173 null dam error

Database/insert_events.py

@@ -8,7 +8,7 @@
 from pandarallel import pandarallel
 from tqdm import tqdm
 
-from Database.scr.normalize_utils import GeoJsonUtils, Logging
+from Database.scr.normalize_utils import GeoJsonUtils, Logging, NormalizeUtils
 
 pandarallel.initialize(progress_bar=False, nb_workers=5)
 
@@ -110,6 +110,7 @@
 
     args = parser.parse_args()
     logger = Logging.get_logger(f"database-insertion", level="INFO", filename="v1_full_run_insertion_raw.log")
+    utils = NormalizeUtils()
 
     connection = sqlite3.connect(args.database_name)
     cursor = connection.cursor()
@@ -190,6 +191,7 @@
 
         for f in tqdm(files, desc="Files"):
             data = pd.read_parquet(f"{args.file_dir}/{f}", engine="fastparquet")
+            data = utils.replace_nulls(data)
 
             logger.info("Converting everything to strings...")
             for c in data.columns:
@@ -261,5 +263,5 @@
         )
         logger.error(f"Insert errors were found! THIS ROW WAS NOT INSERTED! Storing in {tmp_errors_filename}")
         pathlib.Path(args.error_path).mkdir(parents=True, exist_ok=True)
-        errors.to_json(tmp_errors_filename, orient="records")
+        errors.to_json(tmp_errors_filename, orient="records", indent=3)
     connection.close()

Database/parse_events.py

@@ -99,7 +99,7 @@ def parse_main_events(df: pd.DataFrame, target_columns: list):
     for str_col in [x for x in events.columns if x in split_by_pipe_cols]:
         logger.info(f"Splitting column {str_col} by pipe")
         events[str_col] = events[str_col].progress_apply(
-            lambda x: (x.split("|") if isinstance(x, str) else (x if isinstance(x, str) else None))
+            lambda x: (x.split("|") if isinstance(x, str) else (x if isinstance(x, str) else []))
         )
 
     logger.info("STEP: Normalizing country-level administrative areas if present")
@@ -113,7 +113,7 @@ def parse_main_events(df: pd.DataFrame, target_columns: list):
         logger.info("Normalizing administrative areas...")
         events[f"{admin_area_col}_Tmp"] = events["Administrative_Areas"].progress_apply(
             lambda admin_areas: (
-                [norm_loc.normalize_locations(c, is_country=True) for c in admin_areas]
+                [norm_loc.normalize_locations(area=c, is_country=True) for c in admin_areas]
                 if isinstance(admin_areas, list)
                 else []
             )
@@ -220,9 +220,9 @@ def parse_main_events(df: pd.DataFrame, target_columns: list):
     logger.info("Cleaning event names...")
     event_name_col = [x for x in events.columns if "Event_Name" in x]
     if len(event_name_col) == 1:
-        event_name_col = event_name_col[0]
-        events["Event_Names"] = events[event_name_col].progress_apply(
-            lambda x: ([x.strip()] if isinstance(x, str) else ([y.strip() for y in x]) if isinstance(x, list) else None)
+        event_name_col_str: str = event_name_col[0]
+        events["Event_Names"] = events[event_name_col_str].progress_apply(
+            lambda x: ([x.strip()] if isinstance(x, str) else ([y.strip() for y in x]) if isinstance(x, list) else [])
         )
 
     hazards, main_event = "Hazards", "Main_Event"
@@ -251,16 +251,13 @@ def parse_main_events(df: pd.DataFrame, target_columns: list):
         logger.info(f"STEP: Validation relationship between col {hazards} and col {main_event}")
         events = events.progress_apply(lambda row: validation.validate_main_event_hazard_relation(row), axis=1)
 
-    logger.info("Converting annotation columns to strings to store in sqlite3")
-    annotation_cols = [col for col in events.columns if col.endswith(("_with_annotation", "_Annotation"))]
-
     logger.info(f"Storing parsed results for l1 events. Target columns: {target_columns}")
     utils.df_to_parquet(
         events[[x for x in target_columns if x in events.columns]],
         f"{args.output_dir}/l1",
         200,
     )
-    del total_summary_cols, annotation_cols, total_cols
+    del total_summary_cols, total_cols
     return events
 
 
@@ -357,17 +354,14 @@ def parse_sub_level_event(df, level: str, target_columns: list = []):
                 )
             )
         logger.info(f"Normalizing dates for subevet {col}")
-        start_date_col, end_date_col = [c for c in sub_event.columns if col.startswith("Start_Date")], [
-            c for c in sub_event.columns if col.startswith("End_Date")
-        ]
-        assert len(start_date_col) == len(end_date_col), "Check the start and end date columns"
-        assert len(start_date_col) <= 1, "Check the start and end date columns, there might be too many"
-
-        if start_date_col and end_date_col:
-            logger.info(f"Normalizing start and end date in columns {start_date_col} and {end_date_col}")
-            start_date_col, end_date_col = start_date_col[0], end_date_col[0]
+        start_date_col, end_date_col = (
+            "Start_Date" if "Start_Date" in sub_event.columns else None,
+            "End_Date" if "End_Date" in sub_event.columns else None,
+        )
+        concat_list = [sub_event]
+        if start_date_col:
+            logger.info(f"Normalizing start date column {start_date_col}")
             start_dates = sub_event[start_date_col].progress_apply(utils.normalize_date)
-            end_dates = sub_event[end_date_col].progress_apply(utils.normalize_date)
             start_date_cols = pd.DataFrame(
                 start_dates.to_list(),
                 columns=[
@@ -376,6 +370,11 @@ def parse_sub_level_event(df, level: str, target_columns: list = []):
                     f"{start_date_col}_Year",
                 ],
             )
+            concat_list.append(start_date_cols)
+
+        if start_date_col:
+            logger.info(f"Normalizing end date column {end_date_col}")
+            end_dates = sub_event[end_date_col].progress_apply(utils.normalize_date)
             end_date_cols = pd.DataFrame(
                 end_dates.to_list(),
                 columns=[
@@ -384,10 +383,16 @@ def parse_sub_level_event(df, level: str, target_columns: list = []):
                     f"{end_date_col}_Year",
                 ],
             )
-            sub_event.reset_index(inplace=True, drop=True)
-            sub_event = pd.concat([sub_event, start_date_cols, end_date_cols], axis=1)
+            concat_list.append(end_date_cols)
+        sub_event.reset_index(inplace=True, drop=True)
+        sub_event = pd.concat(concat_list, axis=1)
+        del concat_list
 
         if level == "l2" and administrative_area_col in sub_event.columns:
+            logger.info(f"Normalizing nulls in {administrative_area_col} for {level} {col}")
+            sub_event[administrative_area_col] = sub_event[administrative_area_col].progress_apply(
+                lambda admin_areas: utils.filter_null_list(admin_areas) if isinstance(admin_areas, list) else []
+            )
             logger.info(f"Normalizing administrative area names for {level} {col}")
             sub_event[f"{administrative_area_col}_Tmp"] = sub_event[administrative_area_col].progress_apply(
                 lambda admin_areas: (
@@ -437,6 +442,10 @@ def parse_sub_level_event(df, level: str, target_columns: list = []):
             )
 
         elif level == "l3" and administrative_area_col in sub_event.columns:
+            logger.info(f"Normalizing nulls in {administrative_area_col} for {level} {col}")
+            sub_event[administrative_area_col] = sub_event[administrative_area_col].apply(
+                lambda admin_area: utils.filter_null_str(admin_area)
+            )
             sub_event[
                 [
                     f"{administrative_area_col}_Norm",
@@ -459,7 +468,12 @@ def parse_sub_level_event(df, level: str, target_columns: list = []):
             sub_event[f"{administrative_area_col}_GID"] = sub_event[f"{administrative_area_col}_Norm"].progress_apply(
                 lambda area: norm_loc.get_gadm_gid(country=area) if area else []
             )
+
             if location_col in sub_event.columns:
+                logger.info(f"Normalizing nulls in {location_col} for {level} {col}")
+                sub_event[location_col] = sub_event[location_col].progress_apply(
+                    lambda locations: utils.filter_null_list(locations) if isinstance(locations, list) else []
+                )
                 logger.info(f"Normalizing location names for {level} {col}")
                 sub_event[f"{location_col}_Tmp"] = sub_event.progress_apply(
                     lambda row: (
@@ -528,14 +542,13 @@ def parse_sub_level_event(df, level: str, target_columns: list = []):
         logger.info(f"Dropped {rows_before-rows_after} row(s) in {col}")
         del rows_before, rows_after
         logger.info(f"Storing parsed results for subevent {col}")
-        for c in sub_event.columns:
-            sub_event[c] = sub_event[c].astype(str)
         if target_columns:
+            logger.info(f"Storing the following target columns for {col} {level}: {target_columns}")
             sub_event = sub_event[[x for x in target_columns if x in sub_event.columns]]
+        logger.info(f"Normalizing nulls for {level} {col}")
+        sub_event = utils.replace_nulls(sub_event)
         utils.df_to_parquet(
-            sub_event,
-            target_dir=f"{args.output_dir}/{level}/{col}",
-            chunk_size=200,
+            sub_event, target_dir=f"{args.output_dir}/{level}/{col}", chunk_size=200, object_encoding="infer"
         )
 
 

Database/scr/normalize_locations.py

@@ -140,8 +140,9 @@ def geocode_api_request(
 
     def normalize_locations(
         self, area: str, is_country: bool = False, in_country: str = None
-    ) -> tuple[str, str, dict] | None:
+    ) -> tuple[str, str | None, dict | None]:
         """Queries a geocode service for a location (country or smaller) and returns the top result"""
+        original_area = area
         try:
             try:
                 if area:
@@ -164,7 +165,7 @@ def normalize_locations(
             unsd_search_output = self._get_unsd_region(area, return_name=True) if area and is_country else None
             if unsd_search_output:
                 # TODO: add geojson for unsd regions
-                return [unsd_search_output, "UNSD region", None]
+                return [unsd_search_output.title(), "UNSD region", None]
 
             area = area.lower().strip()
             if "_" in area:
@@ -336,7 +337,7 @@ def normalize_locations(
                 f"Could not find location {area}; is_country: {is_country}; in_country: {in_country}. Error message: {err}."
             )
             # return un-normalized area name
-            return (area, None, None)
+            return (original_area, None, None)
 
     def _get_unsd_region(
         self, area, fuzzy_match_n: int = 1, fuzzy_match_cuttoff: float = 0.8, return_name: bool = False

Database/scr/normalize_utils.py

@@ -132,6 +132,28 @@ def eval(self, x: list | str):
             )
             return None
 
+    @staticmethod
+    def filter_null_list(lst: list) -> list:
+        new_list = []
+        for l in lst:
+            if isinstance(l, str):
+                if l.lower().strip() not in ["null", "none"]:
+                    new_list.append(l)
+            elif l == float("nan") or l is None:
+                pass
+            else:
+                new_list.append(l)
+        return new_list
+
+    @staticmethod
+    def filter_null_str(l: str | None) -> str | None:
+        if isinstance(l, str):
+            if l.lower().strip() in ["null", "none"]:
+                return None
+        if l == float("nan") or l is None:
+            return None
+        return l
+
     @staticmethod
     def simple_country_check(c: str):
         try: