app02.py 4.0 KB

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  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. success_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. fail_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']]
  34. for file in files:
  35. file_item = os.path.join(root,file)
  36. print( file_item )
  37. df = pd.read_csv( file_item )
  38. #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']]
  39. for value in df.values:
  40. order_id = value[3].strip()
  41. #print( order_id )
  42. if order_id in r_dict:
  43. #print( order_id )
  44. pass
  45. elif value[8] == 6:
  46. success_result.append(value)
  47. elif value[8] == 4:
  48. fail_result.append(value)
  49. '''
  50. #print( result )
  51. dt = np.dtype((str, 32))
  52. f_value = np.array( result, dtype=dt)
  53. print( f_value )
  54. frame = pd.DataFrame(f_value)
  55. f_path = os.path.basename(file_item)
  56. print( f_path )
  57. write_path = os.path.join(out_path,f_path)
  58. print( write_path )
  59. frame.to_csv(write_path, index=False, header=0 , sep=',',encoding='utf-8')
  60. #f_path = os.path.splitext(file_item)
  61. #write_path = f_path[0] + ".xlsx"
  62. #print( write_path )
  63. #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'])
  64. #frame = pd.DataFrame(f_value)
  65. #frame.to_excel( write_path )
  66. '''
  67. #print( result )
  68. dt = np.dtype((str, 32))
  69. f_success_value = np.array(success_result, dtype=dt)
  70. f_fail_value = np.array(fail_result, dtype=dt)
  71. #print(f_success_value)
  72. success_frame = pd.DataFrame(f_success_value)
  73. fail_frame = pd.DataFrame(f_fail_value)
  74. total_fram = success_frame.append(fail_frame)
  75. f_path = os.path.basename(file_item)
  76. print( f_path )
  77. f_success_path = f_path.replace('.csv','成功订单.csv')
  78. f_fail_path = f_path.replace('.csv','失败订单.csv')
  79. print("f_success:%s"%f_success_path)
  80. write_success_path = os.path.join(out_path,f_success_path)
  81. write_fail_path = os.path.join(out_path,f_fail_path)
  82. write_total_path = os.path.join(out_path,f_path)
  83. print(write_success_path)
  84. success_frame.to_csv(write_success_path, index=False, header=0, sep=',', encoding='utf-8-sig')
  85. fail_frame.to_csv(write_fail_path, index=False, header=0, sep=',', encoding='utf-8-sig')
  86. total_fram.to_csv(write_total_path, index=False, header=0, sep=',', encoding='utf-8-sig')