[pre-commit.ci] auto fixes from pre-commit.com hooks

For more information, see https://pre-commit.ci

src/pudl/transform/phmsagas.py

@@ -183,32 +183,35 @@ def core_phmsagas__yearly_distribution_operators(
     df = df.drop_duplicates()
 
     # Identify non-unique groups based on our PKs
-    non_unique_groups = df[df.groupby(["operator_id_phmsa", "report_number"])["report_number"].transform('size') > 1]
+    non_unique_groups = df[
+        df.groupby(["operator_id_phmsa", "report_number"])["report_number"].transform(
+            "size"
+        )
+        > 1
+    ]
 
     # Apply some custom filtering logic to non-unique groups
-    filtered_non_unique_rows = (
-        non_unique_groups
-        .groupby(["operator_id_phmsa", "report_number"], group_keys=False)
-        .apply(combined_filter)
-    )
+    filtered_non_unique_rows = non_unique_groups.groupby(
+        ["operator_id_phmsa", "report_number"], group_keys=False
+    ).apply(combined_filter)
 
     # Combine filtered non-unique rows with untouched unique rows
     unique_rows = df.drop(non_unique_groups.index)
     df = pd.concat([unique_rows, filtered_non_unique_rows], ignore_index=True)
 
     return df
 
+
 def filter_if_test_in_address(group: pd.DataFrame) -> pd.DataFrame:
-    """
-    Filters out rows with "test" in address columns. The logic is as follows:
-    
-    1. For any group of rows with the same combination of "operator_id_phmsa" 
+    """Filters out rows with "test" in address columns. The logic is as follows:
+
+    1. For any group of rows with the same combination of "operator_id_phmsa"
        and "report_number":
-        - If at least one row in the group does not contain the string "test" 
-          (case-insensitive) in either "office_address_street" or 
-          "headquarters_address_street", keep only the rows in the group 
+        - If at least one row in the group does not contain the string "test"
+          (case-insensitive) in either "office_address_street" or
+          "headquarters_address_street", keep only the rows in the group
           that do not contain "test" in these columns.
-        - If all rows in the group contain "test" in either of the columns, 
+        - If all rows in the group contain "test" in either of the columns,
           leave the group unchanged.
 
     Args:
@@ -219,22 +222,21 @@ def filter_if_test_in_address(group: pd.DataFrame) -> pd.DataFrame:
     """
     # Check if at least one row in the group does NOT contain "test" in both of the specified columns
     contains_test = group.apply(
-        lambda row: "test" in str(row["office_address_street"]).lower() or 
-                    "test" in str(row["headquarters_address_street"]).lower(),
-        axis=1
+        lambda row: "test" in str(row["office_address_street"]).lower()
+        or "test" in str(row["headquarters_address_street"]).lower(),
+        axis=1,
     )
     has_non_test = not contains_test.all()
 
     if has_non_test:
         # Keep rows where "test" does NOT appear in either column
         return group[~contains_test]
-    else:
-        # If all rows have "test", keep the group as is
-        return group
+    # If all rows have "test", keep the group as is
+    return group
+
 
 def filter_by_city_in_name(group: pd.DataFrame) -> pd.DataFrame:
-    """
-    Deduplication filter to only keep rows where "office_address_city" value
+    """Deduplication filter to only keep rows where "office_address_city" value
     is contained in the "operator_name_phmsa" value (case insensitive).
 
     Args:
@@ -244,20 +246,25 @@ def filter_by_city_in_name(group: pd.DataFrame) -> pd.DataFrame:
         pd.DataFrame: The filtered group of rows.
     """
     # Check if any row has "office_address_city" contained in "operator_name_phmsa" (case insensitive)
-    city_in_name = group["office_address_city"].str.lower().apply(
-        lambda city: any(city in name.lower() for name in group["operator_name_phmsa"])
+    city_in_name = (
+        group["office_address_city"]
+        .str.lower()
+        .apply(
+            lambda city: any(
+                city in name.lower() for name in group["operator_name_phmsa"]
+            )
+        )
     )
-    
+
     if city_in_name.any():
         # If any city is contained in the operator name, keep only those rows
         return group[city_in_name]
-    else:
-        # If no city is contained in the operator name, return the group as-is
-        return group
-    
+    # If no city is contained in the operator name, return the group as-is
+    return group
+
+
 def combined_filter(group: pd.DataFrame) -> pd.DataFrame:
-    """
-    Apply all required filters to DataFrame.
+    """Apply all required filters to DataFrame.
 
     Args:
         group (pd.DataFrame): A grouped subset of the DataFrame.
@@ -270,6 +277,7 @@ def combined_filter(group: pd.DataFrame) -> pd.DataFrame:
     group = filter_if_test_in_address(group)
     return group
 
+
 @dataclass
 class PhmsagasCheckSpec:
     """Define some simple checks that can run on FERC 714 assets."""
@@ -318,17 +326,14 @@ def _check_percent_unaccounted_for_gas(df):
     def _check_pk_deduplication(df):
         """Check if the size of filtered non-unique rows exceeds the threshold."""
         # Identify non-unique groups
-        non_unique_groups = (
-            df.groupby(["operator_id_phmsa", "report_number"])
-            .filter(lambda group: len(group) > 1)
+        non_unique_groups = df.groupby(["operator_id_phmsa", "report_number"]).filter(
+            lambda group: len(group) > 1
         )
 
         # Apply the filters to non-unique groups
-        filtered_non_unique_rows = (
-            non_unique_groups
-            .groupby(["operator_id_phmsa", "report_number"], group_keys=False)
-            .apply(combined_filter)
-        )
+        filtered_non_unique_rows = non_unique_groups.groupby(
+            ["operator_id_phmsa", "report_number"], group_keys=False
+        ).apply(combined_filter)
 
         if len(filtered_non_unique_rows) > spec.pk_deduplication_theshold:
             error = (
@@ -337,7 +342,7 @@ def _check_pk_deduplication(df):
             )
             logger.info(error)
             return AssetCheckResult(passed=False, metadata={"errors": error})
-        
+
         return AssetCheckResult(passed=True)
-    
+
     return [_check_percent_unaccounted_for_gas, _check_pk_deduplication]