added tests

.github/workflows/test.yml-NOTUSED → .github/workflows/test.yml

@@ -23,7 +23,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install flit
-          flit install
+          flit install --deps production
       - name: Test with pytest
         run: |
           pytest .

pyproject.toml

@@ -19,12 +19,10 @@ dependencies = [
     "pandas==2.1.3",
     "rich==13.7.0",
     "typer[all]==0.9.0",
+    "pytest==7.4.3",
 ]
 
 [project.optional-dependencies]
-test = [
-    "pytest==7.4.3"
-]
 
 dev = [
     "ipykernel",

src/ak_sap/Database/tables.py

@@ -116,7 +116,7 @@ def apply(self):
         except Exception as e:
             log.critical(str(e) + f'Import Log: {ImportLog}')
 
-def _array_to_pandas(headers: tuple[str], array: tuple) -> pd.DataFrame:
+def _array_to_pandas(headers: tuple, array: tuple) -> pd.DataFrame:
     """Given the table headers as tuple and table data as a single tuple;
     Returns table as a dataframe."""
     num_fields = len(headers)

src/tests/Database/test_tables.py

@@ -0,0 +1,56 @@
+import pandas as pd
+import pytest
+import unittest
+
+from ak_sap.Database.tables import _array_to_pandas, flatten_dataframe
+
+# Test case with a valid input
+def test_array_to_pandas_valid():
+    headers = ('Name', 'Age', 'City')
+    data = ('John', 25, 'New York', 'Alice', 30, 'San Francisco')
+    expected_df = pd.DataFrame({
+        'Name': ['John', 'Alice'],
+        'Age': [25, 30],
+        'City': ['New York', 'San Francisco']
+    })
+    result_df = _array_to_pandas(headers, data)
+    pd.testing.assert_frame_equal(result_df, expected_df)
+
+# Test case with an invalid input (array length is not divisible by header length)
+def test_array_to_pandas_invalid_length():
+    headers = ('Name', 'Age', 'City')
+    data = ('John', 25, 'New York', 'Alice', 30)  # Missing City for Alice
+    with pytest.raises(AssertionError, match=r'Array length \(\d+\) is not divisible by header length \(\d+\)'):
+        _array_to_pandas(headers, data)
+
+class TestFlattenDataFrame(unittest.TestCase):
+
+    def test_flatten_empty_dataframe(self):
+        df = pd.DataFrame()
+        result = flatten_dataframe(df)
+        self.assertEqual(result, tuple(), "Flattening an empty dataframe should return an empty tuple")
+
+    def test_flatten_dataframe_with_values(self):
+        data = {'A': [1, 2, 3], 'B': ['a', 'b', 'c'], 'C': [4.5, 6.7, 8.9]}
+        df = pd.DataFrame(data)
+        result = flatten_dataframe(df)
+        expected = ('1', 'a', '4.5', '2', 'b', '6.7', '3', 'c', '8.9')
+        self.assertEqual(result, expected, "Flattening a dataframe with values should produce the correct tuple")
+
+    def test_flatten_dataframe_with_nan_values(self):
+        data = {'A': [1, 2, None], 'B': ['a', None, 'c'], 'C': [4.5, 6.7, 8.9]}
+        df = pd.DataFrame(data)
+        result = flatten_dataframe(df)
+        expected = ('1.0', 'a', '4.5', '2.0', None, '6.7', None, 'c', '8.9')
+        self.assertEqual(result, expected, "Flattening a dataframe with NaN values should produce the correct tuple")
+
+    def test_flatten_dataframe_with_empty_strings(self):
+        data = {'A': [1, 2, ''], 'B': ['a', '', 'c'], 'C': [4.5, 6.7, 8.9]}
+        df = pd.DataFrame(data)
+        result = flatten_dataframe(df)
+        expected = ('1', 'a', '4.5', '2', None, '6.7', None, 'c', '8.9')
+        self.assertEqual(result, expected, "Flattening a dataframe with empty strings should produce the correct tuple")
+
+
+if __name__ == '__main__':
+    unittest.main()