1. import datetime
  3. def map_date_fields(data, field_mapping):
  4. """
  5. Maps date fields in a data structure based on a provided mapping.
  7. Args:
  8. data (dict or list of dict): The data structure containing date fields.
  9. field_mapping (dict): A dictionary mapping legacy field names to new field names.
  10. e.g., {'old_date_field': 'new_date_field'}
  12. Returns:
  13. dict or list of dict: The modified data structure with mapped date fields.
  14. """
  15. if isinstance(data, dict):
  16. new_data = {}
  17. for old_field, value in data.items():
  18. if old_field in field_mapping:
  19. new_field = field_mapping[old_field]
  20. if isinstance(value, str):
  21. try:
  22. # Attempt to parse the date string
  23. date_obj = datetime.datetime.strptime(value, '%Y-%m-%d') # Adjust format as needed
  24. new_data[new_field] = date_obj
  25. except ValueError:
  26. new_data[new_field] = None # Handle parsing errors
  27. else:
  28. new_data[new_field] = value #if already datetime object
  29. else:
  30. new_data[old_field] = value
  31. return new_data
  32. elif isinstance(data, list):
  33. new_data = []
  34. for item in data:
  35. new_item = map_date_fields(item, field_mapping)
  36. new_data.append(new_item)
  37. return new_data
  38. else:
  39. return data # Handle other data types if needed
  42. if __name__ == '__main__':
  43. # Example Usage
  44. legacy_data = {
  45. 'birthdate': '1990-01-15',
  46. 'report_date': '2023-10-26',
  47. 'expiration_date': '2024-12-31',
  48. 'other_field': 'some_value'
  49. }
  51. mapping = {
  52. 'birthdate': 'date_of_birth',
  53. 'report_date': 'report_date',
  54. 'expiration_date': 'expiry_date'
  55. }
  57. mapped_data = map_date_fields(legacy_data, mapping)
  58. print(mapped_data)
  60. legacy_data_list = [
  61. {'birthdate': '1985-05-20', 'report_date': '2022-03-10'},
  62. {'birthdate': '1998-11-01', 'report_date': '2023-07-18'}
  63. ]
  65. mapped_data_list = map_date_fields(legacy_data_list, mapping)
  66. print(mapped_data_list)

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