glom
is a nice library that can into dotted queries too:
In [1]: from glom import glom
In [2]: data = {'a': {'b': {'c': 'd'}}}
In [3]: glom(data, "a.b.c")
Out[3]: 'd'
A query failure has a nice stack trace, indicating the exact failure spot:
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')
Safeguard with default
:
In [5]: glom(data, "a.b.foo", default="spam")
Out[5]: 'spam'
The beauty of glom
is in the versatile spec parameter. For example, one can easily extract all first names from the following data
:
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']
Read the glom
docs for more examples.