feat: export products as dataframe

Author: ShriramS-Emerson

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 +1,25 @@
+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.
+    """
+    products_dict_representation = [
+        product.dict(exclude_unset=True) 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

poetry.lock

@@ -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"

pyproject.toml

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

tests/unit/__init__.py

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

tests/unit/product/__init__.py

@@ -0,0 +1,102 @@
+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_product_data() -> List[Product]:
+    """Fixture to return a mock product data."""
+    product = Product(
+        id="product_id",
+        part_number="product_part_number",
+        name="product_name",
+        family="product_family",
+        updated_at=datetime(2024, 2, 2, 14, 22, 4, 625155),
+        file_ids=["file1", "file2"],
+        keywords=["keyword1", "keyword2"],
+        properties={"property1": "property1_value", "property2": "property2_value"},
+        workspace="product_workspace",
+    )
+
+    return [product]
+
+
+@pytest.fixture
+def expected_products_dataframe(mock_product_data) -> DataFrame:
+    """Fixture to return the expected DataFrame based on the mock product data."""
+    product = mock_product_data[0]
+    expected_dataframe_structure = {
+        "id": product.id,
+        "part_number": product.part_number,
+        "name": product.name,
+        "family": product.family,
+        "updated_at": product.updated_at,
+        "file_ids": product.file_ids,
+        "keywords": product.keywords,
+        "workspace": product.workspace,
+        "properties.property1": "property1_value",
+        "properties.property2": "property2_value",
+    }
+
+    return pd.json_normalize(expected_dataframe_structure)
+
+
+@pytest.fixture
+def empty_products_data() -> List:
+    """Fixture to return an empty list of products."""
+    return []
+
+
+@pytest.mark.enterprise
+class TestProductDataframeUtilities:
+    def test__convert_products_to_dataframe__with_complete_data(
+        self, mock_product_data, expected_products_dataframe
+    ):
+        """Test normal case with valid product data."""
+        products = mock_product_data
+
+        products_dataframe = convert_products_to_dataframe(products)
+
+        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):
+        """Test case when the input products data is empty."""
+        products = empty_products_data
+
+        products_dataframe = convert_products_to_dataframe(products)
+
+        assert products_dataframe.empty
+
+    def test__convert_products_to_dataframe__with_missing_fields(
+        self, mock_product_data, expected_products_dataframe
+    ):
+        """Test case when some fields in product data are missing."""
+        products = mock_product_data
+        del products[0].keywords
+        del products[0].properties
+
+        products_dataframe = convert_products_to_dataframe(products)
+        expected_products_dataframe = expected_products_dataframe.drop(
+            columns=["keywords", "properties.property1", "properties.property2"]
+        )
+
+        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
+        )