aweson 2.0.0

Traversing and manipulating hierarchical info sets (JSON) using pythonic JSON Path-like expressions

Traversing and manipulating hierarchical data (JSON) using pythonic JSON Path-like expressions

Importing

>>> from aweson import JP, find_all, find_next

Iterating over hierarchical data

>>> content = {"employees": [
...     {"name": "Doe, John", "age": 32, "account": "johndoe"},
...     {"name": "Doe, Jane", "age": -23, "account": "janedoe"},
...     {"name": "Deer, Jude", "age": 42, "account": "judedeer"},
... ]}
>>> list(find_all(content, JP.employees[:].name))
['Doe, John', 'Doe, Jane', 'Deer, Jude']

Note that the JSON Path-like expression JP.employees[:].name is not a string, it’s a Python expression, i.e. your IDE will be of actual help.

Furthermore, to address all items in a list, Pythonic slice expression [:] is used. Naturally, other indexing and slice expressions also work in the conventional Pythonic way:

>>> list(find_all(content, JP.employees[-1].name))
['Deer, Jude']

>>> list(find_all(content, JP.employees[:2].name))
['Doe, John', 'Doe, Jane']

Paths to items iterated

You may be interested in the actual path of an item being returned, just like you get an index alongside items when using enumerate(). For instance, you may want to verify ages being non-negative, and report accurately the path of failure items:

>>> path, item = next(tup for tup in find_all(content, JP.employees[:].age, with_path=True) if tup[1] < 0)
>>> item
-23

The offending path, then, in human-readable format:

>>> str(path)
'$.employees[1].age'

The enclosing record, using .parent attribute of the path obtained for the offending age:

>>> next(find_all(content, path.parent))
{'name': 'Doe, Jane', 'age': -23, 'account': 'janedoe'}

Note, with argument with_path=True passed, find_all() yields tuples instead of single items.

Selecting sub-items

You can select sub-items of iterated items, comes handy into turning one structure into another, like a list of records into a dict:

>>> {tup[0]: tup[1] for tup in find_all(content, JP.employees[:](JP.account, JP.name))}
{'johndoe': 'Doe, John', 'janedoe': 'Doe, Jane', 'judedeer': 'Deer, Jude'}

or, to make your processing logic within the same comprehension expression more readable:

>>> {account: name for account, name in find_all(content, JP.employees[:](JP.account, JP.name))}
{'johndoe': 'Doe, John', 'janedoe': 'Doe, Jane', 'judedeer': 'Deer, Jude'}

You can also make a sub-items selection produce named tuples by explicitly naming sub-paths:

>>> list(find_all(content, JP.employees[:](account=JP.account, name=JP.name)))
[SubSelect(account='johndoe', name='Doe, John'), SubSelect(account='janedoe', name='Doe, Jane'), SubSelect(account='judedeer', name='Deer, Jude')]

Now, the processing code could be elsewhere than the find_all() invocation, as named tuples will carry the field names with them. The produced named tuples will all be called SubSelect but they will be different named tuples for each invocation.

Variable field name selection

The forms JP["field_name"] and JP.field_name are equivalent:

>>> from functools import reduce
>>> def my_sum(content, field_name, initial):
...     return reduce(
...         lambda x, y: x + y,
...         find_all(content, JP.employees[:][field_name]),
...         initial
...     )
>>> my_sum(content, "age", 0)
51
>>> my_sum(content, "account", "")
'johndoejanedoejudedeer'

Field name by regular expressions

Sometimes you have a JSON format where types are represented by JSON objects, e.g.

>>> content = {
...     "apple": [{"name": "red delicious"}, {"name": "punakaneli"}],
...     "pear": [{"name": "wilhelm"}, {"name": "conference"}]
... }

i.e. it’s not a union list of records with a type discriminator [{"type": "apple", "name": ...}, ...]. Iterating over all the fruits, regardless of their type, in our example content above can be achieved by:

>>> list(find_all(content, JP["apple|pear"][:].name))
['red delicious', 'punakaneli', 'wilhelm', 'conference']

if you are only interested in apples and pears, or

>>> list(find_all(content, JP[".*"][:].name))
['red delicious', 'punakaneli', 'wilhelm', 'conference']

if you are interested in fruits other than apples and pears.

Note, that the expression JP["*"] is also supported, but that’s not a regular expression, merely a conventional JSON Path notation, and equivalent to JP[:]:

>>> list(find_all([5, 42, 137], JP["*"]))
[5, 42, 137]

Suppressing indexing errors and key errors

By default, path expressions are strict, e.g. for list indexes:

>>> list(find_all([0, 1], JP[2]))
Traceback (most recent call last):
  ...
IndexError: list index out of range

and for dict keys:

>>> list(find_all({"hello": 42}, JP.hi))
Traceback (most recent call last):
  ...
KeyError: 'hi'

You can suppress these errors and simply have nothing yielded, for list indexes:

>>> list(find_all([0, 1], JP[2], lenient=True))
[]

and for dict keys:

>>> list(find_all({"hello": 42}, JP.hi, lenient=True))
[]

Utility find_next()

Often, you just need a first value, roughly equivalent to a next(find_all(...)) invocation. You can use find_next() for this, for instance

>>> find_next([{"hello": 5}, {"hello": 42}], JP[:].hello)
5
>>> find_next([{"hello": 5}, {"hello": 42}], JP[1].hello)
42

You can also ask for the path of the value returned, in the style of with_path=True above

>>> path, value = find_next([{"hello": 5}, {"hello": 42}], JP[-1].hello, with_path=True)
>>> str(path)
'$[1].hello'
>>> value
42

You can also supply a default value for find_next(), just like for next():

>>> find_next([{"hello": 5}, {"hello": 42}], JP[3].hello, default=17)
17