feat: Export products as dataframe

nisystemlink/clients/product/utilities/__init__.py

@@ -1,3 +1,4 @@
+from ._dataframe_utilities import convert_products_to_dataframe
 from ._file_utilities import get_products_linked_to_file
 
 # flake8: noqa

nisystemlink/clients/product/utilities/_dataframe_utilities.py

@@ -0,0 +1,29 @@
+from typing import List
+
+import pandas as pd
+from nisystemlink.clients.product.models import Product
+from pandas import DataFrame
+
+
+def convert_products_to_dataframe(products: List[Product]) -> DataFrame:
+    """Converts a list of products into a normalized dataframe.
+
+    Args:
+        products (List[Product]): A list of product responses retrieved from the API.
+
+    Returns:
+        DataFrame:
+            - A Pandas DataFrame containing the normalized product data. The DataFrame would consist
+            of all the fields received from the product response.
+
+            - Further, the properties field alone would get normalized. In the sense, there
+            would be separate columns for each of the properties in the list of all products. The
+            normalized column headers would be in the format `properties.property_name`.
+    """
+    products_dict_representation = [product.dict() for product in products]
+    normalized_products_dataframe = pd.json_normalize(
+        products_dict_representation, sep="."
+    )
+    normalized_products_dataframe.dropna(axis="columns", how="all", inplace=True)
+
+    return normalized_products_dataframe

pyproject.toml

@@ -38,6 +38,7 @@ requests = "^2.28.1"
 uplink   = "^0.9.7"
 pydantic = "^1.10.2"
 pyyaml = "^6.0.1"
+pandas = "^2.1.0"
 
 [tool.poetry.group.dev.dependencies]
 black               = ">=22.10,<25.0"

tests/unit/__init__.py

@@ -0,0 +1 @@
+# flake8: noqa

tests/unit/product/__init__.py

@@ -0,0 +1 @@
+# flake8: noqa

tests/unit/product/test_product_dataframe_utilities.py

@@ -0,0 +1,104 @@
+from datetime import datetime
+from typing import List
+
+import pandas as pd
+import pytest
+from nisystemlink.clients.product.models import Product
+from nisystemlink.clients.product.utilities import convert_products_to_dataframe
+from pandas import DataFrame
+
+
+@pytest.fixture
+def mock_products_data() -> List[Product]:
+    """Fixture to return a mock product data."""
+    product1 = Product(
+        id="5ffb2bf6771fa11e877838dd1",
+        part_number="p1",
+        name="product_1",
+        family="product_family",
+        updated_at=datetime(2024, 2, 2, 14, 22, 4, 625155),
+        file_ids=["file11", "file12"],
+        keywords=["keyword11", "keyword12"],
+        properties={"property11": "property11_value", "property12": "property12_value"},
+        workspace="5ffb2bf6771fa11e877838dd0",
+    )
+    product2 = Product(
+        id="5ffb2bf6771fa11e877838dd2",
+        part_number="p2",
+        name="product_2",
+        family="product_family",
+        updated_at=datetime(2024, 2, 2, 14, 22, 4, 625455),
+        file_ids=["file21", "file22"],
+        keywords=["keyword21", "keyword22"],
+        properties={"property21": "property21_value"},
+        workspace="5ffb2bf6771fa11e877838dd0",
+    )
+
+    return [product1, product2]
+
+
+@pytest.fixture
+def expected_products_dataframe(mock_products_data: List[Product]) -> DataFrame:
+    """Fixture to return the expected DataFrame based on the mock product data."""
+    return pd.json_normalize(
+        [mock_product.dict() for mock_product in mock_products_data]
+    )
+
+
+@pytest.fixture
+def empty_products_data() -> List:
+    """Fixture to return an empty list of products."""
+    return []
+
+
+@pytest.mark.enterprise
+@pytest.mark.unit
+class TestProductDataframeUtilities:
+    def test__convert_products_to_dataframe__with_complete_data(
+        self, mock_products_data: List[Product], expected_products_dataframe: DataFrame
+    ):
+        """Test normal case with valid product data."""
+        products_dataframe = convert_products_to_dataframe(mock_products_data)
+
+        assert not products_dataframe.empty
+        assert (
+            products_dataframe.columns.to_list()
+            == expected_products_dataframe.columns.to_list()
+        )
+        pd.testing.assert_frame_equal(
+            products_dataframe, expected_products_dataframe, check_dtype=True
+        )
+
+    def test__convert_products_to_dataframe__with_empty_data(
+        self, empty_products_data: List
+    ):
+        """Test case when the input products data is empty."""
+        products_dataframe = convert_products_to_dataframe(empty_products_data)
+
+        assert products_dataframe.empty
+
+    def test__convert_products_to_dataframe__with_missing_fields(
+        self, mock_products_data: List[Product], expected_products_dataframe: DataFrame
+    ):
+        """Test case when some fields in product data are missing."""
+        products = mock_products_data
+        for product in products:
+            product.keywords = None
+            product.properties = None
+
+        products_dataframe = convert_products_to_dataframe(products)
+        expected_products_dataframe = expected_products_dataframe.drop(
+            columns=expected_products_dataframe.filter(
+                like="properties"
+            ).columns.to_list()
+            + ["keywords"]
+        )
+
+        assert not products_dataframe.empty
+        assert (
+            products_dataframe.columns.to_list()
+            == expected_products_dataframe.columns.to_list()
+        )
+        pd.testing.assert_frame_equal(
+            products_dataframe, expected_products_dataframe, check_dtype=True
+        )