reducing cleaning code

etl/epc/Record.py

@@ -102,11 +102,26 @@ CLEANING_RULES: dict[str, CleaningRule] = {
         "anomaly_to": None,
     },
 
+    "number-open-fireplaces": {
+        "cast": int,
+        "default": 0
+    },
+
     "extension-count": {
         "cast": int,
         "default": 0
     },
 
+    "flat-storey-count": {
+        "cast": int,
+        "anomaly_to": None
+    },
+
+    "number-habitable-rooms": {
+        "cast": int,
+        "anomaly_to": None
+    },
+
     # -----------------------------
     # TO NONE
     # -----------------------------
@@ -410,7 +425,10 @@ class EPCRecord:
 
             if "cast" in rule and value is not None:
                 try:
-                    self._prepared_epc[field] = rule["cast"](value)
+                    if rule["cast"] is int:
+                        self._prepared_epc[field] = int(float(value))
+                    else:
+                        self._prepared_epc[field] = rule["cast"](value)
                 except Exception as e:
                     logger.warning(
                         f"Failed casting field '{field}' value '{value}': {e}"
@@ -581,7 +599,6 @@ class EPCRecord:
 
         self._clean_built_form()
         self._clean_solar_hot_water()
-        self._clean_count_variables()
         self._clean_heat_loss_corridor()
         self._clean_age_band()
         self._clean_year_built()
@@ -867,34 +884,6 @@ class EPCRecord:
             self._prepared_epc["heat-loss-corridor"]
         ]
 
-    def _clean_count_variables(self) -> None:
-        """
-        This method will clean the count variables, if empty or invalid
-        """
-        if not self._prepared_epc:
-            raise ValueError("EPC Recrod doesn not contain epc data")
-
-        _fields = [
-            "number-open-fireplaces",
-            "extension-count",
-            "flat-storey-count",
-            "number-habitable-rooms",
-        ]
-
-        null_attributes = ["flat-storey-count", "number-habitable-rooms"]
-
-        for attribute in _fields:
-            value = self._prepared_epc[attribute]
-            if value in DATA_ANOMALY_MATCHES or pd.isnull(value):
-                if attribute in null_attributes:
-                    value = None
-                else:
-                    value = 0
-            else:
-                value = int(float(value))
-
-            self._prepared_epc[attribute] = value
-
     def _clean_solar_hot_water(self) -> None:
         """
         This method will clean the solar hot water, if empty or invalid