346 lines
14 KiB
Python
346 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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渠道4-5月线索 - 退款相关性分析
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分析 Sheet1 退款状态与各维度的相关性：
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- 渠道、课包、电话接通
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- 用户注册时间、角色创建时间
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- U0行课行为（L1/L2各课完成情况）
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"""
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import pandas as pd
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import numpy as np
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from openpyxl import Workbook
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from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
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from openpyxl.utils.dataframe import dataframe_to_rows
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import glob, sys
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from datetime import datetime
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def load_data():
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files = glob.glob('/root/.openclaw/media/inbound/*d58af024*')
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if not files:
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print("未找到输入文件")
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sys.exit(1)
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input_file = files[0]
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print(f"读取: {input_file}")
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df1 = pd.read_excel(input_file, sheet_name='Sheet1', dtype=str)
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df2 = pd.read_excel(input_file, sheet_name='Sheet2', dtype=str)
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return df1, df2
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def prepare_data(df1, df2):
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"""准备匹配后的分析数据集"""
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# Sheet1 清洗
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df1['账号ID_clean'] = pd.to_numeric(df1['账号ID'], errors='coerce')
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df1['is_refund'] = df1['订单状态'].apply(lambda x: '是' if x == '已退款' else '否')
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# Sheet2 清洗 - 聚合到用户级别
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df2['用户ID_num'] = pd.to_numeric(df2['用户ID'], errors='coerce')
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# U0 10课的列
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u0_cols = ['L1_U00_L01_完成时间', 'L1_U00_L02_完成时间', 'L1_U00_L03_完成时间',
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'L1_U00_L04_完成时间', 'L1_U00_L05_完成时间',
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'L2_U00_L01_完成时间', 'L2_U00_L02_完成时间', 'L2_U00_L03_完成时间',
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'L2_U00_L04_完成时间', 'L2_U00_L05_完成时间']
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l1_cols = u0_cols[:5]
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l2_cols = u0_cols[5:]
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# 按用户聚合 Sheet2
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def agg_user(group):
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row = {}
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row['注册时间'] = group['用户注册时间'].iloc[0] # 同一用户注册时间相同
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# 最早角色创建时间
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create_times = pd.to_datetime(group['角色创建时间'], errors='coerce').dropna()
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row['最早角色创建时间'] = create_times.min() if len(create_times) > 0 else pd.NaT
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# U0行课：只要任一角色完成就算完成
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for c in u0_cols:
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if c in group.columns:
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vals = group[c].dropna()
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vals = vals[vals != '']
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vals = vals[vals != 'nan']
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row[c] = vals.iloc[0] if len(vals) > 0 else ''
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else:
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row[c] = ''
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# L1完成课数
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l1_done = 0
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for c in l1_cols:
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if c in group.columns:
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vals = group[c].dropna()
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vals = vals[vals != '']
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vals = vals[vals != 'nan']
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if len(vals) > 0:
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l1_done += 1
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row['L1完成课数'] = l1_done
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# L2完成课数
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l2_done = 0
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for c in l2_cols:
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if c in group.columns:
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vals = group[c].dropna()
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vals = vals[vals != '']
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vals = vals[vals != 'nan']
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if len(vals) > 0:
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l2_done += 1
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row['L2完成课数'] = l2_done
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row['U0总完成课数'] = l1_done + l2_done
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# 行课类型
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if l1_done > 0 and l2_done > 0:
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row['行课类型'] = 'L1+L2都有'
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elif l1_done > 0:
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row['行课类型'] = '仅L1'
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elif l2_done > 0:
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row['行课类型'] = '仅L2'
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else:
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row['行课类型'] = '无行课'
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# 是否有任何行课
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row['是否有行课'] = '有' if (l1_done + l2_done > 0) else '无'
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return pd.Series(row)
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df2_agg = df2.groupby('用户ID_num', as_index=False).apply(agg_user).reset_index()
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# 匹配
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merged = df1.merge(df2_agg, left_on='账号ID_clean', right_on='用户ID_num', how='left')
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merged['匹配状态'] = merged['用户ID_num'].notna().map({True: '已匹配', False: '未匹配'})
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return merged
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def calc_stats(df, col, refund_col='is_refund'):
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"""计算某维度的退款统计"""
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total = df.groupby(col).size().reset_index(name='总数')
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refund = df[df[refund_col] == '是'].groupby(col).size().reset_index(name='退款数')
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result = total.merge(refund, on=col, how='left')
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result['退款数'] = result['退款数'].fillna(0).astype(int)
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result['退费率'] = (result['退款数'] / result['总数'] * 100).round(1)
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result['退费率'] = result['退费率'].apply(lambda x: f'{x}%')
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result = result.sort_values('总数', ascending=False)
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return result
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def analyze(df):
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"""全面分析"""
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results = {}
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# 1. 渠道 vs 退款
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results['1-渠道'] = calc_stats(df, '渠道')
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# 2. 课包 vs 退款
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results['2-课包'] = calc_stats(df, '课包')
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# 3. 电话接通 vs 退款
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results['3-电话接通'] = calc_stats(df, '电话已接通')
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# 4. 渠道+课包 交叉 vs 退款
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cross = df.groupby(['渠道', '课包']).agg(
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总数=('is_refund', 'count'),
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退款数=('is_refund', lambda x: (x == '是').sum())
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).reset_index()
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cross['退费率'] = (cross['退款数'] / cross['总数'] * 100).round(1)
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cross['退费率'] = cross['退费率'].apply(lambda x: f'{x}%')
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results['4-渠道×课包交叉'] = cross.sort_values('总数', ascending=False)
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# 5. 电话接通+课包 交叉
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cross2 = df.groupby(['电话已接通', '课包']).agg(
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总数=('is_refund', 'count'),
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退款数=('is_refund', lambda x: (x == '是').sum())
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).reset_index()
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cross2['退费率'] = (cross2['退款数'] / cross2['总数'] * 100).round(1)
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cross2['退费率'] = cross2['退费率'].apply(lambda x: f'{x}%')
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results['5-电话接通×课包交叉'] = cross2.sort_values('总数', ascending=False)
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# 6. 用户注册时间（按月） vs 退款
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df['注册月份'] = pd.to_datetime(df['注册时间'], errors='coerce').dt.to_period('M').astype(str)
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df['注册月份'] = df['注册月份'].fillna('未知')
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results['6-注册月份'] = calc_stats(df, '注册月份')
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# 7. 角色创建时间（按月）
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df['角色创建月份'] = pd.to_datetime(df['最早角色创建时间'], errors='coerce').dt.to_period('M').astype(str)
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df['角色创建月份'] = df['角色创建月份'].fillna('未知')
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results['7-角色创建月份'] = calc_stats(df, '角色创建月份')
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# 8. 是否有行课 vs 退款
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results['8-是否有行课'] = calc_stats(df, '是否有行课')
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# 9. 行课类型 vs 退款
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results['9-行课类型'] = calc_stats(df, '行课类型')
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# 10. U0完成课数 vs 退款
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results['10-U0完成课数'] = calc_stats(df, 'U0总完成课数')
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# 11. L1完成课数 vs 退款
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results['11-L1完成课数'] = calc_stats(df, 'L1完成课数')
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# 12. L2完成课数 vs 退款
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results['12-L2完成课数'] = calc_stats(df, 'L2完成课数')
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# 13. 注册到创建时间差 vs 退款（天）
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df['注册时间_dt'] = pd.to_datetime(df['注册时间'], errors='coerce')
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df['角色创建时间_dt'] = pd.to_datetime(df['最早角色创建时间'], errors='coerce')
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df['注册到创建天数'] = (df['角色创建时间_dt'] - df['注册时间_dt']).dt.total_seconds() / 86400
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df['注册到创建天数'] = df['注册到创建天数'].fillna(-1)
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def day_bucket(d):
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if d < 0:
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return '未知'
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elif d <= 1:
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return '≤1天'
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elif d <= 7:
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return '2-7天'
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elif d <= 30:
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return '8-30天'
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elif d <= 90:
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return '31-90天'
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else:
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return '>90天'
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df['注册到创建间隔'] = df['注册到创建天数'].apply(day_bucket)
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results['13-注册到角色创建间隔'] = calc_stats(df, '注册到创建间隔')
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# 14. 匹配状态 vs 退款（未匹配的在Sheet2中找不到数据）
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results['14-匹配状态'] = calc_stats(df, '匹配状态')
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# 整体
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total_count = len(df)
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refund_count = (df['is_refund'] == '是').sum()
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results['_整体'] = {
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'总订单数': total_count,
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'退款订单数': refund_count,
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'退费率': f'{refund_count/total_count*100:.1f}%',
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'已匹配数': df['匹配状态'].value_counts().get('已匹配', 0),
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'未匹配数': df['匹配状态'].value_counts().get('未匹配', 0),
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}
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return results, df
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def write_excel(results, df, output_path):
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"""写入多Sheet Excel"""
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wb = Workbook()
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# 删除默认Sheet
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wb.remove(wb.active)
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header_font = Font(bold=True, size=11)
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header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')
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header_font_white = Font(bold=True, size=11, color='FFFFFF')
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thin_border = Border(
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left=Side(style='thin'), right=Side(style='thin'),
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top=Side(style='thin'), bottom=Side(style='thin')
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)
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red_fill = PatternFill(start_color='FFC7CE', end_color='FFC7CE', fill_type='solid')
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# 汇总页
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ws = wb.create_sheet('汇总')
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ws.append(['指标', '值'])
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for k, v in results['_整体'].items():
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ws.append([k, v])
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ws.append([])
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ws.append(['分析维度摘要'])
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# 各个维度的摘要
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for sheet_name, stat_df in results.items():
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if sheet_name.startswith('_'):
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continue
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if isinstance(stat_df, pd.DataFrame) and len(stat_df) > 0:
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# 找到退费率最高的
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if '退费率' in stat_df.columns:
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max_rate_row = stat_df.iloc[0]
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first_col = stat_df.columns[0]
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ws.append([sheet_name, f'{first_col}={max_rate_row[first_col]}: 退费率{max_rate_row["退费率"]} (退款{max_rate_row["退款数"]}/{max_rate_row["总数"]})'])
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for col in ws.columns:
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col[0].font = header_font
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ws.column_dimensions['A'].width = 30
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ws.column_dimensions['B'].width = 60
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# 各维度Sheet
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for sheet_name, stat_df in results.items():
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if sheet_name.startswith('_') or not isinstance(stat_df, pd.DataFrame):
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continue
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ws = wb.create_sheet(sheet_name[:31]) # Excel sheet name max 31 chars
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# 写入表头
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for col_idx, col_name in enumerate(stat_df.columns, 1):
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cell = ws.cell(row=1, column=col_idx, value=col_name)
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cell.font = header_font_white
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cell.fill = header_fill
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cell.border = thin_border
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cell.alignment = Alignment(horizontal='center')
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# 写入数据
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for row_idx, row in enumerate(stat_df.itertuples(index=False), 2):
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for col_idx, val in enumerate(row, 1):
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cell = ws.cell(row=row_idx, column=col_idx, value=val)
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cell.border = thin_border
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cell.alignment = Alignment(horizontal='center')
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# 高退费率标红 (>整体退费率)
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if '退费率' in stat_df.columns and col_idx == len(row):
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try:
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rate = float(str(val).replace('%', ''))
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if rate > 44.0: # 整体退费率约44%
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cell.fill = red_fill
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cell.font = Font(color='9C0006', bold=True)
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except:
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pass
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# 自动列宽
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for col_idx in range(1, len(stat_df.columns) + 1):
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max_len = max(
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len(str(stat_df.columns[col_idx-1])),
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stat_df.iloc[:, col_idx-1].astype(str).str.len().max()
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)
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ws.column_dimensions[chr(64+col_idx) if col_idx <= 26 else 'A'].width = min(max_len + 4, 30)
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# 明细页：所有匹配后的数据
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ws_detail = wb.create_sheet('匹配明细')
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detail_cols = ['下单日期', '手机号', '渠道', '课包', '订单状态', '电话已接通', '账号ID',
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'匹配状态', '注册时间', '最早角色创建时间', '行课类型', 'U0总完成课数',
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'L1完成课数', 'L2完成课数', '注册月份', '角色创建月份', '注册到创建间隔']
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detail_df = df[detail_cols].copy()
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for col_idx, col_name in enumerate(detail_df.columns, 1):
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cell = ws_detail.cell(row=1, column=col_idx, value=col_name)
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cell.font = header_font_white
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cell.fill = header_fill
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cell.border = thin_border
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cell.alignment = Alignment(horizontal='center')
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for row_idx, row in enumerate(detail_df.itertuples(index=False), 2):
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for col_idx, val in enumerate(row, 1):
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cell = ws_detail.cell(row=row_idx, column=col_idx, value=val if pd.notna(val) else '')
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cell.border = thin_border
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# 退款行标红背景
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if row[4] == '已退款':
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cell.fill = PatternFill(start_color='FFF2CC', end_color='FFF2CC', fill_type='solid')
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wb.save(output_path)
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print(f"报表已保存: {output_path}")
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def main():
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df1, df2 = load_data()
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merged = prepare_data(df1, df2)
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print(f"\n整体: {len(merged)}条, 已匹配{(merged['匹配状态']=='已匹配').sum()}, 未匹配{(merged['匹配状态']=='未匹配').sum()}")
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print(f"退款: {(merged['is_refund']=='是').sum()}, 未退款: {(merged['is_refund']=='否').sum()}")
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results, merged = analyze(merged)
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print(f"\n=== 整体 ===")
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for k, v in results['_整体'].items():
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print(f" {k}: {v}")
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print(f"\n=== 各维度退费率 ===")
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for name, stat in results.items():
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if name.startswith('_') or not isinstance(stat, pd.DataFrame):
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continue
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print(f"\n--- {name} ---")
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print(stat.to_string(index=False))
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ts = datetime.now().strftime('%Y%m%d_%H%M%S')
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output_path = f'/root/.openclaw/workspace/output/渠道4-5月线索_退款相关性分析_{ts}.xlsx'
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write_excel(results, merged, output_path)
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print(f"\n输出文件: {output_path}")
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return output_path
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if __name__ == '__main__':
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main()
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