处理

coop.py

 # coding: utf-8
-# 回款明细
+# spa查询
 import math
 import os
 import shutil
@@ -115,7 +115,7 @@ def main():
     relation_data = asin_sku_relations()  # 获取 ASIN 与 SKU 的对应关系数据
     coop_list = export_list_read_data()  # 获取合作数据列表
     coop_list = coop_list[:10]
-    print(f"共计:{len(coop_list)},条数据")
+    print(f"共计:{len(coop_list)} 条数据")
 
     i = 0
     new_coop_data = []

invoices.py

@@ -28,7 +28,7 @@ warnings.filterwarnings("ignore", category=UserWarning, module="openpyxl")
 
 
 def export_list_read_data():
-    file_name = 'Payments.xlsx'
+    file_name = 'new_Payments.xlsx'
     if os.path.isfile(file_name):
         df = pd.read_excel(file_name)
         return df
@@ -36,15 +36,30 @@ def export_list_read_data():
     page.get(f"https://vendorcentral.amazon.com/hz/vendor/members/remittance/home", timeout=3)
     page.ele("#remittance-home-select-all", timeout=2).click()
     page.ele("#remittance-home-export-link", timeout=2).click.to_download()
-    file.wait_for_downloads(file_name)
+    # file.wait_for_downloads(file_name)
+
+    all_df = pd.read_excel('Payments.xlsx', header=None)
+
+    # 找到所有空行的索引，这里假设完全空的行为表头之间的分界线
+    empty_rows = all_df[all_df.isnull().all(axis=1)].index.tolist()
+
+    # 定位表头与数据的分隔
+    first_header_start = empty_rows[1] + 1  # 第一个表头开始的行
+    second_header_start = empty_rows[2] + 3  # 第二个表头开始的行
+
+    first_df = pd.read_excel('Payments.xlsx', skiprows=first_header_start, nrows=second_header_start - 7)
+    second_df = pd.read_excel('Payments.xlsx', skiprows=second_header_start)
 
-    df = pd.read_excel(file_name, skiprows=21)
     # 定义正则表达式模式，匹配包含 'Price' 或 'PCR' 或 'XXXXXXXX/XXXX/' 的描述
     pattern = r'Price Claim|PCR|Missed Adjustment|Shortage Claim|^[A-Z0-9]{8}/[A-Z0-9]{4}/'
     # 过滤符合条件的行
-    data = df[df['Description'].str.contains(pattern, na=False, regex=True)]
-    excel.save_xls(data,"Payments.xlsx","Remittance payments")
-    return df
+    filtered_second_df  = second_df[second_df['Description'].str.contains(pattern, na=False, regex=True)]
+
+    merged_df = pd.merge(filtered_second_df, first_df[['Payment Number', 'Payment Date']], on='Payment Number',how='left')
+
+    excel.save_xls(merged_df ,"new_Payments.xlsx","Remittance payments")
+
+    return merged_df
 
 def export_details_read_data(invoice_number):
     # 读取详情内容