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@@ -0,0 +1,516 @@
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+#!/usr/bin/env python
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+"""Export historical hourly spend, revenue, CPM and ROI to a Feishu sheet."""
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+
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+from __future__ import annotations
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+
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+import argparse
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+import math
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+import re
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+from datetime import date, datetime, timedelta
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+from pathlib import Path
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+from typing import TYPE_CHECKING, Any
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+from zoneinfo import ZoneInfo
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+
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+import pandas as pd
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+from openpyxl import Workbook
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+from openpyxl.styles import Alignment, Font, PatternFill
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+from openpyxl.utils import get_column_letter
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+
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+if TYPE_CHECKING:
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+ from odps import ODPS
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+
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+
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+SHANGHAI = ZoneInfo("Asia/Shanghai")
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+ROOT = Path(__file__).resolve().parent
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+MINIAPP_CHANNEL = "小程序投流-稳定"
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+REVENUE_TABLE = "ads_ad_own_package_detail_15min"
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+COST_TABLE = "opengid_base_data"
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+DEFAULT_START_HOUR = 6
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+DEFAULT_END_HOUR = 22
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+
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+REPORT_HEADERS = [
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+ "日期",
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+ "小时",
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+ "总投放成本",
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+ "小程序投流成本",
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+ "商业化收入",
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+ "CPM",
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+ "收入/总投放",
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+ "收入/小程序成本",
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+ "当日累计总收入",
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+ "当日累计总投放成本",
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+ "当日累计小程序投流成本",
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+ "当日累计收入/总投放",
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+ "当日累计收入/小程序成本",
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+ "总去重UV",
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+ "总单用户成本",
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+ "小程序去重UV",
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+ "小程序单用户成本",
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+ "收入15分钟窗口数",
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+ "最新收入分区",
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+]
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+
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+
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+def parse_args() -> argparse.Namespace:
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+ parser = argparse.ArgumentParser(description=__doc__)
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+ parser.add_argument(
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+ "--end-date",
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+ default=datetime.now(SHANGHAI).strftime("%Y%m%d"),
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+ help="Inclusive end date in YYYYMMDD format; defaults to today.",
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+ )
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+ parser.add_argument(
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+ "--days",
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+ type=int,
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+ default=7,
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+ help="Number of calendar days to export, inclusive of --end-date.",
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+ )
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+ parser.add_argument(
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+ "--miniapp-channel",
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+ default=MINIAPP_CHANNEL,
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+ help="Channel counted as miniapp delivery cost.",
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+ )
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+ parser.add_argument(
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+ "--start-hour",
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+ type=int,
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+ default=DEFAULT_START_HOUR,
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+ help="First hourly bucket to export; defaults to 6.",
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+ )
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+ parser.add_argument(
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+ "--end-hour",
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+ type=int,
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+ default=DEFAULT_END_HOUR,
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+ help="Last hourly bucket to export, inclusive; defaults to 22.",
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+ )
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+ parser.add_argument(
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+ "--output",
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+ type=Path,
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+ help="Local xlsx output path; defaults to outputs/historical_hourly_roi_*.xlsx.",
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+ )
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+ parser.add_argument(
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+ "--skip-upload",
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+ action="store_true",
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+ help="Only generate the local xlsx; do not import it to Feishu.",
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+ )
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+ return parser.parse_args()
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+
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+
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+def _parse_date(value: str) -> date:
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+ if not re.fullmatch(r"\d{8}", value):
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+ raise ValueError("date must use YYYYMMDD format")
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+ return datetime.strptime(value, "%Y%m%d").date()
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+
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+
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+def _date_values(start_date: date, end_date: date) -> list[date]:
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+ days = (end_date - start_date).days
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+ return [start_date + timedelta(days=offset) for offset in range(days + 1)]
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+
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+
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+def fetch_revenue_rows(
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+ client: "ODPS",
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+ *,
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+ start_date: date,
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+ end_date: date,
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+) -> pd.DataFrame:
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+ sql = f"""
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+SELECT
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+ dt,
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+ report_date,
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+ MIN(NVL(daily_exposure_cnt, 0)) AS daily_exposure_cnt,
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+ MIN(NVL(overall_cpm, 0)) AS overall_cpm,
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+ MIN(NVL(package_cost_times_today, 0)) AS package_cost_times_today
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+FROM loghubods.{REVENUE_TABLE}
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+WHERE dt >= '{start_date:%Y%m%d}000000'
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+ AND dt <= '{end_date:%Y%m%d}235959'
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+ AND data_type = 'today'
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+ AND report_date IS NOT NULL
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+GROUP BY dt, report_date
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+ORDER BY report_date
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+""".strip()
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+ instance = client.execute_sql(sql, hints={"odps.sql.submit.mode": "script"})
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+ with instance.open_reader(tunnel=True) as reader:
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+ return reader.to_pandas()
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+
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+
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+def fetch_cost_rows(
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+ client: "ODPS",
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+ *,
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+ start_date: date,
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+ end_date: date,
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+ miniapp_channel: str,
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+) -> pd.DataFrame:
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+ escaped_channel = miniapp_channel.replace("'", "''")
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+ sql = f"""
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+SELECT
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+ dt AS data_date,
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+ CAST(`点击小时` AS BIGINT) AS hour_of_day,
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+ COUNT(DISTINCT mid) AS total_uv,
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+ SUM(NVL(`成本`, 0)) AS total_cost,
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+ CASE
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+ WHEN COUNT(DISTINCT mid) > 0
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+ THEN SUM(NVL(`成本`, 0)) / COUNT(DISTINCT mid)
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+ ELSE 0
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+ END AS total_unit_user_cost,
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+ COUNT(DISTINCT CASE WHEN channel = '{escaped_channel}' THEN mid ELSE NULL END)
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+ AS miniapp_uv,
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+ SUM(CASE WHEN channel = '{escaped_channel}' THEN NVL(`成本`, 0) ELSE 0 END)
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+ AS miniapp_cost,
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+ CASE
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+ WHEN COUNT(DISTINCT CASE WHEN channel = '{escaped_channel}' THEN mid ELSE NULL END) > 0
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+ THEN SUM(CASE WHEN channel = '{escaped_channel}' THEN NVL(`成本`, 0) ELSE 0 END)
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+ / COUNT(DISTINCT CASE WHEN channel = '{escaped_channel}' THEN mid ELSE NULL END)
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+ ELSE 0
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+ END AS miniapp_unit_user_cost
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+FROM loghubods.{COST_TABLE}
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+WHERE dt >= '{start_date:%Y%m%d}'
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+ AND dt <= '{end_date:%Y%m%d}'
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+ AND usersharedepth = '0'
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+ AND videoid IS NOT NULL
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+ AND NVL(hotsencetype, '') <> '1167'
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+GROUP BY dt, CAST(`点击小时` AS BIGINT)
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+ORDER BY dt, hour_of_day
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+""".strip()
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+ instance = client.execute_sql(sql, hints={"odps.sql.submit.mode": "script"})
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+ with instance.open_reader(tunnel=True) as reader:
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+ return reader.to_pandas()
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+
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+
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+def hourly_revenue(revenue_rows: pd.DataFrame) -> pd.DataFrame:
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+ columns = [
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+ "data_date",
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+ "hour_of_day",
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+ "commercial_revenue",
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+ "cpm",
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+ "revenue_window_count",
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+ "latest_revenue_partition",
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+ ]
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+ if revenue_rows.empty:
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+ return pd.DataFrame(columns=columns)
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+
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+ rows = revenue_rows.copy()
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+ rows["window_start"] = pd.to_datetime(
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+ rows["report_date"].astype(str),
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+ format="%Y%m%d%H%M%S",
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+ errors="coerce",
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+ )
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+ rows = rows.dropna(subset=["window_start"])
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+ rows["data_date"] = rows["window_start"].dt.strftime("%Y%m%d")
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+ rows["hour_of_day"] = rows["window_start"].dt.hour.astype("int64")
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+ rows["commercial_revenue"] = pd.to_numeric(
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+ rows["package_cost_times_today"],
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+ errors="coerce",
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+ ).fillna(0.0)
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+ rows["daily_exposure_cnt"] = pd.to_numeric(
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+ rows["daily_exposure_cnt"],
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+ errors="coerce",
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+ ).fillna(0.0)
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+ rows["overall_cpm"] = pd.to_numeric(rows["overall_cpm"], errors="coerce")
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+ rows["weighted_cpm"] = rows["overall_cpm"].fillna(0.0) * rows[
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+ "daily_exposure_cnt"
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+ ]
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+
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+ grouped = (
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+ rows.groupby(["data_date", "hour_of_day"], as_index=False)
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+ .agg(
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+ commercial_revenue=("commercial_revenue", "sum"),
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+ exposure=("daily_exposure_cnt", "sum"),
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+ weighted_cpm=("weighted_cpm", "sum"),
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+ avg_cpm=("overall_cpm", "mean"),
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+ revenue_window_count=("report_date", "count"),
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+ latest_revenue_partition=("dt", "max"),
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+ )
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+ .sort_values(["data_date", "hour_of_day"])
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+ )
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+ grouped["cpm"] = grouped.apply(
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+ lambda row: (
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+ row["weighted_cpm"] / row["exposure"]
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+ if row["exposure"] and not math.isnan(row["exposure"])
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+ else row["avg_cpm"]
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+ ),
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+ axis=1,
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+ )
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+ return grouped[columns]
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+
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+
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+def hourly_cost(cost_rows: pd.DataFrame) -> pd.DataFrame:
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+ columns = [
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+ "data_date",
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+ "hour_of_day",
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+ "total_cost",
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+ "miniapp_cost",
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+ "total_uv",
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+ "total_unit_user_cost",
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+ "miniapp_uv",
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+ "miniapp_unit_user_cost",
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+ ]
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+ if cost_rows.empty:
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+ return pd.DataFrame(columns=columns)
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+
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+ rows = cost_rows.copy()
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+ rows["data_date"] = rows["data_date"].astype(str)
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+ rows["hour_of_day"] = pd.to_numeric(rows["hour_of_day"], errors="coerce")
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+ rows = rows.dropna(subset=["hour_of_day"])
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+ rows["hour_of_day"] = rows["hour_of_day"].astype("int64")
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+ for column in columns[2:]:
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+ rows[column] = pd.to_numeric(rows[column], errors="coerce").fillna(0.0)
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+ return rows[columns].sort_values(["data_date", "hour_of_day"])
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+
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+
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+def build_hourly_report(
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+ *,
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+ revenue_rows: pd.DataFrame,
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+ cost_rows: pd.DataFrame,
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+ start_date: date,
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+ end_date: date,
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+ start_hour: int = DEFAULT_START_HOUR,
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+ end_hour: int = DEFAULT_END_HOUR,
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+) -> pd.DataFrame:
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+ if not 0 <= start_hour <= end_hour <= 23:
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+ raise ValueError("hours must satisfy 0 <= start_hour <= end_hour <= 23")
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+ # Build the full day first so cumulative values at 06:00 include 00:00-05:00.
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+ grid = pd.DataFrame(
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+ [
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+ {"data_date": f"{item:%Y%m%d}", "hour_of_day": hour}
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+ for item in _date_values(start_date, end_date)
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+ for hour in range(24)
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+ ]
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+ )
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+ report = grid.merge(
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+ hourly_cost(cost_rows),
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+ on=["data_date", "hour_of_day"],
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+ how="left",
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+ ).merge(
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+ hourly_revenue(revenue_rows),
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+ on=["data_date", "hour_of_day"],
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+ how="left",
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+ )
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+
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+ numeric_defaults = {
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+ "total_cost": 0.0,
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+ "miniapp_cost": 0.0,
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+ "commercial_revenue": 0.0,
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+ "total_uv": 0.0,
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+ "total_unit_user_cost": 0.0,
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+ "miniapp_uv": 0.0,
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+ "miniapp_unit_user_cost": 0.0,
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+ "revenue_window_count": 0.0,
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+ }
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+ for column, default in numeric_defaults.items():
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+ report[column] = pd.to_numeric(report[column], errors="coerce").fillna(default)
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+
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+ report["收入/总投放"] = report.apply(
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+ lambda row: (
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+ row["commercial_revenue"] / row["total_cost"]
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+ if row["total_cost"] > 0
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+ else None
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+ ),
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+ axis=1,
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+ )
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+ report["收入/小程序成本"] = report.apply(
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+ lambda row: (
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+ row["commercial_revenue"] / row["miniapp_cost"]
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+ if row["miniapp_cost"] > 0
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+ else None
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+ ),
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+ axis=1,
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+ )
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+ report = report.sort_values(["data_date", "hour_of_day"]).reset_index(drop=True)
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+ report["当日累计总收入"] = report.groupby("data_date")[
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+ "commercial_revenue"
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+ ].cumsum()
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+ report["当日累计总投放成本"] = report.groupby("data_date")[
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+ "total_cost"
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+ ].cumsum()
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+ report["当日累计小程序投流成本"] = report.groupby("data_date")[
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+ "miniapp_cost"
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+ ].cumsum()
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+ report["当日累计收入/总投放"] = report.apply(
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+ lambda row: (
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+ row["当日累计总收入"] / row["当日累计总投放成本"]
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+ if row["当日累计总投放成本"] > 0
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+ else None
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+ ),
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+ axis=1,
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+ )
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+ report["当日累计收入/小程序成本"] = report.apply(
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+ lambda row: (
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+ row["当日累计总收入"] / row["当日累计小程序投流成本"]
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+ if row["当日累计小程序投流成本"] > 0
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+ else None
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+ ),
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+ axis=1,
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+ )
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+ report["日期"] = pd.to_datetime(report["data_date"], format="%Y%m%d").dt.strftime(
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+ "%Y-%m-%d"
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+ )
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+ report["小时"] = report["hour_of_day"].map(lambda value: f"{int(value):02d}:00")
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+ report = report.rename(
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+ columns={
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+ "total_cost": "总投放成本",
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+ "miniapp_cost": "小程序投流成本",
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+ "commercial_revenue": "商业化收入",
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+ "cpm": "CPM",
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+ "total_uv": "总去重UV",
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+ "total_unit_user_cost": "总单用户成本",
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+ "miniapp_uv": "小程序去重UV",
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+ "miniapp_unit_user_cost": "小程序单用户成本",
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+ "revenue_window_count": "收入15分钟窗口数",
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+ "latest_revenue_partition": "最新收入分区",
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+ }
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+ )
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+ return report[
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+ report["hour_of_day"].between(start_hour, end_hour)
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+ ][REPORT_HEADERS]
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+
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+
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+def _clean_cell(value: Any) -> Any:
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+ if pd.isna(value):
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+ return ""
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+ if isinstance(value, float) and math.isfinite(value):
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+ return float(value)
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+ return value
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+
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+
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+def create_workbook(
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+ *,
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+ report: pd.DataFrame,
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+ start_date: date,
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+ end_date: date,
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+ miniapp_channel: str,
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+ output_path: Path,
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+ start_hour: int = DEFAULT_START_HOUR,
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+ end_hour: int = DEFAULT_END_HOUR,
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+) -> Path:
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+ output_path.parent.mkdir(parents=True, exist_ok=True)
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+ workbook = Workbook()
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+ sheet = workbook.active
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+ sheet.title = "历史小时ROI"
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+ sheet.append(REPORT_HEADERS)
|
|
|
+ for row in report.itertuples(index=False):
|
|
|
+ sheet.append([_clean_cell(value) for value in row])
|
|
|
+
|
|
|
+ header_fill = PatternFill("solid", fgColor="1F4E78")
|
|
|
+ for cell in sheet[1]:
|
|
|
+ cell.fill = header_fill
|
|
|
+ cell.font = Font(color="FFFFFF", bold=True)
|
|
|
+ cell.alignment = Alignment(horizontal="center", vertical="center")
|
|
|
+
|
|
|
+ widths = [
|
|
|
+ 13, 9, 14, 16, 14, 10, 14, 17, 16, 18, 22, 20, 24,
|
|
|
+ 11, 14, 13, 16, 17, 18,
|
|
|
+ ]
|
|
|
+ for index, width in enumerate(widths, start=1):
|
|
|
+ sheet.column_dimensions[get_column_letter(index)].width = width
|
|
|
+ for row in sheet.iter_rows(min_row=2):
|
|
|
+ for cell in row:
|
|
|
+ cell.alignment = Alignment(horizontal="center", vertical="center")
|
|
|
+ for column in ("C", "D", "E", "F", "I", "J", "K", "O", "Q"):
|
|
|
+ for cell in sheet[column][1:]:
|
|
|
+ cell.number_format = "0.00"
|
|
|
+ for column in ("G", "H", "L", "M"):
|
|
|
+ for cell in sheet[column][1:]:
|
|
|
+ cell.number_format = "0.0000"
|
|
|
+ for column in ("N", "P", "R"):
|
|
|
+ for cell in sheet[column][1:]:
|
|
|
+ cell.number_format = "0"
|
|
|
+ sheet.freeze_panes = "A2"
|
|
|
+ sheet.auto_filter.ref = sheet.dimensions
|
|
|
+
|
|
|
+ summary = workbook.create_sheet("运行摘要")
|
|
|
+ summary_rows = [
|
|
|
+ ("生成时间", datetime.now(SHANGHAI).strftime("%Y-%m-%d %H:%M:%S")),
|
|
|
+ ("统计开始日期", start_date.strftime("%Y-%m-%d")),
|
|
|
+ ("统计结束日期", end_date.strftime("%Y-%m-%d")),
|
|
|
+ ("每日统计时段", f"{start_hour:02d}:00-{end_hour:02d}:59"),
|
|
|
+ ("统计小时数", len(report)),
|
|
|
+ ("小程序成本渠道", miniapp_channel),
|
|
|
+ ("总投放成本", float(report["总投放成本"].sum())),
|
|
|
+ ("小程序投流成本", float(report["小程序投流成本"].sum())),
|
|
|
+ ("商业化收入", float(report["商业化收入"].sum())),
|
|
|
+ ]
|
|
|
+ summary.append(["字段", "值"])
|
|
|
+ for row in summary_rows:
|
|
|
+ summary.append(list(row))
|
|
|
+ for cell in summary[1]:
|
|
|
+ cell.fill = header_fill
|
|
|
+ cell.font = Font(color="FFFFFF", bold=True)
|
|
|
+ summary.column_dimensions["A"].width = 22
|
|
|
+ summary.column_dimensions["B"].width = 28
|
|
|
+ for cell in summary["B"][1:]:
|
|
|
+ if isinstance(cell.value, float):
|
|
|
+ cell.number_format = "0.00"
|
|
|
+
|
|
|
+ workbook.save(output_path)
|
|
|
+ return output_path
|
|
|
+
|
|
|
+
|
|
|
+def main() -> None:
|
|
|
+ args = parse_args()
|
|
|
+ if args.days <= 0:
|
|
|
+ raise ValueError("--days must be positive")
|
|
|
+ if not 0 <= args.start_hour <= args.end_hour <= 23:
|
|
|
+ raise ValueError(
|
|
|
+ "hours must satisfy 0 <= --start-hour <= --end-hour <= 23"
|
|
|
+ )
|
|
|
+
|
|
|
+ end_date = _parse_date(args.end_date)
|
|
|
+ start_date = end_date - timedelta(days=args.days - 1)
|
|
|
+
|
|
|
+ from dotenv import load_dotenv
|
|
|
+ from feishu_notifier import FeishuNotifier
|
|
|
+ from odps_source import build_odps_client
|
|
|
+
|
|
|
+ load_dotenv(ROOT.parent / "auto_put_ad_mini" / ".env", override=False)
|
|
|
+ load_dotenv(Path.cwd() / ".env", override=False)
|
|
|
+
|
|
|
+ client = build_odps_client()
|
|
|
+ revenue_rows = fetch_revenue_rows(
|
|
|
+ client,
|
|
|
+ start_date=start_date,
|
|
|
+ end_date=end_date,
|
|
|
+ )
|
|
|
+ cost_rows = fetch_cost_rows(
|
|
|
+ client,
|
|
|
+ start_date=start_date,
|
|
|
+ end_date=end_date,
|
|
|
+ miniapp_channel=args.miniapp_channel,
|
|
|
+ )
|
|
|
+ report = build_hourly_report(
|
|
|
+ revenue_rows=revenue_rows,
|
|
|
+ cost_rows=cost_rows,
|
|
|
+ start_date=start_date,
|
|
|
+ end_date=end_date,
|
|
|
+ start_hour=args.start_hour,
|
|
|
+ end_hour=args.end_hour,
|
|
|
+ )
|
|
|
+
|
|
|
+ output_path = args.output or (
|
|
|
+ ROOT
|
|
|
+ / "outputs"
|
|
|
+ / (
|
|
|
+ "historical_hourly_roi_"
|
|
|
+ f"{start_date:%Y%m%d}_{end_date:%Y%m%d}.xlsx"
|
|
|
+ )
|
|
|
+ )
|
|
|
+ path = create_workbook(
|
|
|
+ report=report,
|
|
|
+ start_date=start_date,
|
|
|
+ end_date=end_date,
|
|
|
+ miniapp_channel=args.miniapp_channel,
|
|
|
+ output_path=output_path,
|
|
|
+ start_hour=args.start_hour,
|
|
|
+ end_hour=args.end_hour,
|
|
|
+ )
|
|
|
+
|
|
|
+ print(f"rows={len(report)}")
|
|
|
+ print(f"date_range={start_date:%Y%m%d}-{end_date:%Y%m%d}")
|
|
|
+ print(f"output={path}")
|
|
|
+ if args.skip_upload:
|
|
|
+ print("sheet_url=SKIPPED")
|
|
|
+ return
|
|
|
+
|
|
|
+ url = FeishuNotifier().import_spreadsheet(path)
|
|
|
+ print(f"sheet_url={url}")
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ main()
|
wangyunpeng