你可以使用pydash:

import pydash as _  #NOTE require `pip install pydash`

_.get(example_dict, 'key1.key2', default='Default')

https://pydash.readthedocs.io/en/latest/api.html

我的实现下降到子字典，忽略None值，但失败与TypeError如果发现任何其他

def deep_get(d: dict, *keys, default=None):
    """ Safely get a nested value from a dict

    Example:
        config = {'device': None}
        deep_get(config, 'device', 'settings', 'light')
        # -> None
        
    Example:
        config = {'device': True}
        deep_get(config, 'device', 'settings', 'light')
        # -> TypeError

    Example:
        config = {'device': {'settings': {'light': 'bright'}}}
        deep_get(config, 'device', 'settings', 'light')
        # -> 'light'

    Note that it returns `default` is a key is missing or when it's None.
    It will raise a TypeError if a value is anything else but a dict or None.
    
    Args:
        d: The dict to descend into
        keys: A sequence of keys to follow
        default: Custom default value
    """
    # Descend while we can
    try:
        for k in keys:
            d = d[k]
    # If at any step a key is missing, return default
    except KeyError:
        return default
    # If at any step the value is not a dict...
    except TypeError:
        # ... if it's a None, return default. Assume it would be a dict.
        if d is None:
            return default
        # ... if it's something else, raise
        else:
            raise
    # If the value was found, return it
    else:
        return d

如果您想使用另一个库来解决问题，这是最好的方法

https://github.com/maztohir/dict-path

from dict-path import DictPath

data_dict = {
  "foo1": "bar1",
  "foo2": "bar2",
  "foo3": {
     "foo4": "bar4",
     "foo5": {
        "foo6": "bar6",
        "foo7": "bar7",
     },
  }
}

data_dict_path = DictPath(data_dict)
data_dict_path.get('key1/key2/key3')

你可以使用开源ndicts包中的NestedDict(我是作者)，它有一个完全像字典一样的安全get方法。

>>> from ndicts import NestedDict
>>> nd = NestedDict({"key1": {"key2": 0}}
>>> nd.get(("key1", "key2))
0
>>> nd.get("asd")

你可以使用get两次:

example_dict.get('key1', {}).get('key2')

如果key1或key2不存在，则返回None。

注意，如果example_dict['key1']存在但不是dict(或具有get方法的类dict对象)，仍然可能引发AttributeError。如果example_dict['key1']不可下标，你发布的try..except代码将引发TypeError。

另一个区别是try…除非在第一次丢失钥匙后立即发生短路。get调用链则不然。

如果您希望保留语法example_dict['key1']['key2']，但不希望它引发KeyErrors，那么您可以使用哈希recipe:

class Hasher(dict):
    # https://stackoverflow.com/a/3405143/190597
    def __missing__(self, key):
        value = self[key] = type(self)()
        return value

example_dict = Hasher()
print(example_dict['key1'])
# {}
print(example_dict['key1']['key2'])
# {}
print(type(example_dict['key1']['key2']))
# <class '__main__.Hasher'>

注意，当缺少一个键时，返回一个空的hash。

因为Hasher是dict的一个子类，你可以像使用dict一样使用Hasher。所有相同的方法和语法都是可用的，哈希器只是以不同的方式对待缺失的键。

你可以像这样把一个普通字典转换成哈希:

hasher = Hasher(example_dict)

并将哈希转换为普通字典一样容易:

regular_dict = dict(hasher)

另一种选择是在helper函数中隐藏丑陋的代码:

def safeget(dct, *keys):
    for key in keys:
        try:
            dct = dct[key]
        except KeyError:
            return None
    return dct

这样你剩下的代码就可以保持相对的可读性:

safeget(example_dict, 'key1', 'key2')

Glom是一个很好的库，可以进入点查询:

In [1]: from glom import glom

In [2]: data = {'a': {'b': {'c': 'd'}}}

In [3]: glom(data, "a.b.c")
Out[3]: 'd'

查询失败有一个很好的堆栈跟踪，指出确切的故障点:

In [4]: glom(data, "a.b.foo")
---------------------------------------------------------------------------
PathAccessError                           Traceback (most recent call last)
<ipython-input-4-2a3467493ac4> in <module>
----> 1 glom(data, "a.b.foo")

~/.cache/pypoetry/virtualenvs/neural-knapsack-dE7ihQtM-py3.8/lib/python3.8/site-packages/glom/core.py in glom(target, spec, **kwargs)
   2179 
   2180     if err:
-> 2181         raise err
   2182     return ret
   2183 

PathAccessError: error raised while processing, details below.
 Target-spec trace (most recent last):
 - Target: {'a': {'b': {'c': 'd'}}}
 - Spec: 'a.b.foo'
glom.core.PathAccessError: could not access 'foo', part 2 of Path('a', 'b', 'foo'), got error: KeyError('foo')

默认保护:

In [5]: glom(data, "a.b.foo", default="spam")
Out[5]: 'spam'

格洛姆的魅力在于多功能的规格参数。例如，可以很容易地从以下数据中提取所有的名字:

In [8]: data = {
   ...:     "people": [
   ...:         {"first_name": "Alice", "last_name": "Adams"},
   ...:         {"first_name": "Bob", "last_name": "Barker"}
   ...:     ]
   ...: }

In [9]: glom(data, ("people", ["first_name"]))
Out[9]: ['Alice', 'Bob']

更多的例子请阅读glom文档。