arcgis column name fix (#1581)

eliminates duplicate column and ensures all column names are unique.

data/data-pipeline/data_pipeline/etl/score/constants.py

@@ -246,7 +246,6 @@ TILES_SCORE_COLUMNS = {
     field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD: "IA_LMI_ET",
     field_names.ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD: "IA_UN_ET",
     field_names.ISLAND_POVERTY_PCTILE_THRESHOLD: "IA_POV_ET",
-    field_names.FPL_200_SERIES: "FPL200S",
     field_names.THRESHOLD_COUNT: "TC",
     field_names.CATEGORY_COUNT: "CC",
     field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD: "IAULHSE",
@@ -269,7 +268,6 @@ TILES_SCORE_COLUMNS = {
     field_names.COLLEGE_NON_ATTENDANCE_FIELD: "NCA",
     # This is logically equivalent to "non-college greater than 80%"
     field_names.COLLEGE_ATTENDANCE_LESS_THAN_20_FIELD: "CA_LT20",
-    field_names.LOW_INCOME_THRESHOLD: "FPL200S",
     # Booleans for the front end about the types of thresholds exceeded
     field_names.CLIMATE_THRESHOLD_EXCEEDED: "M_CLT_EOMI",
     field_names.ENERGY_THRESHOLD_EXCEEDED: "M_ENY_EOMI",
@@ -280,6 +278,8 @@ TILES_SCORE_COLUMNS = {
     field_names.HEALTH_THRESHOLD_EXCEEDED: "M_HLTH_EOMI",
     field_names.WORKFORCE_THRESHOLD_EXCEEDED: "M_WKFC_EOMI",
     # These are the booleans for socioeconomic indicators
+    ## this measures low income boolean
+    field_names.FPL_200_SERIES: "FPL200S",
     ## Low high school and low higher ed for t&wd
     field_names.WORKFORCE_SOCIO_INDICATORS_EXCEEDED: "M_WKFC_EBSI",
     ## FPL 200 and low higher ed for all others

data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py

@@ -284,13 +284,21 @@ class GeoScoreETL(ExtractTransformLoad):
 
         def create_esri_codebook(codebook):
             """temporary: helper to make a codebook for esri shapefile only"""
+
+            shapefile_column_field = "shapefile_column"
+            internal_column_name_field = "column_name"
+            column_description_field = "column_description"
+
             logger.info("Creating a codebook that uses the csv names")
             codebook = (
                 pd.Series(codebook)
                 .reset_index()
                 .rename(
                     # kept as strings because no downstream impacts
-                    columns={0: "column_name", "index": "shapefile_column"}
+                    columns={
+                        0: internal_column_name_field,
+                        "index": shapefile_column_field,
+                    }
                 )
             )
 
@@ -304,10 +312,21 @@ class GeoScoreETL(ExtractTransformLoad):
                 object_value="label",
             )
 
-            codebook["column_description"] = codebook["column_name"].map(
-                column_rename_dict
+            codebook[column_description_field] = codebook[
+                internal_column_name_field
+            ].map(column_rename_dict)
+
+            codebook[
+                [
+                    shapefile_column_field,
+                    internal_column_name_field,
+                    column_description_field,
+                ]
+            ].to_csv(
+                self.SCORE_SHP_CODE_CSV,
+                index=False,
             )
-            codebook.to_csv(self.SCORE_SHP_CODE_CSV, index=False)
+            logger.info("Completed writing codebook")
 
         def write_esri_shapefile():
             logger.info("Producing ESRI shapefiles")
@@ -321,19 +340,25 @@ class GeoScoreETL(ExtractTransformLoad):
                 short: long
                 for long, short in constants.TILES_SCORE_COLUMNS.items()
             }
-            for column in self.geojson_score_usa_high.columns:
-                # take first 10 characters, max due to ESRI constraints
-                new_col = column[:10]
+
+            for i, column in enumerate(self.geojson_score_usa_high.columns):
+                # take first 6 characters and add a number to ensure uniqueness
+                # this is the max due to esri (index can be 3-digits)
+                if len(column) > 10:
+                    new_col = column[:6] + f"_{i}"
+                else:
+                    new_col = column
                 codebook[new_col] = reversed_tiles.get(column, column)
                 if new_col != column:
                     renaming_map[column] = new_col
 
-            create_esri_codebook(codebook)
-
             self.geojson_score_usa_high.rename(columns=renaming_map).to_file(
                 self.SCORE_SHP_FILE
             )
             logger.info("Completed writing shapefile")
+
+            create_esri_codebook(codebook)
+
             arcgis_zip_file_path = self.SCORE_SHP_PATH / "usa.zip"
             arcgis_files = []
             for file in os.listdir(self.SCORE_SHP_PATH):