app02.py 3.0 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100
  1. import os
  2. import numpy as np
  3. import pandas as pd
  4. from sqlalchemy import create_engine
  5. ########################################################################################################
  6. # 将小米的订单中,属于小米后台的数据剔除
  7. # data目录:小米后台下载的对账单
  8. # 0730-0809目录:我方ftp下载的对账单
  9. # out目录:给小米生成的新的对账单
  10. ########################################################################################################
  11. curr_path = os.path.dirname(os.path.abspath(__file__))
  12. # 小米后台数据
  13. data_path = os.path.join(curr_path,"data")
  14. print( data_path )
  15. r_dict = {}
  16. for root,dirs,files in os.walk( data_path ):
  17. for file in files:
  18. file_item = os.path.join(root,file)
  19. print( file_item )
  20. df = pd.read_csv( file_item, usecols=[2] ,encoding='utf8')
  21. for value in df.values:
  22. order_id = value[0].strip()
  23. print( order_id )
  24. r_dict[ order_id ] = 1
  25. #print( r_dict )
  26. # 蓝色火焰后台数据
  27. my_path = os.path.join(curr_path,"0729-0824")
  28. out_path = os.path.join(curr_path,"out")
  29. print( my_path )
  30. print( out_path )
  31. for root,dirs,files in os.walk( my_path ):
  32. result = [['订单号','手机号码','规格','商户订单号','收单日期','回调日期','归属地','价格','充值状态','状态描述']]
  33. for file in files:
  34. file_item = os.path.join(root,file)
  35. print( file_item )
  36. df = pd.read_csv( file_item )
  37. #result = [['订单号','手机号码','规格','商户订单号','收单日期','回调日期','归属地','价格','充值状态','状态描述']]
  38. for value in df.values:
  39. order_id = value[3].strip()
  40. #print( order_id )
  41. if order_id in r_dict:
  42. #print( order_id )
  43. pass
  44. elif value[8] == 6:
  45. result.append( value )
  46. '''
  47. #print( result )
  48. dt = np.dtype((str, 32))
  49. f_value = np.array( result, dtype=dt)
  50. print( f_value )
  51. frame = pd.DataFrame(f_value)
  52. f_path = os.path.basename(file_item)
  53. print( f_path )
  54. write_path = os.path.join(out_path,f_path)
  55. print( write_path )
  56. frame.to_csv(write_path, index=False, header=0 , sep=',',encoding='utf-8')
  57. #f_path = os.path.splitext(file_item)
  58. #write_path = f_path[0] + ".xlsx"
  59. #print( write_path )
  60. #frame = pd.DataFrame(f_value, index=['订单号','手机号码','规格','商户订单号','收单日期','回调日期','归属地','价格','充值状态','状态描述'])
  61. #frame = pd.DataFrame(f_value)
  62. #frame.to_excel( write_path )
  63. '''
  64. #print( result )
  65. dt = np.dtype((str, 32))
  66. f_value = np.array( result, dtype=dt)
  67. print( f_value )
  68. frame = pd.DataFrame(f_value)
  69. f_path = os.path.basename(file_item)
  70. print( f_path )
  71. write_path = os.path.join(out_path,f_path)
  72. print( write_path )
  73. frame.to_csv(write_path, index=False, header=0 , sep=',',encoding='utf-8-sig')