feat: edtech + gaming domains, null_if conditional nulls, py.typed, coverage CI

Author: rasinmuhammed

.github/workflows/ci.yml

@@ -27,11 +27,20 @@ jobs:
       - name: Install dependencies
         run: |
           pip install -e ".[dev]"
+          pip install pytest-cov
 
-      - name: Run tests
+      - name: Run tests with coverage
         run: |
-          pytest tests/ -q --tb=short
+          pytest tests/ -q --tb=short --cov=misata --cov-report=xml --cov-report=term-missing
 
       - name: Run scale test
         run: |
           pytest tests/test_integration_scale.py -v -m slow --tb=short
+
+      - name: Upload coverage to Codecov
+        if: matrix.python-version == '3.11'
+        uses: codecov/codecov-action@v4
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          files: coverage.xml
+          fail_ci_if_error: false

misata/py.typed

@@ -1347,6 +1347,9 @@ def generate_batches(self, table_name: str) -> Any:
             # Apply formulas
             df_batch = self._apply_formula_columns(df_batch, table_name)
 
+            # Apply conditional nulls (null_if)
+            df_batch = self._apply_null_if(df_batch, table_name)
+
             # Post-process
             df_batch = self._fix_correlated_columns(df_batch, table_name)
 
@@ -1683,6 +1686,27 @@ def _apply_single_constraint(self, df: pd.DataFrame, constraint: Any) -> pd.Data
 
         return df
 
+    def _apply_null_if(self, df: pd.DataFrame, table_name: str) -> pd.DataFrame:
+        """Set column to NaN/NaT where a sibling column matches a trigger value.
+
+        Reads ``null_if`` from each column's distribution_params:
+            null_if: {"column": "status", "values": ["cancelled", "refunded"]}
+        or the shorthand single-value form:
+            null_if: {"column": "status", "value": "cancelled"}
+        """
+        columns = self.config.columns.get(table_name, [])
+        for col in columns:
+            spec = col.distribution_params.get("null_if")
+            if not spec:
+                continue
+            ref_col = spec.get("column")
+            trigger_values = spec.get("values") or ([spec["value"]] if "value" in spec else [])
+            if not ref_col or not trigger_values or ref_col not in df.columns or col.name not in df.columns:
+                continue
+            mask = df[ref_col].isin(trigger_values)
+            df.loc[mask, col.name] = np.nan
+        return df
+
     def _apply_formula_columns(self, df: pd.DataFrame, table_name: str) -> pd.DataFrame:
         """Apply formula-based derived columns using context for lookups."""
         try: