adding squasher utility

panaetius/utilities/__init__.py

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+"""General utilities."""
+
+from panaetius.utilities import squasher

panaetius/utilities/squasher.py

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+"""Squash a json object or Python dictionary into a single level dictionary."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+import itertools
+from typing import Iterator, Tuple
+
+
+class Squash:
+    """Squash a json object or Python dictionary into a single level dictionary."""
+
+    def __init__(self, data: dict) -> None:
+        """
+        Create a Squash object to squash data into a single level dictionary.
+
+        Args:
+            data (dict): [description]
+
+        Example:
+            squashed_data = Squash(my_data)
+
+            squashed_data.as_dict
+        """
+        self.data = data
+
+    @property
+    def as_dict(self) -> dict:
+        """
+        Return the squashed data as a dictionary.
+
+        Returns:
+            dict: The original data squashed as a dict.
+        """
+        return self._squash()
+
+    @staticmethod
+    def _unpack_dict(
+        key: str, value: dict | list | str
+    ) -> Iterator[Tuple[str, dict | list | str]]:
+        if isinstance(value, dict):
+            for sub_key, sub_value in value.items():
+                temporary_key = f"{key}_{sub_key}"
+                yield temporary_key, sub_value
+        elif isinstance(value, list):
+            for index, sub_value in enumerate(value):
+                temporary_key = f"{key}_{index}"
+                yield temporary_key, sub_value
+        else:
+            yield key, value
+
+    def _squash(self) -> dict:
+        result = deepcopy(self.data)
+        while True:
+            result = dict(
+                itertools.chain.from_iterable(
+                    itertools.starmap(self._unpack_dict, result.items())
+                )
+            )
+            if not any(
+                isinstance(value, dict) for value in result.values()
+            ) and not any(isinstance(value, list) for value in result.values()):
+                break
+        return result

tests/test_utilities/test_squasher.py

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+import pytest
+
+from panaetius import utilities
+
+
+def test_squashed_data(squashed_data, squashed_data_result):
+    # act
+    squashed_data_pre_squashed = utilities.squasher.Squash(squashed_data).as_dict
+
+    # assert
+    assert squashed_data_pre_squashed == squashed_data_result
+
+
+@pytest.fixture
+def squashed_data():
+    return {
+        "destination_addresses": [
+            "Washington, DC, USA",
+            "Philadelphia, PA, USA",
+            "Santa Barbara, CA, USA",
+            "Miami, FL, USA",
+            "Austin, TX, USA",
+            "Napa County, CA, USA",
+        ],
+        "origin_addresses": ["New York, NY, USA"],
+        "rows": [
+            {
+                "elements": [
+                    {
+                        "distance": {"text": "227 mi", "value": 365468},
+                        "duration": {
+                            "text": "3 hours 54 mins",
+                            "value": 14064,
+                        },
+                        "status": "OK",
+                    },
+                    {
+                        "distance": {"text": "94.6 mi", "value": 152193},
+                        "duration": {"text": "1 hour 44 mins", "value": 6227},
+                        "status": "OK",
+                    },
+                    {
+                        "distance": {"text": "2,878 mi", "value": 4632197},
+                        "duration": {
+                            "text": "1 day 18 hours",
+                            "value": 151772,
+                        },
+                        "status": "OK",
+                    },
+                    {
+                        "distance": {"text": "1,286 mi", "value": 2069031},
+                        "duration": {
+                            "text": "18 hours 43 mins",
+                            "value": 67405,
+                        },
+                        "status": "OK",
+                    },
+                    {
+                        "distance": {"text": "1,742 mi", "value": 2802972},
+                        "duration": {"text": "1 day 2 hours", "value": 93070},
+                        "status": "OK",
+                    },
+                    {
+                        "distance": {"text": "2,871 mi", "value": 4620514},
+                        "duration": {
+                            "text": "1 day 18 hours",
+                            "value": 152913,
+                        },
+                        "status": "OK",
+                    },
+                ]
+            }
+        ],
+        "status": "OK",
+    }
+
+
+@pytest.fixture
+def squashed_data_result():
+    return {
+        "destination_addresses_0": "Washington, DC, USA",
+        "destination_addresses_1": "Philadelphia, PA, USA",
+        "destination_addresses_2": "Santa Barbara, CA, USA",
+        "destination_addresses_3": "Miami, FL, USA",
+        "destination_addresses_4": "Austin, TX, USA",
+        "destination_addresses_5": "Napa County, CA, USA",
+        "origin_addresses_0": "New York, NY, USA",
+        "rows_0_elements_0_distance_text": "227 mi",
+        "rows_0_elements_0_distance_value": 365468,
+        "rows_0_elements_0_duration_text": "3 hours 54 mins",
+        "rows_0_elements_0_duration_value": 14064,
+        "rows_0_elements_0_status": "OK",
+        "rows_0_elements_1_distance_text": "94.6 mi",
+        "rows_0_elements_1_distance_value": 152193,
+        "rows_0_elements_1_duration_text": "1 hour 44 mins",
+        "rows_0_elements_1_duration_value": 6227,
+        "rows_0_elements_1_status": "OK",
+        "rows_0_elements_2_distance_text": "2,878 mi",
+        "rows_0_elements_2_distance_value": 4632197,
+        "rows_0_elements_2_duration_text": "1 day 18 hours",
+        "rows_0_elements_2_duration_value": 151772,
+        "rows_0_elements_2_status": "OK",
+        "rows_0_elements_3_distance_text": "1,286 mi",
+        "rows_0_elements_3_distance_value": 2069031,
+        "rows_0_elements_3_duration_text": "18 hours 43 mins",
+        "rows_0_elements_3_duration_value": 67405,
+        "rows_0_elements_3_status": "OK",
+        "rows_0_elements_4_distance_text": "1,742 mi",
+        "rows_0_elements_4_distance_value": 2802972,
+        "rows_0_elements_4_duration_text": "1 day 2 hours",
+        "rows_0_elements_4_duration_value": 93070,
+        "rows_0_elements_4_status": "OK",
+        "rows_0_elements_5_distance_text": "2,871 mi",
+        "rows_0_elements_5_distance_value": 4620514,
+        "rows_0_elements_5_duration_text": "1 day 18 hours",
+        "rows_0_elements_5_duration_value": 152913,
+        "rows_0_elements_5_status": "OK",
+        "status": "OK",
+    }