Merge pull request #50 from UBC-MDS/checknullvalue

fix(test): updated the test on what if no NA in the dataset

tests/test_missing.py

@@ -8,12 +8,36 @@ def df():
     Fixture to create a sample DataFrame for testing.
     """
     data = {
-        'A': [1, 2, None, 4],
-        'B': [None, None, 3, 4],
+        'A': [1, 2, pd.NA, 4],
+        'B': [pd.NA, pd.NA, 3, 4],
         'C': [1, 2, 3, 4]
     }
     return pd.DataFrame(data)
 
+@pytest.fixture
+def df_no_missing():
+    """
+    Fixture to create a DataFrame with no missing values.
+    """
+    data = {
+        'A': [1, 2, 3, 4],
+        'B': [5, 6, 7, 8],
+        'C': [9, 10, 11, 12]
+    }
+    return pd.DataFrame(data)
+
+@pytest.fixture
+def df_with_none():
+    """
+    Fixture to create a DataFrame that contains None values.
+    """
+    data = {
+        'A': [1, None, pd.NA, 4],  # None and pd.NA mixed in column A
+        'B': [None, None, 3, 4],   # Only None values in column B
+        'C': [1, 2, 3, 4]          # No missing values in column C
+    }
+    return pd.DataFrame(data)
+
 
 def test_missing_values_summary(df):
     """
@@ -33,3 +57,35 @@ def test_missing_values_summary(df):
 
     # Check the specific values in the Series
     assert list(result.values) == expected_values, "Values in the Series do not match the expected output."
+
+
+def test_no_missing_values(df_no_missing):
+    """
+    Test case where no missing values exist in the DataFrame.
+    """
+    result = missing_values_summary(df_no_missing)
+
+    # Check if the function returns an empty Series or the expected message
+    assert result.empty or result == "No missing value!", (
+        "Function should return 'No missing value!' or an empty Series when no missing values are present."
+    )
+
+
+def test_missing_values_summary_with_none(df_with_none):
+    """
+    Test case where the DataFrame contains None values.
+    """
+    result = missing_values_summary(df_with_none)
+
+    # Expected results
+    expected_index = {'B', 'A'}  # Use a set instead of a list (order-independent)
+    expected_values = ['2 (50.0%)', '2 (50.0%)']  # None values counted as missing
+
+    # Check if the result is a Series
+    assert isinstance(result, pd.Series), "Result should be a Series."
+
+    # Check if the resulting Series has the correct index (ignoring order)
+    assert set(result.index) == expected_index, "Index does not match the expected order."
+
+    # Check the specific values in the Series
+    assert list(result.values) == expected_values, "Values in the Series do not match the expected output."