298 lines
12 KiB
Python
298 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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根据需求清单（CSV/Excel）批量拼接 YouTube Studio 内容管理器 explore URL。
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用法:
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python build_studio_urls.py -i 需求清单.csv -o 输出.csv
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python build_studio_urls.py -i 需求清单.xlsx -o 输出.csv
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输入列（支持中英文别名，未填可留空）:
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所有者名称 : 所有者名称 / owner_name
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所有者ID : 所有者ID / owner_id / o
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实体名称 : 实体名称 / 群组名称 / 频道名称 / 节目名称 / entity_name / group_name
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实体ID : 实体ID / 群组ID / group_id / entity_id / id
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实体类型 : 实体类型 / 类型 / entity_type (群组/所有者/频道/节目，或 GROUP/CONTENT_OWNER/CHANNEL/VIDEO)
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数据周期 : 数据周期 / 周期 / period / time_period (yyyy.mm.dd-yyyy.mm.dd 或 yyyy.m.d-yyyy.m.d)
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国家 : 国家 / 国家/地区 / country / countries (一个或多个，中文名或 ISO 两位代码)
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说明:
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- 数据周期起止日期均包含在数据范围内，time_period 结束值取结束日后一天的日界线。
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- 国家为多个时，ur_values 以 '%27' 包裹、'%7C' 连接（如 美国,日本 -> %27US%27%7C%27JP%27）。
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- 中文国家名 -> ISO 代码的映射放在同目录 countries.json（可自行扩充）；已是两位代码的原样透传。
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- 固定参数（metric/granularity/dimension/t_metrics 等）在本文件 CONFIG 中统一配置。
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"""
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import argparse
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import json
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import os
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import re
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import sys
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from datetime import date
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import pandas as pd
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from urllib.parse import quote
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# ---------------------------------------------------------------------------
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# 固定参数（所有需求共用，按需修改）
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# ---------------------------------------------------------------------------
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CONFIG = {
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"explore_type": "TABLE_AND_CHART",
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"metric": "SUBSCRIBERS_NET_CHANGE", # 主指标
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"granularity": "DAY", # DAY / WEEK / MONTH / YEAR
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"dimension": "USER", # 细分维度
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"t_metrics": [ # 表格列指标（可多个）
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"SUBSCRIBERS_NET_CHANGE",
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"VIDEO_COUNT_FIRST_PUBLISHED",
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"ENGAGED_VIEWS",
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"EXTERNAL_VIEWS",
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"EXTERNAL_WATCH_TIME",
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"AVERAGE_WATCH_TIME",
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"TOTAL_ESTIMATED_EARNINGS",
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],
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"o_column": "SUBSCRIBERS_NET_CHANGE", # 排序字段
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"o_direction": "ANALYTICS_ORDER_DIRECTION_DESC", # DESC / ASC
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"comparison_type": "NONE",
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}
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# 实体类型 中文/代码 -> URL 参数值（可扩充）
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ENTITY_TYPE_MAP = {
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"群组": "GROUP", "GROUP": "GROUP",
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"所有者": "CONTENT_OWNER", "账号": "CONTENT_OWNER", "CONTENT_OWNER": "CONTENT_OWNER",
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"频道": "CHANNEL", "CHANNEL": "CHANNEL",
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"节目": "VIDEO", "视频": "VIDEO", "VIDEO": "VIDEO",
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}
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DEFAULT_ENTITY_TYPE = "GROUP"
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# 日界线锚点 + 整日偏移（见 docs/adr/0001）
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ANCHOR_DATE = date(2026, 6, 15)
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ANCHOR_MS = 1781506800000
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MS_PER_DAY = 86400000
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# 数据周期正则：支持 2026.07.01-2026.08.01 / 2026.7.1-2026.8.1 / 2026-07-01~2026-08-01
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PERIOD_RE = re.compile(
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r"(\d{4})[.\-/](\d{1,2})[.\-/](\d{1,2})\s*[-~～]\s*(\d{4})[.\-/](\d{1,2})[.\-/](\d{1,2})"
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)
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# 输入列名 -> 规范字段（键为去空白、转小写后的列名）
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COLUMN_ALIASES = {
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# 所有者
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"所有者名称": "owner_name", "ownername": "owner_name", "owner_name": "owner_name",
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"所有者": "owner_name",
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"所有者id": "owner_id", "ownerid": "owner_id", "owner_id": "owner_id", "o": "owner_id",
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# 实体
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"实体名称": "entity_name", "entity_name": "entity_name",
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"群组名称": "entity_name", "group_name": "entity_name", "groupname": "entity_name",
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"频道名称": "entity_name", "节目名称": "entity_name",
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"实体id": "entity_id", "entity_id": "entity_id", "entityid": "entity_id",
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"群组id": "entity_id", "group_id": "entity_id", "groupid": "entity_id",
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"群组": "entity_id", "id": "entity_id",
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"实体类型": "entity_type", "entity_type": "entity_type", "entitytype": "entity_type",
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"类型": "entity_type", "type": "entity_type",
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# 数据周期
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"数据周期": "period", "周期": "period", "period": "period",
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"time_period": "period", "timeperiod": "period", "日期范围": "period",
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# 国家
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"国家": "countries", "国家/地区": "countries", "国家地区": "countries",
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"countries": "countries", "country": "countries", "筛选国家": "countries", "地区": "countries",
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}
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def ts(y, m, d):
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"""日期 -> 日界线 Unix 毫秒（锚点 + 整日偏移）。"""
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return ANCHOR_MS + (date(y, m, d) - ANCHOR_DATE).days * MS_PER_DAY
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def normalize_columns(df):
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"""按别名把输入列映射到规范字段。返回 (field->column_index, 未识别列名列表)。"""
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mapping = {}
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unknown = []
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for col in df.columns:
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key = str(col).strip().lower()
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field = COLUMN_ALIASES.get(key)
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if field:
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mapping[field] = col
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else:
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unknown.append(str(col))
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return mapping, unknown
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def parse_period(text):
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"""解析数据周期，返回 (start_ms, end_ms)，起止日期均含。"""
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text = str(text).strip()
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m = PERIOD_RE.search(text)
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if not m:
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raise ValueError("无法解析数据周期: %r（应为 yyyy.mm.dd-yyyy.mm.dd）" % text)
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y1, m1, d1, y2, m2, d2 = (int(g) for g in m.groups())
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start_ms = ts(y1, m1, d1)
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end_ms = ts(y2, m2, d2) + MS_PER_DAY # 结束日包含
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return start_ms, end_ms
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def load_country_map(path):
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"""加载 中文国家名 -> ISO 代码 映射。文件缺失则仅支持两位代码透传。"""
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if not os.path.exists(path):
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print("[提示] 未找到国家映射文件 %s，仅支持直接填写两位 ISO 代码" % path, file=sys.stderr)
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return {}
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with open(path, "r", encoding="utf-8-sig") as f:
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data = json.load(f)
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return {str(k).strip(): str(v).strip().upper() for k, v in data.items()}
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def parse_countries(text, country_map):
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"""解析国家列（一个或多个，中文名或 ISO 代码），返回 ISO 代码列表。"""
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if text is None or (isinstance(text, float) and str(text) == "nan"):
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return []
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raw = str(text)
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parts = re.split(r"[,，、;；\s|]+", raw)
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codes = []
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for part in parts:
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p = part.strip().strip("'\"").strip()
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if not p:
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continue
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upper = p.upper()
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if re.fullmatch(r"[A-Z]{2}", upper): # 已是两位 ISO 代码
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codes.append(upper)
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elif p in country_map:
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codes.append(country_map[p])
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else:
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raise ValueError("未识别的国家: %r（不在映射文件中，也不是两位 ISO 代码）" % p)
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return codes
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def resolve_entity_type(text):
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"""实体类型 -> URL 参数值。空则用默认群组。"""
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if text is None or (isinstance(text, float) and str(text) == "nan") or str(text).strip() == "":
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return DEFAULT_ENTITY_TYPE
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key = str(text).strip()
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if key in ENTITY_TYPE_MAP:
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return ENTITY_TYPE_MAP[key]
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raise ValueError("未识别的实体类型: %r（应为 群组/所有者/频道/节目 或 GROUP/CONTENT_OWNER/CHANNEL/VIDEO）" % key)
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def build_url(row, country_map):
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"""根据一行需求生成 URL。返回 (url, 状态, 错误信息)。"""
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owner_id = row.get("owner_id", "").strip()
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entity_type = resolve_entity_type(row.get("entity_type", ""))
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entity_id = row.get("entity_id", "").strip()
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if not owner_id:
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return None, "error", "缺少所有者ID"
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if not entity_id:
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if entity_type == "CONTENT_OWNER":
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entity_id = owner_id # 账号整体场景回退
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else:
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return None, "error", "缺少实体ID"
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period = row.get("period", "").strip()
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if not period:
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return None, "error", "缺少数据周期"
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start_ms, end_ms = parse_period(period)
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codes = parse_countries(row.get("countries", ""), country_map)
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base = "https://studio.youtube.com/owner/%s/analytics/tab-overview/period-default/explore" % owner_id
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params = []
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params.append("o=%s" % owner_id)
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params.append("entity_type=%s" % entity_type)
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params.append("entity_id=%s" % entity_id)
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if codes:
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ur_values = quote("|".join("'%s'" % c for c in codes), safe="") # 'US'|'JP' -> %27US%27%7C%27JP%27
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params.append("ur_dimensions=COUNTRY")
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params.append("ur_values=%s" % ur_values)
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params.append("ur_inclusive_starts=")
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params.append("ur_exclusive_ends=")
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params.append("time_period=%d%%2C%d" % (start_ms, end_ms))
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params.append("explore_type=%s" % CONFIG["explore_type"])
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params.append("metric=%s" % CONFIG["metric"])
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params.append("granularity=%s" % CONFIG["granularity"])
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for m in CONFIG["t_metrics"]:
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params.append("t_metrics=%s" % m)
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params.append("dimension=%s" % CONFIG["dimension"])
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params.append("o_column=%s" % CONFIG["o_column"])
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params.append("o_direction=%s" % CONFIG["o_direction"])
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params.append("comparison_type=%s" % CONFIG["comparison_type"])
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url = base + "?" + "&".join(params)
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return url, "ok", ""
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def main():
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parser = argparse.ArgumentParser(description="批量拼接 YouTube Studio explore URL")
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parser.add_argument("-i", "--input", required=True, help="需求清单文件（.csv / .xlsx / .xls）")
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parser.add_argument("-o", "--output", default=None, help="输出 CSV 路径（默认：输入同目录 studio_urls_output.csv）")
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parser.add_argument("--countries", default=None, help="国家映射 JSON 路径（默认：脚本同目录 countries.json）")
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args = parser.parse_args()
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if not os.path.exists(args.input):
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print("错误：输入文件不存在: %s" % args.input, file=sys.stderr)
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sys.exit(1)
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ext = os.path.splitext(args.input)[1].lower()
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if ext == ".csv":
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try:
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df = pd.read_csv(args.input, encoding="utf-8-sig")
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except UnicodeDecodeError:
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df = pd.read_csv(args.input, encoding="gbk") # Excel 另存的 ANSI/GBK
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elif ext in (".xlsx", ".xls"):
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df = pd.read_excel(args.input)
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else:
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print("错误：不支持的输入格式: %s（支持 .csv/.xlsx/.xls）" % ext, file=sys.stderr)
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sys.exit(1)
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df = df.dropna(how="all") # 去掉全空行
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mapping, unknown = normalize_columns(df)
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if unknown:
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print("[提示] 未识别的列（忽略）: %s" % ", ".join(unknown), file=sys.stderr)
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missing = [f for f in ("owner_id", "period") if f not in mapping]
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if missing:
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print("错误：输入缺少必要列: %s" % ", ".join(missing), file=sys.stderr)
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sys.exit(1)
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countries_file = args.countries or os.path.join(os.path.dirname(os.path.abspath(__file__)), "countries.json")
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country_map = load_country_map(countries_file)
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records = []
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errors = []
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for idx, raw in df.iterrows():
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row = {f: ("" if pd.isna(raw[mapping[f]]) else str(raw[mapping[f]])) for f in mapping}
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try:
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url, status, msg = build_url(row, country_map)
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except ValueError as e:
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url, status, msg = None, "error", str(e)
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if status == "ok":
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start_ms, end_ms = parse_period(row.get("period", ""))
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codes = parse_countries(row.get("countries", ""), country_map)
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records.append({
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"所有者名称": row.get("owner_name", ""),
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"所有者ID": row.get("owner_id", ""),
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"实体类型": row.get("entity_type", ""),
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"实体名称": row.get("entity_name", ""),
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"实体ID": row.get("entity_id", ""),
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"数据周期": row.get("period", ""),
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"国家": row.get("countries", ""),
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"国家代码": ",".join(codes),
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"开始时间戳": start_ms,
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"结束时间戳": end_ms,
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"URL": url,
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})
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else:
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errors.append((idx + 2, row.get("owner_name", ""), row.get("entity_name", ""), msg))
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out_path = args.output or os.path.join(
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os.path.dirname(os.path.abspath(args.input)), "studio_urls_output.csv")
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out_df = pd.DataFrame(records)
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out_df.to_csv(out_path, index=False, encoding="utf-8-sig")
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print("已生成 %d 条 URL -> %s" % (len(records), out_path))
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if errors:
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print("\n以下 %d 行生成失败：" % len(errors), file=sys.stderr)
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for r in errors:
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print(" 第%d行 所有者=%s 实体=%s：%s" % r, file=sys.stderr)
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sys.exit(2)
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if __name__ == "__main__":
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main()
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