统计每个 (channel, status) 组合的订单数，结果以两级索引的形式交付。
channel、status。n 的列里（可以交一个名为 n 的 Series，也可以交只有 n 一列的 DataFrame）。channel 为 null 的订单也要单独成一组，不能丢掉。输入
orders(order_id, customer_id, order_ts, status, channel)输出
index 为 [channel, status]，值列为 n。行顺序不限。
orders shop/orders
| # | order_idint64 | customer_idint64 | order_tsdatetime | statusstring | channelstring |
|---|---|---|---|---|---|
| 1 | 1001 | 39 | 2026-01-01 19:38:00 | paid | app |
| 2 | 1002 | 16 | 2026-01-17 14:07:00 | paid | app |
| 3 | 1003 | 4 | 2026-01-08 15:40:20 | cancelled | web |
| 4 | 1004 | 2 | 2026-05-23 10:48:58 | paid | null |
| 5 | 1005 | 12 | 2026-06-22 11:07:22 | paid | store |
| 6 | 1006 | 17 | 2026-08-15 05:15:25 | paid | web |
| 7 | 1007 | 9 | 2026-06-11 01:00:32 | refunded | store |
| 8 | 1008 | 45 | 2026-09-19 20:47:10 | paid | web |
| 9 | 1009 | 2 | 2025-10-05 04:53:38 | paid | app |
| 10 | 1010 | 4 | 2026-02-19 01:39:54 | paid | app |
| 11 | 1011 | 28 | 2026-03-05 03:04:45 | refunded | store |
| 12 | 1012 | 2 | 2026-08-01 07:09:17 | paid | web |
| 13 | 1013 | 26 | 2026-01-22 03:48:14 | refunded | store |
| 14 | 1014 | 2 | 2026-02-09 01:41:41 | paid | app |
| 15 | 1015 | 5 | 2026-08-03 19:49:04 | refunded | web |
| 16 | 1016 | 26 | 2026-02-16 00:43:43 | refunded | app |
| 17 | 1017 | 9 | 2025-12-06 04:48:57 | pending | app |
| 18 | 1018 | 999 | 2026-05-19 12:13:43 | paid | store |
| 19 | 1019 | 26 | 2026-02-05 15:02:26 | paid | store |
| 20 | 1020 | 44 | 2025-11-24 11:19:00 | cancelled | web |
| # | channelString | statusString | nInt64 |
|---|---|---|---|
| 1 | app | cancelled | 12 |
| 2 | app | paid | 45 |
| 3 | app | pending | 6 |
| 4 | app | refunded | 8 |
| 5 | phone | cancelled | 1 |
| 6 | phone | paid | 7 |
| 7 | phone | pending | 1 |
| 8 | store | cancelled | 3 |
| 9 | store | paid | 19 |
| 10 | store | pending | 4 |
| 11 | store | refunded | 6 |
| 12 | web | cancelled | 13 |
| 13 | web | paid | 38 |
| 14 | web | pending | 3 |
| 15 | web | refunded | 15 |
| 16 | null | cancelled | 2 |
| 17 | null | paid | 13 |
| 18 | null | refunded | 4 |