我改编了GenesRus和unutbu的答案，非常简单:

class new_dict(dict):
    def deep_get(self, *args, default=None):
        _empty_dict = {}
        out = self
        for key in args:
            out = out.get(key, _empty_dict)
        return out if out else default

它适用于:

d = new_dict(some_data)
d.deep_get("key1", "key2", "key3", ..., default=some_value)

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")

虽然reduce方法简洁而简短，但我认为简单的循环更容易理解。我还包含了一个默认参数。

def deep_get(_dict, keys, default=None):
    for key in keys:
        if isinstance(_dict, dict):
            _dict = _dict.get(key, default)
        else:
            return default
    return _dict

作为理解reduce一行程序如何工作的练习，我执行了以下操作。但最终循环方法对我来说似乎更直观。

def deep_get(_dict, keys, default=None):

    def _reducer(d, key):
        if isinstance(d, dict):
            return d.get(key, default)
        return default

    return reduce(_reducer, keys, _dict)

使用

nested = {'a': {'b': {'c': 42}}}

print deep_get(nested, ['a', 'b'])
print deep_get(nested, ['a', 'b', 'z', 'z'], default='missing')

减少方法的改进很少，使其与列表一起工作。也使用数据路径作为字符串除以点，而不是数组。

def deep_get(dictionary, path):
    keys = path.split('.')
    return reduce(lambda d, key: d[int(key)] if isinstance(d, list) else d.get(key) if d else None, keys, dictionary)

def safeget(_dct, *_keys):
    if not isinstance(_dct, dict): raise TypeError("Is not instance of dict")
    def foo(dct, *keys):
        if len(keys) == 0: return dct
        elif not isinstance(_dct, dict): return None
        else: return foo(dct.get(keys[0], None), *keys[1:])
    return foo(_dct, *_keys)

assert safeget(dict()) == dict()
assert safeget(dict(), "test") == None
assert safeget(dict([["a", 1],["b", 2]]),"a", "d") == None
assert safeget(dict([["a", 1],["b", 2]]),"a") == 1
assert safeget({"a":{"b":{"c": 2}},"d":1}, "a", "b")["c"] == 2

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。