Make import more obvious (#1835)

usds · Sep 6, 2022 · 2d0a4aa · 2d0a4aa
1 parent 17669c9
commit 2d0a4aa
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Showing 2 changed files with 26 additions and 27 deletions.
diff --git a/data/data-pipeline/data_pipeline/tests/score/fixtures.py b/data/data-pipeline/data_pipeline/tests/score/fixtures.py
@@ -2,16 +2,15 @@
 import pytest
 from data_pipeline.config import settings
 from data_pipeline.score import field_names
+from data_pipeline.score.field_names import GEOID_TRACT_FIELD
 from data_pipeline.etl.score import constants
 
-GEOID_TRACT_FIELD_NAME = field_names.GEOID_TRACT_FIELD
-
 
 @pytest.fixture(scope="session")
 def final_score_df():
     return pd.read_csv(
         settings.APP_ROOT / "data" / "score" / "csv" / "full" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: str},
+        dtype={GEOID_TRACT_FIELD: str},
         low_memory=False,
     )
 
@@ -21,7 +20,7 @@ def census_df():
     census_csv = constants.DATA_PATH / "dataset" / "census_acs_2019" / "usa.csv"
     return pd.read_csv(
         census_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -31,7 +30,7 @@ def ejscreen_df():
     ejscreen_csv = constants.DATA_PATH / "dataset" / "ejscreen" / "usa.csv"
     return pd.read_csv(
         ejscreen_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -43,7 +42,7 @@ def hud_housing_df():
     )
     return pd.read_csv(
         hud_housing_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -53,7 +52,7 @@ def cdc_places_df():
     cdc_places_csv = constants.DATA_PATH / "dataset" / "cdc_places" / "usa.csv"
     return pd.read_csv(
         cdc_places_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -68,7 +67,7 @@ def census_acs_median_incomes_df():
     )
     return pd.read_csv(
         census_acs_median_incomes_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -80,7 +79,7 @@ def cdc_life_expectancy_df():
     )
     return pd.read_csv(
         cdc_life_expectancy_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -92,7 +91,7 @@ def doe_energy_burden_df():
     )
     return pd.read_csv(
         doe_energy_burden_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -101,7 +100,7 @@ def doe_energy_burden_df():
 def national_risk_index_df():
     return pd.read_csv(
         constants.DATA_PATH / "dataset" / "national_risk_index" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -110,7 +109,7 @@ def national_risk_index_df():
 def dot_travel_disadvantage_df():
     return pd.read_csv(
         constants.DATA_PATH / "dataset" / "travel_composite" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -119,7 +118,7 @@ def dot_travel_disadvantage_df():
 def fsf_fire_df():
     return pd.read_csv(
         constants.DATA_PATH / "dataset" / "fsf_wildfire_risk" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -128,7 +127,7 @@ def fsf_fire_df():
 def fsf_flood_df():
     return pd.read_csv(
         constants.DATA_PATH / "dataset" / "fsf_flood_risk" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -137,7 +136,7 @@ def fsf_flood_df():
 def nature_deprived_df():
     return pd.read_csv(
         constants.DATA_PATH / "dataset" / "nlcd_nature_deprived" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -146,7 +145,7 @@ def nature_deprived_df():
 def eamlis_df():
     return pd.read_csv(
         constants.DATA_PATH / "dataset" / "eamlis" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -155,7 +154,7 @@ def eamlis_df():
 def fuds_df():
     return pd.read_csv(
         constants.DATA_PATH / "dataset" / "us_army_fuds" / "usa.csv",
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -167,7 +166,7 @@ def geocorr_urban_rural_df():
     )
     return pd.read_csv(
         geocorr_urban_rural_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -179,7 +178,7 @@ def census_decennial_df():
     )
     return pd.read_csv(
         census_decennial_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -191,7 +190,7 @@ def census_2010_df():
     )
     return pd.read_csv(
         census_2010_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -202,7 +201,7 @@ def hrs_df():
 
     return pd.read_csv(
         hrs_csv,
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
     )
 
@@ -212,8 +211,8 @@ def national_tract_df():
     national_tract_csv = constants.DATA_CENSUS_CSV_FILE_PATH
     return pd.read_csv(
         national_tract_csv,
-        names=[GEOID_TRACT_FIELD_NAME],
-        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        names=[GEOID_TRACT_FIELD],
+        dtype={GEOID_TRACT_FIELD: "string"},
         low_memory=False,
         header=None,
     )
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_output.py b/data/data-pipeline/data_pipeline/tests/score/test_output.py
@@ -6,6 +6,7 @@
 import pandas as pd
 import numpy as np
 from data_pipeline.score import field_names
+from data_pipeline.score.field_names import GEOID_TRACT_FIELD
 from .fixtures import (
     final_score_df,
     ejscreen_df,
@@ -30,7 +31,6 @@
 
 
 pytestmark = pytest.mark.smoketest
-GEOID_TRACT_FIELD_NAME = field_names.GEOID_TRACT_FIELD
 UNMATCHED_TRACK_THRESHOLD = 1000
 
 
@@ -258,7 +258,7 @@ def test_data_sources(
         final = "final_"
         df: pd.DataFrame = final_score_df.merge(
             data_source,
-            on=GEOID_TRACT_FIELD_NAME,
+            on=GEOID_TRACT_FIELD,
             indicator="MERGE",
             suffixes=(final, f"_{data_source_name}"),
             how="outer",
@@ -267,7 +267,7 @@ def test_data_sources(
         # Make our lists of columns for later comparison
         core_cols = data_source.columns.intersection(
             final_score_df.columns
-        ).drop(GEOID_TRACT_FIELD_NAME)
+        ).drop(GEOID_TRACT_FIELD)
         data_source_columns = [f"{col}_{data_source_name}" for col in core_cols]
         final_columns = [f"{col}{final}" for col in core_cols]
         assert (
@@ -312,7 +312,7 @@ def test_data_sources(
 def test_output_tracts(final_score_df, national_tract_df):
     df = final_score_df.merge(
         national_tract_df,
-        on=GEOID_TRACT_FIELD_NAME,
+        on=GEOID_TRACT_FIELD,
         how="outer",
         indicator="MERGE",
     )