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jmesflat

Built upon and considered an extension of jmespath, jmesflat is similarly pronounced (say "James flat") and provides a simple interface for flattening, 'unflattening', and merging deeply nested json objects.

Common use cases:

>>> # 1. Building deeply nested objects without constructing individual layers:
>>> import jmesflat as jf
>>> nest1 = jf.unflatten({"a.b[0].c[0].d": "e", "a.b[1].f": "g"})
>>> nest1
{'a': {'b': [{'c': [{'d': 'e'}]}, {'f': 'g'}]}}
>>>
>>> # 2. Merging deeply nested objects:
>>> nest2 = {"a": {"b": [{"f": "g"}, {"c": [{"d": "e"}]}]}}
>>> merged_nest = jf.merge(nest1, nest2)
>>> merged_nest
{'a': {'b': [{'c': [{'d': 'e'}], 'f': 'g'}, {'c': [{'d': 'e'}], 'f': 'g'}]}}
>>>
>>> # 3. Making dumps of complex nest objects compact and human readable
>>> import json
>>> print(json.dumps(jf.flatten(merged_nest), indent=2))
{
  "a.b[0].c[0].d": "e",
  "a.b[0].f": "g",
  "a.b[1].c[0].d": "e",
  "a.b[1].f": "g"
}

Installation

pip install jmesflat

Compatibility

Python 3.9 or greater

Basic Features Demo

  1. Keys can contain spaces and reserved characters (@ and -)
  2. Supports any arbitrary nesting pattern including mixed and multi-level array objects
  3. Empty lists / dicts are considered atomic types and included in the flattened output alongside the 'true' atomic types int, float, str, bool, and None
  4. Flatten / unflatten / merge at an arbitrary object depth using the level parameter (depth cannot exceed the depth of the first array instance)
  5. Extend rather than overwrite arrays during merge operations using the array_merge parameter. 'topdown' merges extend at the first array instance. 'bottomup' extends at the final array instance.
  6. Scrub the data during flatten/unflatten/merge operations or simply scrub a nested object via clean using the discard_check parameter. The check is ONLY applied to nest2 during the merge operation.
>>> import json
>>> import jmesflat as jf
>>>
>>> test_nest = {
...     "Outer Object Key 1": {
...         "mixedArray": [
...             "mixed array string",
...             {"mixed Array Object 1 Key": "spaces demo"},
...             12345,
...             [
...                 {"@subArray": "@ symbol demo"},
...                 {"@subArray": 1.2345},
...                 {"@subArray": None},
...             ],
...             {"mixed-array-object-2-key": "dashed key demo"},
...             [],
...             {},
...         ],
...     },
...     "Outer Object Key 2": {
...         "deepNest": {
...             "a": [
...                 {"b": 1},
...                 {
...                     "c": {
...                         "d": [
...                             {"e": "f", "g": "h"},
...                             {"e": "f1"}
...                         ]
...                     }
...                 }
...             ]
...         },
...     },
... }
>>>
>>> flat = jf.flatten(test_nest, level=1)
>>> print(json.dumps(flat, indent=2))
{
  "Outer Object Key 1": {
    "mixedArray[0]": "mixed array string",
    "mixedArray[1].mixed Array Object 1 Key": "spaces demo",
    "mixedArray[2]": 12345,
    "mixedArray[3][0].@subArray": "@ symbol demo",
    "mixedArray[3][1].@subArray": "@ symbol demo",
    "mixedArray[3][2].@subArray": "@ symbol demo",
    "mixedArray[4].mixed-array-object-2-key": "dashed key demo",
    "mixedArray[5]": [],
    "mixedArray[6]": {}
  },
  "Outer Object Key 2": {
    "deepNest.a[0].b": 1,
    "deepNest.a[1].c.d[0].e": "f",
    "deepNest.a[1].c.d[0].g": "h",
    "deepNest.a[1].c.d[1].e": "f1"
  }
}
>>>
>>> jf.unflatten(flat, level=1) == test_nest
True
>>>
>>> from copy import deepcopy
>>> test_nest2 = deepcopy(test_nest)
>>> # NOTE: `jf.flatten` wrapper is used for ease of visualization only in the merge/clean examples below
>>> print(json.dumps(jf.flatten(jf.merge(test_nest, test_nest2, level=1), level=2), indent=2))
{
  "Outer Object Key 1": {
    "mixedArray": {
      "[0]": "mixed array string",
      "[1].mixed Array Object 1 Key": "spaces demo",
      "[2]": 12345,
      "[3][0].@subArray": "@ symbol demo",
      "[3][1].@subArray": 1.2345,
      "[3][2].@subArray": null,
      "[4].mixed-array-object-2-key": "dashed key demo",
      "[5]": [],
      "[6]": {}
    }
  },
  "Outer Object Key 2": {
    "deepNest": {
      "a[0].b": 1,
      "a[1].c.d[0].e": "f",
      "a[1].c.d[0].g": "h",
      "a[1].c.d[1].e": "f1"
    }
  }
}
>>> print(json.dumps(jf.flatten(jf.merge(test_nest, test_nest2, level=1, array_merge="topdown"), level=2), indent=2))
{
  "Outer Object Key 1": {
    "mixedArray": {
      "[0]": "mixed array string",
      "[1].mixed Array Object 1 Key": "spaces demo",
      "[2]": 12345,
      "[3][0].@subArray": "@ symbol demo",
      "[3][1].@subArray": 1.2345,
      "[3][2].@subArray": null,
      "[4].mixed-array-object-2-key": "dashed key demo",
      "[5]": [],
      "[6]": {},
      "[7]": "mixed array string",
      "[8].mixed Array Object 1 Key": "spaces demo",
      "[9]": 12345,
      "[10][0].@subArray": "@ symbol demo",
      "[10][1].@subArray": 1.2345,
      "[10][2].@subArray": null,
      "[11].mixed-array-object-2-key": "dashed key demo",
      "[12]": [],
      "[13]": {}
    }
  },
  "Outer Object Key 2": {
    "deepNest": {
      "a[0].b": 1,
      "a[1].c.d[0].e": "f",
      "a[1].c.d[0].g": "h",
      "a[1].c.d[1].e": "f1",
      "a[2].b": 1,
      "a[3].c.d[0].e": "f",
      "a[3].c.d[0].g": "h",
      "a[3].c.d[1].e": "f1"
    }
  }
}
>>> print(json.dumps(jf.flatten(jf.merge(test_nest, test_nest2, level=1, array_merge="bottomup"), level=2), indent=2))
{
  "Outer Object Key 1": {
    "mixedArray": {
      "[0]": "mixed array string",
      "[1].mixed Array Object 1 Key": "spaces demo",
      "[2]": 12345,
      "[3][0].@subArray": "@ symbol demo",
      "[3][1].@subArray": 1.2345,
      "[3][2].@subArray": null,
      "[3][3].@subArray": "@ symbol demo",
      "[3][4].@subArray": 1.2345,
      "[3][5].@subArray": null,
      "[4].mixed-array-object-2-key": "dashed key demo",
      "[5]": [],
      "[6]": {},
      "[7]": "mixed array string",
      "[8].mixed Array Object 1 Key": "spaces demo",
      "[9]": 12345,
      "[10].mixed-array-object-2-key": "dashed key demo",
      "[11]": [],
      "[12]": {}
    }
  },
  "Outer Object Key 2": {
    "deepNest": {
      "a[0].b": 1,
      "a[1].c.d[0].e": "f",
      "a[1].c.d[0].g": "h",
      "a[1].c.d[1].e": "f1",
      "a[1].c.d[2].e": "f",
      "a[1].c.d[2].g": "h",
      "a[1].c.d[3].e": "f1",
      "a[2].b": 1
    }
  }
}
>>> print(json.dumps(jf.flatten(jf.clean(test_nest, discard_check=lambda key, val: "-" in key or not isinstance(val, (str, int))), level=2), indent=2))
{
  "Outer Object Key 1": {
    "mixedArray": {
      "[0]": "mixed array string",
      "[1].mixed Array Object 1 Key": "spaces demo",
      "[2]": 12345,
      "[3][0].@subArray": "@ symbol demo"
    }
  },
  "Outer Object Key 2": {
    "deepNest": {
      "a[0].b": 1,
      "a[1].c.d[0].e": "f",
      "a[1].c.d[0].g": "h",
      "a[1].c.d[1].e": "f1"
    }
  }
}

The constants Module

The constants module allows global defaults to be set for several key features. For example, a global discard check function can be defined by setting the value of jf.constants.DISCARD_CHECK. If the user wishes to discard all None type values, simply set jf.constants.DISCARD_CHECK = lambda _, val: val is None after the initial import jmesflat as jf statement. In addition, users can customize the default values that will be used when extending arrays during an index preserving unflatten operation via jf.MISSING_ARRAY_ENTRY_VALUE, a callable that accepts the flattened key of the array element being set and the value said is being set to and returns the value that should be used to pad the array until its length is >= the desired index. Other settings in the constants module are considered 'use at your own risk' and included for possible future extensibility.