Fix linter and dependency issues (DOI-DO#8)

data/data-pipeline/data_pipeline/application.py

@@ -58,6 +58,7 @@
     help=dataset_cli_help,
 )
 
+
 @click.group()
 def cli():
     """Defines a click group for the commands below"""
@@ -415,6 +416,7 @@ def clear_data_source_cache(dataset: str):
 
     log_goodbye()
 
+
 @cli.command(
     help="Generate scoring and tiles",
 )
@@ -441,6 +443,7 @@ def full_run(ctx, use_cache):
     ctx.invoke(etl_run, dataset=None, use_cache=use_cache)
     ctx.invoke(full_post_etl)
 
+
 def log_title(title: str, subtitle: str = None):
     """Logs a title in our fancy title format"""
     logger.info("-" * LOG_LINE_WIDTH)

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

@@ -10,6 +10,7 @@
 
 logger = get_module_logger(__name__)
 
+
 class Downloader:
     """A simple class to encapsulate the download capabilities of the application"""
 
@@ -44,6 +45,7 @@ def download_file_from_url(
             file_contents = response.content
             logger.debug("Downloaded.")
         else:
+            # pylint: disable-next=broad-exception-raised
             raise Exception(
                 f"HTTP response {response.status_code} from url {file_url}. Info: {response.content}"
             )

data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py

@@ -39,6 +39,7 @@ def _choose_best_mask(
     for mask in masks_in_priority_order:
         if any(geo_df[mask][column_to_impute].notna()):
             return mask
+    # pylint: disable-next=broad-exception-raised
     raise Exception("No mask found")
 
 

data/data-pipeline/data_pipeline/score/score_narwhal.py

@@ -1014,12 +1014,12 @@ def _mark_tribal_dacs(self) -> None:
         )
 
     def _mark_territory_dacs(self) -> None:
-        """Territory tracts that are flagged as low income are Score N communities.
-        """
+        """Territory tracts that are flagged as low income are Score N communities."""
         self.df[field_names.SCORE_N_COMMUNITIES] = np.where(
-            self.df[field_names.GEOID_TRACT_FIELD]
-                .str.startswith(tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)) &
-            self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED],
+            self.df[field_names.GEOID_TRACT_FIELD].str.startswith(
+                tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)
+            )
+            & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED],
             True,
             self.df[field_names.SCORE_N_COMMUNITIES],
         )
@@ -1049,9 +1049,14 @@ def _mark_poverty_flag(self) -> None:
             combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_200_FPL_FIELD_2010,
             threshold_cutoff_for_island_areas=self.LOW_INCOME_THRESHOLD,
         )
-        self.df.loc[self.df[field_names.GEOID_TRACT_FIELD].str.startswith(tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)),
-                    field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] = (
-            self.df[island_areas_poverty_200_criteria_field_name] >= self.LOW_INCOME_THRESHOLD
+        self.df.loc[
+            self.df[field_names.GEOID_TRACT_FIELD].str.startswith(
+                tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)
+            ),
+            field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED,
+        ] = (
+            self.df[island_areas_poverty_200_criteria_field_name]
+            >= self.LOW_INCOME_THRESHOLD
         )
 
     def _get_percent_of_tract_that_is_dac(self) -> float:

data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py

@@ -2,7 +2,6 @@
 import pandas as pd
 import pytest
 from data_pipeline.config import settings
-from data_pipeline.etl.score import constants
 from data_pipeline.etl.score.etl_score import ScoreETL
 from data_pipeline.score import field_names
 from data_pipeline.score.score_narwhal import ScoreNarwhal
@@ -13,9 +12,11 @@
 
 TEST_DATA_FOLDER = settings.APP_ROOT / "tests" / "score" / "test_utils" / "data"
 
+
 @pytest.fixture
 def toy_score_df(scope="module"):
-    return pd.read_csv(TEST_DATA_FOLDER / "test_drop_tracts_from_percentile.csv",
+    return pd.read_csv(
+        TEST_DATA_FOLDER / "test_drop_tracts_from_percentile.csv",
         dtype={field_names.GEOID_TRACT_FIELD: str},
     )
 
@@ -84,41 +85,46 @@ def test_drop_all_tracts(toy_score_df):
 
 
 def test_mark_territory_dacs():
-    test_data = pd.read_csv(TEST_DATA_FOLDER / "test_mark_territory_dacs.csv",
+    test_data = pd.read_csv(
+        TEST_DATA_FOLDER / "test_mark_territory_dacs.csv",
         dtype={field_names.GEOID_TRACT_FIELD: str},
     )
     # Sanity check on the input data
     assert not test_data[field_names.SCORE_N_COMMUNITIES].all()
 
     scorer = ScoreNarwhal(test_data)
     scorer._mark_territory_dacs()
-    territory_filter = test_data[field_names.GEOID_TRACT_FIELD].str.startswith(tuple(constants.TILES_ISLAND_AREA_FIPS_CODES))
     # Check territories are set to true
-    expected_new_dacs_filter = (
-        test_data[field_names.GEOID_TRACT_FIELD].isin(['60050951100', '66010951100', '69110001101', '78010990000'])
+    expected_new_dacs_filter = test_data[field_names.GEOID_TRACT_FIELD].isin(
+        ["60050951100", "66010951100", "69110001101", "78010990000"]
     )
-    assert test_data.loc[expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES].all()
+    assert test_data.loc[
+        expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES
+    ].all()
     # Non-territories are still false
-    assert not test_data.loc[~expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES].all()
+    assert not test_data.loc[
+        ~expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES
+    ].all()
 
 
 def test_mark_poverty_flag():
-    test_data = pd.read_csv(TEST_DATA_FOLDER / "test_mark_poverty_flag.csv",
+    test_data = pd.read_csv(
+        TEST_DATA_FOLDER / "test_mark_poverty_flag.csv",
         dtype={field_names.GEOID_TRACT_FIELD: str},
     )
     # Sanity check on the input data
     assert not test_data[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED].all()
 
     scorer = ScoreNarwhal(test_data)
     scorer._mark_poverty_flag()
-    expected_low_income_filter = (
-        test_data[field_names.GEOID_TRACT_FIELD].isin(['36087011302', '66010951100', '78010990000'])
+    expected_low_income_filter = test_data[field_names.GEOID_TRACT_FIELD].isin(
+        ["36087011302", "66010951100", "78010990000"]
     )
     # Three tracts are set to true
-    assert (
-        test_data[expected_low_income_filter][field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED].all()
-    )
+    assert test_data[expected_low_income_filter][
+        field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED
+    ].all()
     # Everything else is false
-    assert (
-        not test_data[~expected_low_income_filter][field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED].all()
-    )
+    assert not test_data[~expected_low_income_filter][
+        field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED
+    ].all()