(subset=&#91;'CheckIn',&nbsp;'CheckOut'], inplace=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u8ba1\u7b97\u5de5\u4f5c\u65f6\u957f\n&nbsp; &nbsp; df&#91;'Work Duration'] = (df&#91;'CheckOut'] - df&#91;'CheckIn']).dt.total_seconds() \/&nbsp;3600\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u63d0\u53d6\u65f6\u95f4\u7ef4\u5ea6\n&nbsp; &nbsp; df&#91;'Week'] = df&#91;'CheckIn'].dt.isocalendar().week\n&nbsp; &nbsp; df&#91;'Month'] = df&#91;'CheckIn'].dt.month\n&nbsp; &nbsp; df&#91;'Weekday'] = df&#91;'CheckIn'].dt.day_name()\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;return&nbsp;df\n\ndef&nbsp;plot_bar(data, title, xlabel, ylabel, color=None, figsize=(12,&nbsp;6), add_values=True):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u6761\u5f62\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp; ax = data.plot(kind='bar', color=color, edgecolor='white', linewidth=0.7, zorder=2)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u6dfb\u52a0\u6570\u503c\u6807\u7b7e\n&nbsp; &nbsp;&nbsp;if&nbsp;add_values:\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;for&nbsp;p&nbsp;in&nbsp;ax.patches:\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; height = p.get_height()\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ax.annotate(f'{height:.1f}',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (p.get_x() + p.get_width() \/&nbsp;2., height),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ha='center', va='center',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; xytext=(0,&nbsp;5),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; textcoords='offset points',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; fontsize=10,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; color='dimgrey')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp; plt.xticks(rotation=45, ha='right')\n&nbsp; &nbsp; plt.grid(axis='y', linestyle='--', alpha=0.7, zorder=1)\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\ndef&nbsp;plot_line(data_dict, title, xlabel, ylabel, figsize=(14,&nbsp;7)):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u6298\u7ebf\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u83b7\u53d6\u989c\u8272\u5faa\u73af\n&nbsp; &nbsp; colors = sns.color_palette(\"husl\", len(data_dict))\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;for&nbsp;i, (name, series)&nbsp;in&nbsp;enumerate(data_dict.items()):\n&nbsp; &nbsp; &nbsp; &nbsp; plt.plot(series.index, series.values,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;marker='o', markersize=8,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;linewidth=2.5,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;color=colors&#91;i],\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;label=name,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;zorder=3)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u56fe\u4f8b\n&nbsp; &nbsp; plt.legend(title='\u5458\u5de5',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;bbox_to_anchor=(1.02,&nbsp;1),&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;loc='upper left',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;frameon=True,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;framealpha=0.9,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;edgecolor='white')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u6dfb\u52a0\u7f51\u683c\n&nbsp; &nbsp; plt.grid(True, linestyle='--', alpha=0.6, zorder=1)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u5750\u6807\u8f74\n&nbsp; &nbsp; plt.gca().spines&#91;'top'].set_visible(False)\n&nbsp; &nbsp; plt.gca().spines&#91;'right'].set_visible(False)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\ndef&nbsp;plot_heatmap(pivot_table, title, xlabel, ylabel, figsize=(14,&nbsp;8)):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u70ed\u529b\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u521b\u5efa\u81ea\u5b9a\u4e49\u989c\u8272\u6620\u5c04\n&nbsp; &nbsp; cmap = sns.diverging_palette(220,&nbsp;20, as_cmap=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; ax = sns.heatmap(pivot_table,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;annot=True,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;fmt='.1f',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cmap=cmap,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;linewidths=0.5,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;linecolor='white',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cbar_kws={\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'label':&nbsp;'\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'shrink':&nbsp;0.8,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'format':&nbsp;'%.1f'\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;},\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;annot_kws={\"size\":&nbsp;10,&nbsp;\"color\":&nbsp;\"black\"})\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u5750\u6807\u8f74\u6807\u7b7e\n&nbsp; &nbsp; plt.xticks(rotation=45, ha='right')\n&nbsp; &nbsp; plt.yticks(rotation=0)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u8c03\u6574\u989c\u8272\u6761\u4f4d\u7f6e\n&nbsp; &nbsp; cbar = ax.collections&#91;0].colorbar\n&nbsp; &nbsp; cbar.ax.yaxis.label.set_size(12)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\ndef&nbsp;plot_stacked_bar(data, title, xlabel, ylabel, figsize=(14,&nbsp;7)):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u5806\u53e0\u6761\u5f62\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp; ax = data.plot(kind='bar', stacked=True,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;edgecolor='white', linewidth=0.7,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;zorder=2)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp; plt.xticks(rotation=45, ha='right')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u56fe\u4f8b\n&nbsp; &nbsp; plt.legend(title='\u5468\/\u6708',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;bbox_to_anchor=(1.02,&nbsp;1),&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;loc='upper left',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;frameon=True,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;framealpha=0.9)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u6dfb\u52a0\u7f51\u683c\n&nbsp; &nbsp; plt.grid(axis='y', linestyle='--', alpha=0.7, zorder=1)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u5750\u6807\u8f74\n&nbsp; &nbsp; plt.gca().spines&#91;'top'].set_visible(False)\n&nbsp; &nbsp; plt.gca().spines&#91;'right'].set_visible(False)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\n# \u4e3b\u7a0b\u5e8f\u903b\u8f91\nif&nbsp;__name__ ==&nbsp;'__main__':\n&nbsp; &nbsp;&nbsp;# \u52a0\u8f7d\u5e76\u5904\u7406\u6570\u636e\n&nbsp; &nbsp; df = load_and_preprocess_data(\"employee_attendance.xlsx\")\n&nbsp; &nbsp; print(df.head())\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 1. \u6bcf\u4e2a\u5458\u5de5\u7684\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; avg_work = df.groupby('Name')&#91;'Work Duration'].mean().sort_values(ascending=False)\n&nbsp; &nbsp; plot_bar(avg_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\u6392\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;color=sns.color_palette(\"viridis\", len(avg_work)),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;add_values=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 2. \u6bcf\u4e2a\u5458\u5de5\u7684\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; total_work = df.groupby('Name')&#91;'Work Duration'].sum().sort_values(ascending=False)\n&nbsp; &nbsp; plot_bar(total_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u603b\u5de5\u4f5c\u65f6\u957f\u6392\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;color=sns.color_palette(\"plasma\", len(total_work)),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;add_values=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 3. \u6bcf\u4e2a\u5458\u5de5\u6bcf\u5468\u7684\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u5806\u53e0\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; weekly_work = df.groupby(&#91;'Name',&nbsp;'Week'])&#91;'Work Duration'].sum().unstack(fill_value=0)\n&nbsp; &nbsp; plot_stacked_bar(weekly_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u5468\u5de5\u4f5c\u65f6\u957f\u5206\u5e03',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 4. \u6bcf\u4e2a\u5458\u5de5\u6bcf\u6708\u7684\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u5806\u53e0\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; monthly_work = df.groupby(&#91;'Name',&nbsp;'Month'])&#91;'Work Duration'].sum().unstack(fill_value=0)\n&nbsp; &nbsp; plot_stacked_bar(monthly_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u5206\u5e03',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 5. \u6bcf\u4e2a\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u8d8b\u52bf\u6298\u7ebf\u56fe\uff08\u4f18\u5316\u6298\u7ebf\u56fe\uff09\n&nbsp; &nbsp; monthly_trends = {name: group.groupby('Month')&#91;'Work Duration'].sum()\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;for&nbsp;name, group&nbsp;in&nbsp;df.groupby('Name')}\n&nbsp; &nbsp; plot_line(monthly_trends,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;'\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u8d8b\u52bf\u5206\u6790',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;'\u6708\u4efd',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 6. \u70ed\u529b\u56fe\uff1a\u6bcf\u5468\u5de5\u4f5c\u5206\u5e03\uff08\u4f18\u5316\u70ed\u529b\u56fe\uff09\n&nbsp; &nbsp; pivot_weekly = df.pivot_table(index='Name', columns='Week',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; values='Work Duration', aggfunc='sum', fill_value=0)\n&nbsp; &nbsp; plot_heatmap(pivot_weekly,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u5468\u5de5\u4f5c\u65f6\u957f\u70ed\u529b\u56fe',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5468\u6b21',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 7. \u70ed\u529b\u56fe\uff1a\u6bcf\u6708\u5de5\u4f5c\u5206\u5e03\uff08\u4f18\u5316\u70ed\u529b\u56fe\uff09\n&nbsp; &nbsp; pivot_monthly = df.pivot_table(index='Name', columns='Month',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;values='Work Duration', aggfunc='sum', fill_value=0)\n&nbsp; &nbsp; plot_heatmap(pivot_monthly,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u70ed\u529b\u56fe',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u6708\u4efd',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u65b0\u589e\uff1a\u5de5\u4f5c\u65e5\u5206\u5e03\u5206\u6790\uff08\u997c\u56fe\uff09\n&nbsp; &nbsp; weekday_counts = df&#91;'Weekday'].value_counts()\n&nbsp; &nbsp; plt.figure(figsize=(10,&nbsp;8), dpi=100)\n&nbsp; &nbsp; colors = sns.color_palette(\"pastel\")&#91;0:7]\n&nbsp; &nbsp; wedges, texts, autotexts = plt.pie(weekday_counts,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; autopct='%1.1f%%',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; startangle=90,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; colors=colors,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; wedgeprops={'linewidth':&nbsp;1,&nbsp;'edgecolor':&nbsp;'white'},\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; textprops={'fontsize':&nbsp;12})\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title('\u5de5\u4f5c\u65e5\u5206\u5e03\u6bd4\u4f8b', pad=20, fontweight='bold', fontsize=16)\n&nbsp; &nbsp; plt.legend(wedges, weekday_counts.index,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; title=\"\u661f\u671f\",\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; loc=\"center left\",\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; bbox_to_anchor=(1,&nbsp;0,&nbsp;0.5,&nbsp;1))\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u7f8e\u5316\u767e\u5206\u6bd4\u6807\u7b7e\n&nbsp; &nbsp;&nbsp;for&nbsp;autotext&nbsp;in&nbsp;autotexts:\n&nbsp; &nbsp; &nbsp; &nbsp; autotext.set_color('white')\n&nbsp; &nbsp; &nbsp; &nbsp; autotext.set_fontweight('bold')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>\u6848\u4f8b2\n\nimport&nbsp;pandas&nbsp;as&nbsp;pd\nimport&nbsp;matplotlib.pyplot&nbsp;as&nbsp;plt\nimport&nbsp;seaborn&nbsp;as&nbsp;sns\nimport&nbsp;numpy&nbsp;as&nbsp;np\nfrom&nbsp;matplotlib.ticker&nbsp;import&nbsp;FuncFormatter\n\n# \u8bbe\u7f6e\u5168\u5c40\u6837\u5f0f\u548c\u5b57\u4f53\nplt.style.use('seaborn-v0_8-whitegrid')\nsns.set_palette(\"husl\") &nbsp;# \u4f7f\u7528\u66f4\u534f\u8c03\u7684\u914d\u8272\u65b9\u6848\nplt.rcParams&#91;'font.sans-serif'] = &#91;'Microsoft YaHei',&nbsp;'SimHei',&nbsp;'Arial Unicode MS']\nplt.rcParams&#91;'axes.unicode_minus'] =&nbsp;False\nplt.rcParams&#91;'font.size'] =&nbsp;12\nplt.rcParams&#91;'axes.titlesize'] =&nbsp;16\nplt.rcParams&#91;'axes.labelsize'] =&nbsp;14\nplt.rcParams&#91;'xtick.labelsize'] =&nbsp;12\nplt.rcParams&#91;'ytick.labelsize'] =&nbsp;12\nplt.rcParams&#91;'legend.fontsize'] =&nbsp;12\n\ndef&nbsp;load_and_preprocess_data(file_path):\n&nbsp; &nbsp;&nbsp;\"\"\"\u52a0\u8f7d\u5e76\u9884\u5904\u7406\u6570\u636e\"\"\"\n&nbsp; &nbsp; df = pd.read_excel(file_path)\n&nbsp; &nbsp; df&#91;'CheckIn'] = pd.to_datetime(df&#91;'CheckIn'], errors='coerce')\n&nbsp; &nbsp; df&#91;'CheckOut'] = pd.to_datetime(df&#91;'CheckOut'], errors='coerce')\n&nbsp; &nbsp; df.dropna(subset=&#91;'CheckIn',&nbsp;'CheckOut'], inplace=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u8ba1\u7b97\u5de5\u4f5c\u65f6\u957f\n&nbsp; &nbsp; df&#91;'Work Duration'] = (df&#91;'CheckOut'] - df&#91;'CheckIn']).dt.total_seconds() \/&nbsp;3600\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u63d0\u53d6\u65f6\u95f4\u7ef4\u5ea6\n&nbsp; &nbsp; df&#91;'Week'] = df&#91;'CheckIn'].dt.isocalendar().week\n&nbsp; &nbsp; df&#91;'Month'] = df&#91;'CheckIn'].dt.month\n&nbsp; &nbsp; df&#91;'Weekday'] = df&#91;'CheckIn'].dt.day_name()\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;return&nbsp;df\n\ndef&nbsp;plot_bar(data, title, xlabel, ylabel, color=None, figsize=(12,&nbsp;6), add_values=True):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u6761\u5f62\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp; ax = data.plot(kind='bar', color=color, edgecolor='white', linewidth=0.7, zorder=2)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u6dfb\u52a0\u6570\u503c\u6807\u7b7e\n&nbsp; &nbsp;&nbsp;if&nbsp;add_values:\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;for&nbsp;p&nbsp;in&nbsp;ax.patches:\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; height = p.get_height()\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ax.annotate(f'{height:.1f}',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (p.get_x() + p.get_width() \/&nbsp;2., height),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ha='center', va='center',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; xytext=(0,&nbsp;5),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; textcoords='offset points',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; fontsize=10,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; color='dimgrey')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp; plt.xticks(rotation=45, ha='right')\n&nbsp; &nbsp; plt.grid(axis='y', linestyle='--', alpha=0.7, zorder=1)\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\ndef&nbsp;plot_line(data_dict, title, xlabel, ylabel, figsize=(14,&nbsp;7)):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u6298\u7ebf\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u83b7\u53d6\u989c\u8272\u5faa\u73af\n&nbsp; &nbsp; colors = sns.color_palette(\"husl\", len(data_dict))\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;for&nbsp;i, (name, series)&nbsp;in&nbsp;enumerate(data_dict.items()):\n&nbsp; &nbsp; &nbsp; &nbsp; plt.plot(series.index, series.values,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;marker='o', markersize=8,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;linewidth=2.5,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;color=colors&#91;i],\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;label=name,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;zorder=3)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u56fe\u4f8b\n&nbsp; &nbsp; plt.legend(title='\u5458\u5de5',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;bbox_to_anchor=(1.02,&nbsp;1),&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;loc='upper left',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;frameon=True,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;framealpha=0.9,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;edgecolor='white')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u6dfb\u52a0\u7f51\u683c\n&nbsp; &nbsp; plt.grid(True, linestyle='--', alpha=0.6, zorder=1)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u5750\u6807\u8f74\n&nbsp; &nbsp; plt.gca().spines&#91;'top'].set_visible(False)\n&nbsp; &nbsp; plt.gca().spines&#91;'right'].set_visible(False)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\ndef&nbsp;plot_heatmap(pivot_table, title, xlabel, ylabel, figsize=(14,&nbsp;8)):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u70ed\u529b\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u521b\u5efa\u81ea\u5b9a\u4e49\u989c\u8272\u6620\u5c04\n&nbsp; &nbsp; cmap = sns.diverging_palette(220,&nbsp;20, as_cmap=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; ax = sns.heatmap(pivot_table,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;annot=True,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;fmt='.1f',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cmap=cmap,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;linewidths=0.5,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;linecolor='white',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cbar_kws={\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'label':&nbsp;'\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'shrink':&nbsp;0.8,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'format':&nbsp;'%.1f'\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;},\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;annot_kws={\"size\":&nbsp;10,&nbsp;\"color\":&nbsp;\"black\"})\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u5750\u6807\u8f74\u6807\u7b7e\n&nbsp; &nbsp; plt.xticks(rotation=45, ha='right')\n&nbsp; &nbsp; plt.yticks(rotation=0)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u8c03\u6574\u989c\u8272\u6761\u4f4d\u7f6e\n&nbsp; &nbsp; cbar = ax.collections&#91;0].colorbar\n&nbsp; &nbsp; cbar.ax.yaxis.label.set_size(12)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\ndef&nbsp;plot_stacked_bar(data, title, xlabel, ylabel, figsize=(14,&nbsp;7)):\n&nbsp; &nbsp;&nbsp;\"\"\"\u4f18\u5316\u540e\u7684\u5806\u53e0\u6761\u5f62\u56fe\u7ed8\u5236\u51fd\u6570\"\"\"\n&nbsp; &nbsp; plt.figure(figsize=figsize, dpi=100)\n&nbsp; &nbsp; ax = data.plot(kind='bar', stacked=True,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;edgecolor='white', linewidth=0.7,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;zorder=2)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title(title, pad=20, fontweight='bold')\n&nbsp; &nbsp; plt.xlabel(xlabel, labelpad=10)\n&nbsp; &nbsp; plt.ylabel(ylabel, labelpad=10)\n&nbsp; &nbsp; plt.xticks(rotation=45, ha='right')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u56fe\u4f8b\n&nbsp; &nbsp; plt.legend(title='\u5468\/\u6708',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;bbox_to_anchor=(1.02,&nbsp;1),&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;loc='upper left',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;frameon=True,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;framealpha=0.9)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u6dfb\u52a0\u7f51\u683c\n&nbsp; &nbsp; plt.grid(axis='y', linestyle='--', alpha=0.7, zorder=1)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u4f18\u5316\u5750\u6807\u8f74\n&nbsp; &nbsp; plt.gca().spines&#91;'top'].set_visible(False)\n&nbsp; &nbsp; plt.gca().spines&#91;'right'].set_visible(False)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()\n\n# \u4e3b\u7a0b\u5e8f\u903b\u8f91\nif&nbsp;__name__ ==&nbsp;'__main__':\n&nbsp; &nbsp;&nbsp;# \u52a0\u8f7d\u5e76\u5904\u7406\u6570\u636e\n&nbsp; &nbsp; df = load_and_preprocess_data(\"employee_attendance.xlsx\")\n&nbsp; &nbsp; print(df.head())\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 1. \u6bcf\u4e2a\u5458\u5de5\u7684\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; avg_work = df.groupby('Name')&#91;'Work Duration'].mean().sort_values(ascending=False)\n&nbsp; &nbsp; plot_bar(avg_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\u6392\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5e73\u5747\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;color=sns.color_palette(\"viridis\", len(avg_work)),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;add_values=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 2. \u6bcf\u4e2a\u5458\u5de5\u7684\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; total_work = df.groupby('Name')&#91;'Work Duration'].sum().sort_values(ascending=False)\n&nbsp; &nbsp; plot_bar(total_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u603b\u5de5\u4f5c\u65f6\u957f\u6392\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;color=sns.color_palette(\"plasma\", len(total_work)),\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;add_values=True)\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 3. \u6bcf\u4e2a\u5458\u5de5\u6bcf\u5468\u7684\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u5806\u53e0\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; weekly_work = df.groupby(&#91;'Name',&nbsp;'Week'])&#91;'Work Duration'].sum().unstack(fill_value=0)\n&nbsp; &nbsp; plot_stacked_bar(weekly_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u5468\u5de5\u4f5c\u65f6\u957f\u5206\u5e03',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 4. \u6bcf\u4e2a\u5458\u5de5\u6bcf\u6708\u7684\u5de5\u4f5c\u65f6\u957f\uff08\u4f18\u5316\u5806\u53e0\u6761\u5f62\u56fe\uff09\n&nbsp; &nbsp; monthly_work = df.groupby(&#91;'Name',&nbsp;'Month'])&#91;'Work Duration'].sum().unstack(fill_value=0)\n&nbsp; &nbsp; plot_stacked_bar(monthly_work,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u5206\u5e03',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 5. \u6bcf\u4e2a\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u8d8b\u52bf\u6298\u7ebf\u56fe\uff08\u4f18\u5316\u6298\u7ebf\u56fe\uff09\n&nbsp; &nbsp; monthly_trends = {name: group.groupby('Month')&#91;'Work Duration'].sum()\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;for&nbsp;name, group&nbsp;in&nbsp;df.groupby('Name')}\n&nbsp; &nbsp; plot_line(monthly_trends,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;'\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u8d8b\u52bf\u5206\u6790',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;'\u6708\u4efd',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;'\u603b\u5de5\u4f5c\u65f6\u957f\uff08\u5c0f\u65f6\uff09')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 6. \u70ed\u529b\u56fe\uff1a\u6bcf\u5468\u5de5\u4f5c\u5206\u5e03\uff08\u4f18\u5316\u70ed\u529b\u56fe\uff09\n&nbsp; &nbsp; pivot_weekly = df.pivot_table(index='Name', columns='Week',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; values='Work Duration', aggfunc='sum', fill_value=0)\n&nbsp; &nbsp; plot_heatmap(pivot_weekly,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u5468\u5de5\u4f5c\u65f6\u957f\u70ed\u529b\u56fe',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5468\u6b21',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# 7. \u70ed\u529b\u56fe\uff1a\u6bcf\u6708\u5de5\u4f5c\u5206\u5e03\uff08\u4f18\u5316\u70ed\u529b\u56fe\uff09\n&nbsp; &nbsp; pivot_monthly = df.pivot_table(index='Name', columns='Month',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;values='Work Duration', aggfunc='sum', fill_value=0)\n&nbsp; &nbsp; plot_heatmap(pivot_monthly,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u6bcf\u6708\u5de5\u4f5c\u65f6\u957f\u70ed\u529b\u56fe',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u6708\u4efd',&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'\u5458\u5de5\u59d3\u540d')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u65b0\u589e\uff1a\u5de5\u4f5c\u65e5\u5206\u5e03\u5206\u6790\uff08\u997c\u56fe\uff09\n&nbsp; &nbsp; weekday_counts = df&#91;'Weekday'].value_counts()\n&nbsp; &nbsp; plt.figure(figsize=(10,&nbsp;8), dpi=100)\n&nbsp; &nbsp; colors = sns.color_palette(\"pastel\")&#91;0:7]\n&nbsp; &nbsp; wedges, texts, autotexts = plt.pie(weekday_counts,&nbsp;\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; autopct='%1.1f%%',\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; startangle=90,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; colors=colors,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; wedgeprops={'linewidth':&nbsp;1,&nbsp;'edgecolor':&nbsp;'white'},\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; textprops={'fontsize':&nbsp;12})\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.title('\u5de5\u4f5c\u65e5\u5206\u5e03\u6bd4\u4f8b', pad=20, fontweight='bold', fontsize=16)\n&nbsp; &nbsp; plt.legend(wedges, weekday_counts.index,\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; title=\"\u661f\u671f\",\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; loc=\"center left\",\n&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; bbox_to_anchor=(1,&nbsp;0,&nbsp;0.5,&nbsp;1))\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp;&nbsp;# \u7f8e\u5316\u767e\u5206\u6bd4\u6807\u7b7e\n&nbsp; &nbsp;&nbsp;for&nbsp;autotext&nbsp;in&nbsp;autotexts:\n&nbsp; &nbsp; &nbsp; &nbsp; autotext.set_color('white')\n&nbsp; &nbsp; &nbsp; &nbsp; autotext.set_fontweight('bold')\n&nbsp; &nbsp;&nbsp;\n&nbsp; &nbsp; plt.tight_layout()\n&nbsp; &nbsp; plt.show()<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>\u7b2c\u4e00\u6b65\uff1a\u6570\u636e\u52a0\u8f7d\u4e0e\u6e05\u6d17 \u9996\u5148\u6570\u636e\u662f\u4e00\u4efd Excel \u6587\u4ef6\uff0c\u6211\u4eec\u9700\u8981\u9884\u5904\u7406\uff0c\u5305\u542b\u4e86\u5458\u5de5\u7684\u6253\u5361\u8bb0\u5f55\uff0c\u5305\u62ec&hellip; <a href=\"http:\/\/viplao.com\/index.php\/2025\/09\/13\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%92%8c%e6%8c%96%e6%8e%98-%e5%88%86%e6%9e%90%e5%91%98%e5%b7%a5\/\" class=\"more-link read-more\" rel=\"bookmark\">\u7ee7\u7eed\u9605\u8bfb <span class=\"screen-reader-text\">\u3010Python\u5b9e\u8df5\u6848\u4f8b\u3011\u7535\u5546\u5e73\u53f0\u6570\u636e\u5206\u6790\u548c\u6316\u6398 -\u5206\u6790\u5458\u5de5\u51fa\u52e4<\/span><i class=\"fa fa-arrow-right\"><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[28],"views":386,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3878"}],"collection":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/comments?post=3878"}],"version-history":[{"count":3,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3878\/revisions"}],"predecessor-version":[{"id":3908,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3878\/revisions\/3908"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=3878"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=3878"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=3878"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}