从字典中获取与格式匹配的密钥字符串-python

时间:2019-02-25 19:08:12

标签: python python-3.x

我的列表包含以下字典。

[OrderedDict([('Employee Number', '1'), ('Employee Name', 'Ms. A'), ('RMG SPOC', 'X'), ('Total Experience (yrs)', '3.06'), ('Days Unallocated', '18'), ('Skill Details', 'Manual testing'), ('Contact Number', '1234')]), OrderedDict([('Employee Number', '2'), ('Employee Name', 'Mr. B'), ('RMG SPOC', 'Y'), ('Total Experience (yrs)', '2.51'), ('Days Unallocated', '28'), ('Skill Details', 'Manual Testing'), ('Contact Number', '2345')]), OrderedDict([('Employee Number', '3'), ('Employee Name', 'Mr. C'), ('RMG SPOC', 'Z'), ('Total Experience (yrs)', '1.86'), ('Days Unallocated', '9'), ('Skill Details', 'C++, Manual Testing, Oracle'), ('Contact Number', '4567')]), OrderedDict([('Employee Number', '4'), ('Employee Name', 'Mr. D'), ('RMG SPOC', 'xyz'), ('Total Experience (yrs)', '7.68'), ('Days Unallocated', '23'), ('Skill Details', 'Manual Testing, SQL, HCM'), ('Contact Number', '789')])]

我正在通过使用上述数据的for循环准备字典来将这些值推入数据库。

emp_data = {"employee_name" : data['Employee Name'],
            "employee_number" : data['Employee Number'],
            "date_added" : datetime.datetime.now(),
            "rmg_spoc" : data['RMG SPOC'],
            "status" : "To be evaluated",
            "total_experience" : data['Total Experience (yrs)'],
            "days_unallocated" : data['Days Unallocated'],
            "skill_details" : data['Skill Details'],
            "contact_number" : data['Contact Number'],
            "reviewer" : "To be assigned",
            "comments" : "To be added"}

我从excel / csv获取原始数据。只要键与提供的excel / csv中的数据匹配,此方法就可以正常工作。

如果excel / csv将“雇员姓名”作为“雇员姓名”或“雇员姓名”,则上述方法将不起作用。

有没有一种方法可以解决此问题,例如将“员工姓名”之类的键映射到与以下任何格式(“员工姓名”,“员工姓名”,“员工姓名”),“ rmg_spoc”相匹配的值”与任何格式(“ RMG”,“ rmg”,“ RMG SPOC”,“ rmg spoc”,“ rmg *”)匹配,“ total_experience”与任何格式(“总体验”,“总体验”, '* [E] [e] xperience *')。

3 个答案:

答案 0 :(得分:1)

这似乎是一个不区分大小写的字典搜索(This SO question and all its duplicates)反复出现的问题的例子

帖子中建议的解决方案是使用包装器来像这样命令dict(或collections.OrderedDict):

import collections

class CaseInsensitiveDict(collections.Mapping):
    def __init__(self, d):
        self._d = d
        self._s = dict((k.lower(), k) for k in d)
    def __contains__(self, k):
        return k.lower() in self._s
    def __len__(self):
        return len(self._s)
    def __iter__(self):
        return iter(self._s)
    def __getitem__(self, k):
        return self._d[self._s[k.lower()]]
    def actual_key_case(self, k):
        return self._s.get(k.lower())

在代码中,您只需使用此包装器包装字典即可,以便执行不区分大小写的键搜索:

data_items = [OrderedDict([('Employee Number', '1'), ('Employee Name', 'Ms. A'), ('RMG SPOC', 'X'), ('Total Experience (yrs)', '3.06'), ('Days Unallocated', '18'), ('Skill Details', 'Manual testing'), ('Contact Number', '1234')]), OrderedDict([('Employee Number', '2'), ('Employee Name', 'Mr. B'), ('RMG SPOC', 'Y'), ('Total Experience (yrs)', '2.51'), ('Days Unallocated', '28'), ('Skill Details', 'Manual Testing'), ('Contact Number', '2345')]), OrderedDict([('Employee Number', '3'), ('Employee Name', 'Mr. C'), ('RMG SPOC', 'Z'), ('Total Experience (yrs)', '1.86'), ('Days Unallocated', '9'), ('Skill Details', 'C++, Manual Testing, Oracle'), ('Contact Number', '4567')]), OrderedDict([('Employee Number', '4'), ('Employee Name', 'Mr. D'), ('RMG SPOC', 'xyz'), ('Total Experience (yrs)', '7.68'), ('Days Unallocated', '23'), ('Skill Details', 'Manual Testing, SQL, HCM'), ('Contact Number', '789')])]


data = CaseInsensitiveDict(data[0])

print(data['EmplOYee NAME'])
# should print 'Ms. A'
print(data['Employee NAME'])
# should print 'Ms. A'
print(data['EmploYee NAME'])
# should print 'Ms. A'
print(data['EmployeE NAME'])
# should print 'Ms. A'
print(data['Employee Name'])
# should print 'Ms. A'

答案 1 :(得分:0)

@ C.Nivs是正确的,下面的代码片段根据您的示例展示了其工作原理。

options = ('Employee Number','EMPLOYEE NUMBER','employee number')

for option in options:
  assert option.strip().lower() == "employee number"
  print("true")

答案 2 :(得分:0)

首先对输入中的键进行规范化怎么办? -也许会执行以下操作:

normalized_data = [{key.lower().replace(' ', '_'): val for key, val in datum.items()} for datum in data]

对于示例数据,您将获得:

[{'employee_number': '1', 'employee_name': 'Ms. A', 'rmg_spoc': 'X', 'total_experience_(yrs)': '3.06', 'days_unallocated': '18', 'skill_details': 'Manual testing', 'contact_number': '1234'},
 {'employee_number': '2', 'employee_name': 'Mr. B', 'rmg_spoc': 'Y', 'total_experience_(yrs)': '2.51', 'days_unallocated': '28', 'skill_details': 'Manual Testing', 'contact_number': '2345'},
 {'employee_number': '3', 'employee_name': 'Mr. C', 'rmg_spoc': 'Z', 'total_experience_(yrs)': '1.86', 'days_unallocated': '9', 'skill_details': 'C++, Manual Testing, Oracle', 'contact_number': '4567'},
 {'employee_number': '4', 'employee_name': 'Mr. D', 'rmg_spoc': 'xyz', 'total_experience_(yrs)': '7.68', 'days_unallocated': '23', 'skill_details': 'Manual Testing, SQL, HCM', 'contact_number': '789'}]