columns to keep and tests

.github/workflows/deploy_be_staging.yml

@@ -109,7 +109,7 @@ jobs:
           # Deploy to S3 for the staging URL
           message: |
             ** Map Deployed! **   
-            Map with Staging Backend: https://screeningtool.geoplatform.gov/en/cejst/?flags=stage_hash=${{env.PR_NUMBER}}/${{env.SHA_NUMBER}}
+            Map with Staging Backend: https://screeningtool.geoplatform.gov/en?flags=stage_hash=${{env.PR_NUMBER}}/${{env.SHA_NUMBER}}
             Find tiles here: https://justice40-data.s3.amazonaws.com/data-pipeline-staging/${{env.PR_NUMBER}}/${{env.SHA_NUMBER}}/data/score/tiles
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           repo-token-user-login: "github-actions[bot]"

data/data-pipeline/data_pipeline/etl/base.py

@@ -71,6 +71,13 @@ class ExtractTransformLoad:
     # COLUMNS_TO_KEEP is used to identify which columns to keep in the output df.
     COLUMNS_TO_KEEP: typing.List[str] = None
 
+    # INPUT_GEOID_TRACT_FIELD_NAME is the field name that identifies the Census Tract ID
+    # on the input file
+    INPUT_GEOID_TRACT_FIELD_NAME: str = None
+
+    # NULL_REPRESENTATION is how nulls are represented on the input field
+    NULL_REPRESENTATION: str = None
+
     # Thirteen digits in a census block group ID.
     EXPECTED_CENSUS_BLOCK_GROUPS_CHARACTER_LENGTH: int = 13
     # TODO: investigate. Census says there are only 217,740 CBGs in the US. This might
@@ -84,10 +91,12 @@ class ExtractTransformLoad:
     #  periods. https://github.com/usds/justice40-tool/issues/964
     EXPECTED_MAX_CENSUS_TRACTS: int = 74160
 
+    # We use ourput_df as the final dataframe to use to write to the CSV
+    # It is used on the "load" base class method
     output_df: pd.DataFrame = None
 
     @classmethod
-    def yaml_config_load(cls):
+    def yaml_config_load(cls) -> dict:
         # check if the class instance has score YAML definitions
         datasets_config = load_yaml_dict_from_file(
             cls.DATASET_CONFIG / "datasets.yml",
@@ -108,10 +117,24 @@ class ExtractTransformLoad:
             )
             sys.exit()
 
-        # set the fields
+        # set some of the basic fields
         cls.LAST_UPDATED_YEAR = dataset_config["last_updated_year"]
         cls.SOURCE_URL = dataset_config["source_url"]
         cls.INPUT_EXTRACTED_FILE_NAME = dataset_config["extracted_file_name"]
+        cls.INPUT_GEOID_TRACT_FIELD_NAME = dataset_config[
+            "input_geoid_tract_field_name"
+        ]
+        cls.NULL_REPRESENTATION = dataset_config["null_representation"]
+
+        # get the columns to write on the CSV
+        cls.COLUMNS_TO_KEEP = [
+            cls.GEOID_TRACT_FIELD_NAME,  # always index with geoid tract id
+        ]
+        for field in dataset_config["load_fields"]:
+            cls.COLUMNS_TO_KEEP.append(field["long_name"])
+
+        # return the config dict
+        return dataset_config
 
     # This is a classmethod so it can be used by `get_data_frame` without
     # needing to create an instance of the class. This is a use case in `etl_score`.
@@ -263,8 +286,7 @@ class ExtractTransformLoad:
 
         Data is written in the specified local data folder or remote AWS S3 bucket.
 
-        Uses the directory from `self.OUTPUT_DIR` and the file name from
+        Uses the directory and the file name from `self._get_output_file_path`.
-        `self._get_output_file_path`.
         """
         logger.info(f"Saving `{self.NAME}` CSV")
 

data/data-pipeline/data_pipeline/etl/score/config/datasets.yml

@@ -40,7 +40,7 @@ datasets:
         excel_download: true
       - short_name: "has_ag_val"
         df_field_name: "CONTAINS_AGRIVALUE"
-        long_name: "Expected building loss rate (Natural Hazards Risk Index)"
+        long_name: "Contains agricultural value"
         field_type: bool
         tile_include: true
         csv_download: true

data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py

@@ -26,8 +26,15 @@ class NationalRiskIndexETL(ExtractTransformLoad):
 
     def __init__(self):
         # load YAML config
-        super().yaml_config_load()
+        self.DATASET_CONFIG = super().yaml_config_load()
 
+        # define the full path for the input CSV file
+        self.INPUT_CSV = self.get_tmp_path() / self.INPUT_EXTRACTED_FILE_NAME
+
+        # this is the main dataframe
+        self.df: pd.DataFrame
+
+        # Start dataset-specific vars here
         self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_INPUT_FIELD_NAME = (
             "EAL_SCORE"
         )
@@ -58,18 +65,6 @@ class NationalRiskIndexETL(ExtractTransformLoad):
         )
         self.CONTAINS_AGRIVALUE = "Contains agricultural value"
 
-        self.COLUMNS_TO_KEEP = [
-            self.GEOID_TRACT_FIELD_NAME,
-            self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME,
-            self.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME,
-            self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME,
-            self.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME,
-            self.CONTAINS_AGRIVALUE,
-        ]
-
-        self.INPUT_CSV = self.get_tmp_path() / self.INPUT_EXTRACTED_FILE_NAME
-        self.df: pd.DataFrame
-
     def extract(self) -> None:
         """Unzips NRI dataset from the FEMA data source and writes the files
         to the temporary data folder for use in the transform() method
@@ -90,19 +85,18 @@ class NationalRiskIndexETL(ExtractTransformLoad):
         """
         logger.info("Transforming National Risk Index Data")
 
-        NRI_TRACT_COL = "TRACTFIPS"  # Census Tract Column in NRI data
-
         # read in the unzipped csv from NRI data source then rename the
         # Census Tract column for merging
         df_nri: pd.DataFrame = pd.read_csv(
             self.INPUT_CSV,
-            dtype={NRI_TRACT_COL: "string"},
+            dtype={self.INPUT_GEOID_TRACT_FIELD_NAME: "string"},
-            na_values=["None"],
+            na_values=[self.NULL_REPRESENTATION],
             low_memory=False,
         )
+
         df_nri.rename(
             columns={
-                NRI_TRACT_COL: self.GEOID_TRACT_FIELD_NAME,
+                self.INPUT_GEOID_TRACT_FIELD_NAME: self.GEOID_TRACT_FIELD_NAME,
                 self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_INPUT_FIELD_NAME: self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME,
             },
             inplace=True,
@@ -185,6 +179,7 @@ class NationalRiskIndexETL(ExtractTransformLoad):
         # Note: `round` is smart enough to only apply to float columns.
         df_nri = df_nri.round(10)
 
+        # Assign the final df to the class' output_df for the load method
         self.output_df = df_nri
 
     def load(self) -> None:

data/data-pipeline/data_pipeline/tests/conftest.py

@@ -52,8 +52,3 @@ def mock_etl(monkeypatch, mock_paths) -> None:
     data_path, tmp_path = mock_paths
     monkeypatch.setattr(ExtractTransformLoad, "DATA_PATH", data_path)
     monkeypatch.setattr(ExtractTransformLoad, "TMP_PATH", tmp_path)
-    monkeypatch.setattr(
-        ExtractTransformLoad,
-        "CONTENT_CONFIG",
-        Path.cwd() / "data_pipeline" / "score" / "config",
-    )

data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py

@@ -119,6 +119,7 @@ class TestETL:
         """
         # Setup
         etl = self._get_instance_of_etl_class()
+        etl.__init__()
         data_path, tmp_path = mock_paths
 
         assert etl.DATA_PATH == data_path
@@ -256,6 +257,7 @@ class TestETL:
         etl = self._setup_etl_instance_and_run_extract(
             mock_etl=mock_etl, mock_paths=mock_paths
         )
+        etl.__init__()
         etl.transform()
 
         assert etl.output_df is not None
@@ -273,6 +275,7 @@ class TestETL:
         """
         # setup - input variables
         etl = self._get_instance_of_etl_class()
+        etl.__init__()
 
         # setup - mock transform step
         df_transform = pd.read_csv(