{"id":4075,"date":"2025-10-18T11:57:50","date_gmt":"2025-10-18T03:57:50","guid":{"rendered":"http:\/\/viplao.com\/?p=4075"},"modified":"2025-10-18T11:58:29","modified_gmt":"2025-10-18T03:58:29","slug":"%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e8%bf%9b%e9%98%b6%e7%af%87%e3%80%91-%e8%87%aa%e5%8a%a8%e5%8c%96%e6%8a%a5%e5%91%8a%e7%94%9f%e6%88%90","status":"publish","type":"post","link":"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e8%bf%9b%e9%98%b6%e7%af%87%e3%80%91-%e8%87%aa%e5%8a%a8%e5%8c%96%e6%8a%a5%e5%91%8a%e7%94%9f%e6%88%90\/","title":{"rendered":"\u3010\u8fd0\u8425\u6570\u636e\u5206\u6790-\u8fdb\u9636\u7bc7\u3011 \u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210"},"content":{"rendered":"\n<p><strong>\u3010\u7406\u8bba\u8bb2\u89e3\u3011<\/strong><\/p>\n\n\n\n<p>\u6570\u636e\u5206\u6790\u7684\u6700\u7ec8\u76ee\u7684\u662f\u63d0\u4f9b\u6d1e\u5bdf\u548c\u5efa\u8bae\u3002\u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210\u53ef\u4ee5\u5c06\u5206\u6790\u7ed3\u679c\u3001\u5173\u952e\u6307\u6807\u548c\u56fe\u8868\u81ea\u52a8\u6c47\u603b\u6210\u62a5\u544a\uff0c\u5927\u5927\u8282\u7701\u4eba\u5de5\u5236\u4f5c\u62a5\u544a\u7684\u65f6\u95f4\uff0c\u5e76\u786e\u4fdd\u62a5\u544a\u7684\u4e00\u81f4\u6027\u3002<\/p>\n\n\n\n<p><strong>\u6838\u5fc3\u6280\u672f\uff1a<\/strong><\/p>\n\n\n\n<ul>\n<li><strong>Pandas\uff1a<\/strong>&nbsp;\u7528\u4e8e\u6570\u636e\u6c47\u603b\u548c\u751f\u6210\u8868\u683c\u3002<\/li>\n\n\n\n<li><strong>Matplotlib\/Seaborn\uff1a<\/strong>&nbsp;\u7528\u4e8e\u751f\u6210\u56fe\u8868\u3002<\/li>\n\n\n\n<li><strong>Python\u5e93\uff1a<\/strong>\n<ul>\n<li><code>openpyxl<\/code>&nbsp;\/&nbsp;<code>xlsxwriter<\/code>\uff1a\u751f\u6210Excel\u62a5\u544a\u3002<\/li>\n\n\n\n<li><code>ReportLab<\/code>&nbsp;\/&nbsp;<code>Fpdf<\/code>\uff1a\u751f\u6210PDF\u62a5\u544a\uff08\u66f4\u590d\u6742\uff0c\u5165\u95e8\u7ea7\u53ef\u5148\u7528Excel\uff09\u3002<\/li>\n\n\n\n<li><code>Jinja2<\/code>\uff1a\u7ed3\u5408HTML\u6a21\u677f\u751f\u6210\u52a8\u6001\u62a5\u544a\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p><strong>\u3010\u81ea\u52a8\u751f\u6210\u6570\u636e\u96c6\u4e0e\u4ee3\u7801\u5b9e\u4f8b\u3011<\/strong><\/p>\n\n\n\n<p>\u6211\u4eec\u5c06\u57fa\u4e8e\u524d\u9762\u5206\u6790\u7684\u8ba2\u5355\u6570\u636e\uff0c\u751f\u6210\u4e00\u4efd\u5305\u542b\u5173\u952e\u6307\u6807\u548c\u56fe\u8868\u7684Excel\u62a5\u544a\u3002<\/p>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime\nimport os\n\n# --- \u6570\u636e\u96c6\u751f\u6210 (\u590d\u7528\u8ba2\u5355\u6570\u636e) ---\nnp.random.seed(42)\n# \u5047\u8bbe df_orders \u662f\u7ecf\u8fc7\u6e05\u6d17\u548c\u7279\u5f81\u5de5\u7a0b\u7684\u8ba2\u5355\u6570\u636e\ndf_orders = generate_product_sales_data(num_orders=5000)\ndf_orders&#91;'order_date'] = df_orders&#91;'order_time'].dt.date # \u63d0\u53d6\u65e5\u671f\n\n# --- \u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210\u811a\u672c ---\nprint(\"\\n--- \u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210\u811a\u672c ---\")\n\ndef generate_ecommerce_report(df_data, report_date=None, output_folder='.\/reports\/'):\n    \"\"\"\n    \u751f\u6210\u7535\u5546\u8fd0\u8425\u65e5\u62a5\u3002\n    \"\"\"\n    if report_date is None:\n        report_date = datetime.now().date()\n    \n    if not os.path.exists(output_folder):\n        os.makedirs(output_folder)\n\n    report_filename = os.path.join(output_folder, f\"\u7535\u5546\u8fd0\u8425\u65e5\u62a5_{report_date.strftime('%Y%m%d')}.xlsx\")\n    \n    # \u4f7f\u7528Pandas ExcelWriter\u521b\u5efaExcel\u6587\u4ef6\n    with pd.ExcelWriter(report_filename, engine='xlsxwriter') as writer:\n        # --- 1. \u6574\u4f53\u8fd0\u8425\u6982\u89c8 ---\n        # \u8ba1\u7b97\u5173\u952e\u6307\u6807\n        total_revenue = df_data&#91;'total_revenue'].sum()\n        total_orders = df_data&#91;'order_id'].nunique()\n        avg_order_value = total_revenue \/ total_orders\n        \n        overview_data = pd.DataFrame({\n            '\u6307\u6807': &#91;'\u603b\u9500\u552e\u989d', '\u603b\u8ba2\u5355\u91cf', '\u5e73\u5747\u8ba2\u5355\u91d1\u989d'],\n            '\u6570\u503c': &#91;f'{total_revenue:.2f}\u5143', f'{total_orders}\u5355', f'{avg_order_value:.2f}\u5143']\n        })\n        overview_data.to_excel(writer, sheet_name='\u8fd0\u8425\u6982\u89c8', index=False)\n        \n        # --- 2. \u6bcf\u65e5\u9500\u552e\u8d8b\u52bf\u56fe ---\n        daily_sales_trend = df_data.groupby('order_date')&#91;'total_revenue'].sum().reset_index()\n        plt.figure(figsize=(10, 6))\n        sns.lineplot(x='order_date', y='total_revenue', data=daily_sales_trend, marker='o')\n        plt.title('\u6bcf\u65e5\u9500\u552e\u989d\u8d8b\u52bf')\n        plt.xlabel('\u65e5\u671f')\n        plt.ylabel('\u9500\u552e\u989d (\u5143)')\n        plt.xticks(rotation=45)\n        plt.grid(True)\n        plt.tight_layout()\n        \n        # \u5c06\u56fe\u8868\u4fdd\u5b58\u4e3a\u56fe\u7247\u5e76\u63d2\u5165Excel\n        chart_path = os.path.join(output_folder, 'daily_sales_trend.png')\n        plt.savefig(chart_path)\n        plt.close() # \u5173\u95ed\u56fe\u8868\uff0c\u91ca\u653e\u5185\u5b58\n        \n        worksheet = writer.sheets&#91;'\u8fd0\u8425\u6982\u89c8']\n        worksheet.insert_image('A6', chart_path) # \u63d2\u5165\u56fe\u7247\u5230A6\u5355\u5143\u683c\n        \n        # --- 3. \u5546\u54c1\u7c7b\u522b\u9500\u552e\u989d\u6392\u884c\u699c ---\n        category_sales = df_data.groupby('category')&#91;'total_revenue'].sum().sort_values(ascending=False).reset_index()\n        category_sales.to_excel(writer, sheet_name='\u5546\u54c1\u5206\u6790', index=False, startrow=0, startcol=0)\n\n        # \u7ed8\u5236\u54c1\u7c7b\u9500\u552e\u989d\u67f1\u72b6\u56fe\n        plt.figure(figsize=(10, 6))\n        sns.barplot(x='category', y='total_revenue', data=category_sales, palette='viridis')\n        plt.title('\u5404\u5546\u54c1\u7c7b\u522b\u603b\u9500\u552e\u989d')\n        plt.xlabel('\u5546\u54c1\u7c7b\u522b')\n        plt.ylabel('\u9500\u552e\u989d (\u5143)')\n        plt.xticks(rotation=45)\n        plt.tight_layout()\n        \n        chart_path_cat = os.path.join(output_folder, 'category_sales.png')\n        plt.savefig(chart_path_cat)\n        plt.close()\n        \n        worksheet_cat = writer.sheets&#91;'\u5546\u54c1\u5206\u6790']\n        worksheet_cat.insert_image('A10', chart_path_cat)\n\n        print(f\"\u62a5\u544a\u5df2\u751f\u6210: {report_filename}\")\n\n# \u8fd0\u884c\u62a5\u544a\u751f\u6210\u51fd\u6570\ngenerate_ecommerce_report(df_orders)\n\n# \u3010\u8fd0\u8425\u7b56\u7565\u5efa\u8bae\u3011\nprint(\"\\n--- \u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210\u7684\u8fd0\u8425\u4ef7\u503c ---\")\nprint(\"1. **\u8282\u7701\u65f6\u95f4:** \u6781\u5927\u51cf\u5c11\u4eba\u5de5\u5236\u4f5c\u62a5\u544a\u7684\u65f6\u95f4\uff0c\u63d0\u9ad8\u6548\u7387\u3002\")\nprint(\"2. **\u6570\u636e\u4e00\u81f4\u6027:** \u786e\u4fdd\u6bcf\u6b21\u62a5\u544a\u7684\u6570\u636e\u6765\u6e90\u548c\u8ba1\u7b97\u903b\u8f91\u4e00\u81f4\u3002\")\nprint(\"3. **\u53ca\u65f6\u6027:** \u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u6bcf\u5929\u3001\u6bcf\u5468\u81ea\u52a8\u751f\u6210\uff0c\u4fdd\u8bc1\u8fd0\u8425\u4eba\u5458\u53ca\u65f6\u83b7\u53d6\u6700\u65b0\u6570\u636e\u3002\")\nprint(\"4. **\u6807\u51c6\u5316:** \u7edf\u4e00\u62a5\u544a\u683c\u5f0f\uff0c\u4fbf\u4e8e\u56e2\u961f\u6210\u5458\u7406\u89e3\u548c\u6c9f\u901a\u3002\")<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4e92\u52a8\u95ee\u7b54\u3011<\/strong><\/p>\n\n\n\n<ul>\n<li>\u5728Python\u4e2d\uff0c\u9664\u4e86&nbsp;<code>xlsxwriter<\/code>\uff0c\u8fd8\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u6765\u64cd\u4f5cExcel\u6587\u4ef6\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u5c06\u591a\u4e2a\u56fe\u8868\u63d2\u5165\u5230Excel\u7684\u540c\u4e00\u4e2asheet\u4e2d\uff0c\u5e76\u8c03\u6574\u5b83\u4eec\u7684\u4f4d\u7f6e\uff1f<\/li>\n\n\n\n<li>\u5982\u679c\u9700\u8981\u751f\u6210PDF\u683c\u5f0f\u7684\u62a5\u544a\uff0cPython\u6709\u54ea\u4e9b\u5e38\u7528\u5e93\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u8ba9\u751f\u6210\u7684\u62a5\u544a\u66f4\u5177\u4ea4\u4e92\u6027\uff1f\uff08\u63d0\u793a\uff1a\u7ed3\u5408Excel\u7684\u900f\u89c6\u8868\u6216\u5b8f\uff09<\/li>\n\n\n\n<li>\u5728\u5b9e\u9645\u5de5\u4f5c\u4e2d\uff0c\u81ea\u52a8\u5316\u62a5\u544a\u901a\u5e38\u9700\u8981\u5305\u542b\u54ea\u4e9b\u5173\u952e\u90e8\u5206\uff1f<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>8.3 \u4ea4\u4e92\u5f0f\u4eea\u8868\u76d8\u521d\u6b65\uff08Streamlit\/Dash\u5165\u95e8\uff09<\/strong><\/h4>\n\n\n\n<p><strong>\u3010\u7406\u8bba\u8bb2\u89e3\u3011<\/strong><\/p>\n\n\n\n<p>\u867d\u7136\u81ea\u52a8\u5316\u62a5\u544a\u5f88\u65b9\u4fbf\uff0c\u4f46\u5b83\u901a\u5e38\u662f\u9759\u6001\u7684\u3002\u4ea4\u4e92\u5f0f\u4eea\u8868\u76d8\uff08Dashboard\uff09\u53ef\u4ee5\u8ba9\u4f60\u901a\u8fc7\u70b9\u51fb\u3001\u9009\u62e9\u7b49\u64cd\u4f5c\uff0c\u52a8\u6001\u5730\u63a2\u7d22\u6570\u636e\uff0c\u83b7\u5f97\u66f4\u6df1\u5c42\u6b21\u7684\u6d1e\u5bdf\u3002<\/p>\n\n\n\n<p><strong>\u6838\u5fc3\u601d\u60f3\uff1a<\/strong><\/p>\n\n\n\n<ul>\n<li><strong>\u6570\u636e\u53ef\u89c6\u5316\uff1a<\/strong>&nbsp;\u5c55\u793a\u5173\u952e\u6307\u6807\u548c\u8d8b\u52bf\u3002<\/li>\n\n\n\n<li><strong>\u4ea4\u4e92\u6027\uff1a<\/strong>&nbsp;\u7528\u6237\u53ef\u4ee5\u81ea\u5b9a\u4e49\u89c6\u56fe\uff0c\u5982\u9009\u62e9\u65e5\u671f\u8303\u56f4\u3001\u5546\u54c1\u7c7b\u522b\u3001\u7528\u6237\u7fa4\u4f53\u7b49\u3002<\/li>\n\n\n\n<li><strong>\u5b9e\u65f6\u6027\uff1a<\/strong>&nbsp;\u53ef\u4ee5\u8fde\u63a5\u5230\u5b9e\u65f6\u66f4\u65b0\u7684\u6570\u636e\u6e90\u3002<\/li>\n<\/ul>\n\n\n\n<p><strong>\u5e38\u7528\u5de5\u5177\uff1a<\/strong><\/p>\n\n\n\n<ul>\n<li><strong>Streamlit\uff1a<\/strong>&nbsp;\u7b80\u5355\u6613\u7528\uff0c\u7528\u7eafPython\u4ee3\u7801\u5feb\u901f\u6784\u5efaWeb\u5e94\u7528\u3002<\/li>\n\n\n\n<li><strong>Dash\uff1a<\/strong>&nbsp;\u57fa\u4e8eFlask, React.js, Plotly.js\uff0c\u529f\u80fd\u66f4\u5f3a\u5927\uff0c\u66f4\u7075\u6d3b\u3002<\/li>\n<\/ul>\n\n\n\n<p><strong>\u3010\u81ea\u52a8\u751f\u6210\u6570\u636e\u96c6\u4e0e\u4ee3\u7801\u5b9e\u4f8b\u3011<\/strong><\/p>\n\n\n\n<p>\u6211\u4eec\u5c06\u4f7f\u7528Streamlit\u6765\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7535\u5546\u8fd0\u8425\u4eea\u8868\u76d8\uff0c\u5c55\u793a\u9500\u552e\u989d\u8d8b\u52bf\u548c\u5546\u54c1\u7c7b\u522b\u9500\u552e\u60c5\u51b5\u3002<\/p>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime, timedelta\n\n# --- \u6570\u636e\u96c6\u751f\u6210 (\u590d\u7528\u8ba2\u5355\u6570\u636e) ---\nnp.random.seed(42)\n# \u5047\u8bbe df_orders \u662f\u7ecf\u8fc7\u6e05\u6d17\u548c\u7279\u5f81\u5de5\u7a0b\u7684\u8ba2\u5355\u6570\u636e\ndf_orders = generate_product_sales_data(num_orders=10000, start_date='2023-01-01', end_date='2023-06-30')\ndf_orders&#91;'order_date'] = df_orders&#91;'order_time'].dt.date # \u63d0\u53d6\u65e5\u671f\n\n# --- Streamlit \u4eea\u8868\u76d8\u4ee3\u7801 ---\n# \u8fd0\u884c\u65b9\u5f0f\uff1a\u5728\u547d\u4ee4\u884c\u4e2d\u8fdb\u5165\u4fdd\u5b58\u6b64\u811a\u672c\u7684\u76ee\u5f55\uff0c\u7136\u540e\u8fd0\u884c `streamlit run your_dashboard_script.py`\n\nst.set_page_config(layout=\"wide\") # \u8bbe\u7f6e\u9875\u9762\u5e03\u5c40\u4e3a\u5bbd\u5c4f\nst.title(\"\ud83d\udecd\ufe0f \u7535\u5546\u8fd0\u8425\u6570\u636e\u5206\u6790\u4eea\u8868\u76d8\")\n\n# --- \u4fa7\u8fb9\u680f\u8fc7\u6ee4\u5668 ---\nst.sidebar.header(\"\u6570\u636e\u7b5b\u9009\")\n\n# \u65e5\u671f\u8303\u56f4\u9009\u62e9\nmin_date = df_orders&#91;'order_date'].min()\nmax_date = df_orders&#91;'order_date'].max()\ndate_range = st.sidebar.date_input(\n    \"\u9009\u62e9\u65e5\u671f\u8303\u56f4\",\n    value=(min_date, max_date),\n    min_value=min_date,\n    max_value=max_date\n)\n\nif len(date_range) == 2:\n    start_date, end_date = date_range\n    df_filtered = df_orders&#91;(df_orders&#91;'order_date'] &gt;= start_date) &amp; (df_orders&#91;'order_date'] &lt;= end_date)]\nelse:\n    df_filtered = df_orders # \u5982\u679c\u53ea\u9009\u62e9\u4e86\u4e00\u4e2a\u65e5\u671f\uff0c\u5219\u4e0d\u8fc7\u6ee4\n\n# \u5546\u54c1\u7c7b\u522b\u9009\u62e9\nall_categories = &#91;'\u6240\u6709\u7c7b\u522b'] + sorted(df_filtered&#91;'category'].unique().tolist())\nselected_category = st.sidebar.selectbox(\"\u9009\u62e9\u5546\u54c1\u7c7b\u522b\", all_categories)\n\nif selected_category != '\u6240\u6709\u7c7b\u522b':\n    df_filtered = df_filtered&#91;df_filtered&#91;'category'] == selected_category]\n\n# --- \u4e3b\u5185\u5bb9\u533a\u57df ---\n\nst.header(\"\u5173\u952e\u8fd0\u8425\u6307\u6807\")\ncol1, col2, col3 = st.columns(3)\n\nwith col1:\n    total_revenue = df_filtered&#91;'total_revenue'].sum()\n    st.metric(\"\u603b\u9500\u552e\u989d\", f\"{total_revenue:,.2f} \u5143\")\n\nwith col2:\n    total_orders = df_filtered&#91;'order_id'].nunique()\n    st.metric(\"\u603b\u8ba2\u5355\u91cf\", f\"{total_orders:,} \u5355\")\n\nwith col3:\n    avg_order_value = total_revenue \/ total_orders if total_orders &gt; 0 else 0\n    st.metric(\"\u5e73\u5747\u8ba2\u5355\u91d1\u989d\", f\"{avg_order_value:,.2f} \u5143\")\n\nst.markdown(\"---\")\n\nst.header(\"\u9500\u552e\u8d8b\u52bf\u5206\u6790\")\n# \u6bcf\u65e5\u9500\u552e\u989d\u8d8b\u52bf\u56fe\ndaily_sales_trend = df_filtered.groupby('order_date')&#91;'total_revenue'].sum().reset_index()\nfig_trend, ax_trend = plt.subplots(figsize=(10, 5))\nsns.lineplot(x='order_date', y='total_revenue', data=daily_sales_trend, marker='o', ax=ax_trend)\nax_trend.set_title(f\"\u6bcf\u65e5\u9500\u552e\u989d\u8d8b\u52bf ({start_date.strftime('%Y-%m-%d')} \u81f3 {end_date.strftime('%Y-%m-%d')})\")\nax_trend.set_xlabel('\u65e5\u671f')\nax_trend.set_ylabel('\u9500\u552e\u989d (\u5143)')\nax_trend.tick_params(axis='x', rotation=45)\nst.pyplot(fig_trend)\n\n\nst.markdown(\"---\")\n\nst.header(\"\u5546\u54c1\u7c7b\u522b\u9500\u552e\u8868\u73b0\")\n# \u5546\u54c1\u7c7b\u522b\u9500\u552e\u989d\u67f1\u72b6\u56fe\ncategory_sales = df_filtered.groupby('category')&#91;'total_revenue'].sum().sort_values(ascending=False).reset_index()\nfig_cat, ax_cat = plt.subplots(figsize=(10, 5))\nsns.barplot(x='category', y='total_revenue', data=category_sales, palette='viridis', ax=ax_cat)\nax_cat.set_title(\"\u5404\u5546\u54c1\u7c7b\u522b\u603b\u9500\u552e\u989d\")\nax_cat.set_xlabel('\u5546\u54c1\u7c7b\u522b')\nax_cat.set_ylabel('\u9500\u552e\u989d (\u5143)')\nax_cat.tick_params(axis='x', rotation=45, ha='right')\nst.pyplot(fig_cat)\n\nst.markdown(\"---\")\n\nst.header(\"\u539f\u59cb\u6570\u636e\u9884\u89c8\")\nst.dataframe(df_filtered.head()) # \u5c55\u793a\u7b5b\u9009\u540e\u7684\u539f\u59cb\u6570\u636e\u524d\u51e0\u884c\n\n# \u3010\u8fd0\u8425\u7b56\u7565\u5efa\u8bae\u3011\nprint(\"\\n--- \u4ea4\u4e92\u5f0f\u4eea\u8868\u76d8\u7684\u8fd0\u8425\u4ef7\u503c ---\")\nprint(\"1. **\u5b9e\u65f6\u6d1e\u5bdf:** \u8fd0\u8425\u4eba\u5458\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\uff0c\u52a8\u6001\u8c03\u6574\u89c6\u56fe\uff0c\u5feb\u901f\u83b7\u53d6\u6240\u9700\u4fe1\u606f\u3002\")\nprint(\"2. **\u51b3\u7b56\u652f\u6301:** \u76f4\u89c2\u5c55\u793a\u5173\u952e\u6307\u6807\u548c\u8d8b\u52bf\uff0c\u8f85\u52a9\u5feb\u901f\u505a\u51fa\u8fd0\u8425\u51b3\u7b56\u3002\")\nprint(\"3. **\u6613\u4e8e\u5206\u4eab:** \u65b9\u4fbf\u56e2\u961f\u6210\u5458\u5171\u4eab\u548c\u7406\u89e3\u6570\u636e\u3002\")\nprint(\"4. **\u51cf\u5c11\u91cd\u590d\u5f00\u53d1:** \u4e00\u6b21\u5f00\u53d1\uff0c\u591a\u4eba\u4f7f\u7528\uff0c\u907f\u514d\u91cd\u590d\u5236\u4f5c\u62a5\u544a\u3002\")<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4e92\u52a8\u95ee\u7b54\u3011<\/strong><\/p>\n\n\n\n<ul>\n<li>Streamlit\u548cDash\u76f8\u6bd4\uff0c\u5404\u81ea\u7684\u4f18\u52bf\u548c\u52a3\u52bf\u662f\u4ec0\u4e48\uff1f\u5728\u4ec0\u4e48\u573a\u666f\u4e0b\u9009\u62e9\u54ea\u4e2a\u5de5\u5177\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u5c06\u66f4\u591a\u7684\u8fc7\u6ee4\u5668\uff08\u5982\u7528\u6237ID\u3001\u54c1\u724c\u3001\u652f\u4ed8\u65b9\u5f0f\uff09\u6dfb\u52a0\u5230Streamlit\u4eea\u8868\u76d8\u4e2d\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u5728\u4eea\u8868\u76d8\u4e2d\u96c6\u6210\u66f4\u591a\u7684\u56fe\u8868\u7c7b\u578b\uff0c\u4f8b\u5982RFM\u5206\u7fa4\u7684\u997c\u56fe\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u5c06\u4eea\u8868\u76d8\u90e8\u7f72\u5230\u7ebf\u4e0a\uff0c\u8ba9\u66f4\u591a\u4eba\u8bbf\u95ee\uff1f<\/li>\n\n\n\n<li>\u4ea4\u4e92\u5f0f\u4eea\u8868\u76d8\u5728\u7535\u5546\u8fd0\u8425\u7684\u65e5\u5e38\u5de5\u4f5c\u4e2d\u53ef\u4ee5\u53d1\u6325\u54ea\u4e9b\u5177\u4f53\u4f5c\u7528\uff1f<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u3010\u7406\u8bba\u8bb2\u89e3\u3011 \u6570\u636e\u5206\u6790\u7684\u6700\u7ec8\u76ee\u7684\u662f\u63d0\u4f9b\u6d1e\u5bdf\u548c\u5efa\u8bae\u3002\u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210\u53ef\u4ee5\u5c06\u5206\u6790\u7ed3\u679c\u3001\u5173\u952e\u6307\u6807\u548c\u56fe\u8868\u81ea\u52a8\u6c47&hellip; <a href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e8%bf%9b%e9%98%b6%e7%af%87%e3%80%91-%e8%87%aa%e5%8a%a8%e5%8c%96%e6%8a%a5%e5%91%8a%e7%94%9f%e6%88%90\/\" class=\"more-link read-more\" rel=\"bookmark\">\u7ee7\u7eed\u9605\u8bfb <span class=\"screen-reader-text\">\u3010\u8fd0\u8425\u6570\u636e\u5206\u6790-\u8fdb\u9636\u7bc7\u3011 \u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210<\/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":632,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4075"}],"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=4075"}],"version-history":[{"count":2,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4075\/revisions"}],"predecessor-version":[{"id":4099,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4075\/revisions\/4099"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=4075"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=4075"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=4075"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}