move claude-marketplace to ai-proj-helper
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308
plugins/feishu-plugin/migrate_wps.py
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308
plugins/feishu-plugin/migrate_wps.py
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#!/usr/bin/env python3
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"""
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WPS .dbt 文件迁移到飞书多维表格
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"""
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import pandas as pd
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import requests
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import json
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import re
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from datetime import datetime, timedelta
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from typing import List, Dict, Any, Optional
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# ========== 配置 ==========
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ZHIYUN_APP_ID = "cli_a9f29dca82b9dbef"
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ZHIYUN_APP_SECRET = "sDfhjG7QT1S4gfHiMVYSygmPQPN1R2Ho"
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BASE_URL = "https://open.feishu.cn/open-apis"
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# 源文件
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SOURCE_FILE = "/Users/donglinlai/Downloads/酷采团购系统优化进度表.dbt.xlsx"
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class FeishuBitable:
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"""飞书多维表格操作工具类"""
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def __init__(self):
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self._token = None
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self._token_expires = None
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@property
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def token(self) -> str:
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if self._token and self._token_expires and datetime.now() < self._token_expires:
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return self._token
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url = f"{BASE_URL}/auth/v3/tenant_access_token/internal"
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response = requests.post(url, json={
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"app_id": ZHIYUN_APP_ID,
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"app_secret": ZHIYUN_APP_SECRET
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})
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data = response.json()
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if data.get("code") != 0:
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raise Exception(f"获取 token 失败: {data}")
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self._token = data["tenant_access_token"]
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self._token_expires = datetime.now() + timedelta(seconds=data.get("expire", 7200) - 60)
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return self._token
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@property
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def headers(self):
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return {
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"Authorization": f"Bearer {self.token}",
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"Content-Type": "application/json"
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}
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def create_bitable(self, name: str) -> Dict:
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"""创建多维表格"""
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url = f"{BASE_URL}/bitable/v1/apps"
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response = requests.post(url, headers=self.headers, json={"name": name})
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data = response.json()
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if data.get("code") != 0:
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raise Exception(f"创建多维表格失败: {data}")
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return data["data"]["app"]
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def create_table(self, app_token: str, name: str, fields: List[Dict]) -> Dict:
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"""创建数据表"""
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url = f"{BASE_URL}/bitable/v1/apps/{app_token}/tables"
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payload = {
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"table": {
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"name": name,
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"default_view_name": "默认视图",
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"fields": fields
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}
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}
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response = requests.post(url, headers=self.headers, json=payload)
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data = response.json()
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if data.get("code") != 0:
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raise Exception(f"创建数据表失败: {data}")
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return data["data"]
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def batch_create_records(self, app_token: str, table_id: str,
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records: List[Dict], batch_size: int = 100) -> int:
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"""批量创建记录"""
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url = f"{BASE_URL}/bitable/v1/apps/{app_token}/tables/{table_id}/records/batch_create"
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total_created = 0
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for i in range(0, len(records), batch_size):
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batch = records[i:i+batch_size]
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payload = {"records": [{"fields": r} for r in batch]}
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response = requests.post(url, headers=self.headers, json=payload)
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data = response.json()
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if data.get("code") != 0:
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print(f" [WARN] 批次 {i//batch_size + 1} 部分失败: {data.get('msg', '')}")
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# 尝试逐条插入
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for record in batch:
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try:
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single_url = f"{BASE_URL}/bitable/v1/apps/{app_token}/tables/{table_id}/records"
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single_resp = requests.post(single_url, headers=self.headers, json={"fields": record})
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if single_resp.json().get("code") == 0:
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total_created += 1
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except:
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pass
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else:
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total_created += len(data["data"]["records"])
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return total_created
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def analyze_column_type(series: pd.Series, col_name: str) -> Dict:
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"""分析列的数据类型，返回飞书字段定义"""
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col_lower = col_name.lower()
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# 根据列名判断类型
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if any(kw in col_lower for kw in ['日期', '时间', 'date', 'time', '提出时间', '发版日期', '更新时间']):
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return {"field_name": col_name, "type": 5, "property": {"date_formatter": "yyyy/MM/dd"}}
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if any(kw in col_lower for kw in ['图片', '附件', '截图', 'image', 'file', 'attachment']):
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return {"field_name": col_name, "type": 1} # 作为文本处理
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if any(kw in col_lower for kw in ['优先级', '状态', '类型', '分类', '终端', '严重程度']):
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# 提取唯一值作为选项
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unique_vals = series.dropna().astype(str).unique()
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unique_vals = [v for v in unique_vals if v and v != 'nan' and len(v) < 50][:20]
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if len(unique_vals) > 0 and len(unique_vals) <= 20:
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return {
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"field_name": col_name,
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"type": 3, # 单选
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"property": {
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"options": [{"name": str(v)} for v in unique_vals]
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}
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}
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if any(kw in col_lower for kw in ['进度', '百分比', '%']):
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return {"field_name": col_name, "type": 2} # 数字
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# 检查是否为数字列
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try:
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numeric_vals = pd.to_numeric(series.dropna(), errors='coerce')
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if numeric_vals.notna().sum() / max(len(series.dropna()), 1) > 0.8:
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return {"field_name": col_name, "type": 2} # 数字
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except:
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pass
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# 默认为文本
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return {"field_name": col_name, "type": 1}
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def clean_value(val: Any, field_type: int) -> Any:
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"""清理和转换值"""
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if pd.isna(val) or val is None:
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return None
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if field_type == 5: # 日期
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try:
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if isinstance(val, (datetime, pd.Timestamp)):
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return int(val.timestamp() * 1000)
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elif isinstance(val, str):
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dt = pd.to_datetime(val)
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return int(dt.timestamp() * 1000)
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except:
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return None
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if field_type == 2: # 数字
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try:
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return float(val)
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except:
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return None
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if field_type == 3: # 单选
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val_str = str(val).strip()
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if val_str and val_str != 'nan':
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return val_str
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return None
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# 文本类型
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val_str = str(val).strip()
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if val_str == 'nan' or not val_str:
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return None
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# 限制文本长度
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if len(val_str) > 10000:
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val_str = val_str[:10000] + "..."
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return val_str
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def migrate_sheet(bitable: FeishuBitable, app_token: str,
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df: pd.DataFrame, sheet_name: str) -> str:
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"""迁移单个 Sheet 到数据表"""
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print(f"\n{'='*50}")
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print(f"迁移 Sheet: 【{sheet_name}】")
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print(f"{'='*50}")
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# 清理列名
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df.columns = [str(c).strip() for c in df.columns]
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# 去除完全空的行
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df = df.dropna(how='all')
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print(f" 数据: {len(df)} 行, {len(df.columns)} 列")
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# 分析字段类型
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fields = []
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field_types = {}
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for col in df.columns:
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if not col or col.startswith('Unnamed'):
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continue
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field_def = analyze_column_type(df[col], col)
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fields.append(field_def)
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field_types[col] = field_def["type"]
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print(f" 字段: {len(fields)} 个")
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# 创建数据表
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table_info = bitable.create_table(app_token, sheet_name, fields)
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table_id = table_info["table_id"]
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print(f" [OK] 数据表创建成功: {table_id}")
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# 准备记录数据
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records = []
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for _, row in df.iterrows():
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record = {}
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for col in df.columns:
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if not col or col.startswith('Unnamed'):
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continue
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val = clean_value(row[col], field_types.get(col, 1))
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if val is not None:
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record[col] = val
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if record: # 只添加非空记录
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records.append(record)
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print(f" 准备导入 {len(records)} 条记录...")
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# 批量创建记录
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if records:
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created = bitable.batch_create_records(app_token, table_id, records)
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print(f" [OK] 成功导入 {created}/{len(records)} 条记录")
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else:
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print(f" [INFO] 无有效数据")
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return table_id
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def main():
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print("\n" + "#" * 60)
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print("# WPS 文件迁移到飞书多维表格")
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print("#" * 60)
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bitable = FeishuBitable()
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# Step 1: 读取源文件
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print("\n" + "=" * 50)
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print("Step 1: 读取源文件")
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print("=" * 50)
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xlsx = pd.ExcelFile(SOURCE_FILE)
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print(f" 文件: {SOURCE_FILE}")
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print(f" Sheet 数量: {len(xlsx.sheet_names)}")
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# Step 2: 创建多维表格
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print("\n" + "=" * 50)
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print("Step 2: 创建飞书多维表格")
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print("=" * 50)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M")
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bitable_name = f"酷采团购系统优化进度表 (迁移 {timestamp})"
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app_info = bitable.create_bitable(bitable_name)
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app_token = app_info["app_token"]
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print(f" [OK] 多维表格创建成功")
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print(f" 名称: {bitable_name}")
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print(f" app_token: {app_token}")
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# Step 3: 迁移每个 Sheet
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print("\n" + "=" * 50)
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print("Step 3: 迁移数据表")
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print("=" * 50)
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table_ids = {}
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for sheet_name in xlsx.sheet_names:
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df = pd.read_excel(xlsx, sheet_name=sheet_name)
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table_id = migrate_sheet(bitable, app_token, df, sheet_name)
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table_ids[sheet_name] = table_id
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# 完成
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print("\n" + "=" * 60)
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print("迁移完成!")
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print("=" * 60)
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print(f"\n多维表格信息:")
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print(f" 名称: {bitable_name}")
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print(f" app_token: {app_token}")
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print(f"\n数据表:")
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for name, tid in table_ids.items():
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print(f" - {name}: {tid}")
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print(f"\n访问地址:")
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print(f" https://zhiyuncai.feishu.cn/base/{app_token}")
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print()
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return app_token, table_ids
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if __name__ == "__main__":
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main()
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