fixing merge conflicts

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

@@ -1,8 +1,7 @@
 import pandas as pd
-
+from data_pipeline.config import settings
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.utils import get_module_logger
-from data_pipeline.config import settings
 
 logger = get_module_logger(__name__)
 

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

@@ -1,13 +1,15 @@
 import pathlib
 from pathlib import Path
-import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+import pandas as pd
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.etl.score.etl_utils import (
     compare_to_list_of_expected_state_fips_codes,
 )
 from data_pipeline.score import field_names
-from data_pipeline.utils import get_module_logger, download_file_from_url
+from data_pipeline.utils import download_file_from_url
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

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

@@ -1,9 +1,11 @@
 import typing
-import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
-from data_pipeline.utils import get_module_logger, download_file_from_url
+import pandas as pd
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.score import field_names
+from data_pipeline.utils import download_file_from_url
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

data/data-pipeline/data_pipeline/etl/sources/cdc_svi_index/README.md

@@ -53,7 +53,7 @@ For SVI 2018, the authors also included two adjunct variables, 1) 2014-2018 ACS
 
 **Important Notes**
 
-1. Tracts with zero estimates for the total population (N = 645 for the U.S.) were removed during the ranking process. These tracts were added back to the SVI databases after ranking. 
+1. Tracts with zero estimates for the total population (N = 645 for the U.S.) were removed during the ranking process. These tracts were added back to the SVI databases after ranking.
 
 2. The TOTPOP field value is 0, but the percentile ranking fields (RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, and RPL_THEMES) were set to -999.
 
@@ -66,4 +66,4 @@ here: https://www.census.gov/programs-surveys/acs/data/variance-tables.html.
 
 For selected ACS 5-year Detailed Tables, “Users can calculate margins of error for aggregated data by using the variance replicates. Unlike available approximation formulas, this method results in an exact margin of error by using the covariance term.”
 
-MOEs are _not_ included nor considered during this data processing nor for the scoring comparison tool.
+MOEs are _not_ included nor considered during this data processing nor for the scoring comparison tool.

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

@@ -1,9 +1,8 @@
-import pandas as pd
 import numpy as np
-
+import pandas as pd
 from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.utils import get_module_logger
 from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

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

@@ -3,12 +3,12 @@ import json
 import subprocess
 from enum import Enum
 from pathlib import Path
+
 import geopandas as gpd
-
 from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.utils import get_module_logger, unzip_file_from_url
-
 from data_pipeline.etl.sources.census.etl_utils import get_state_fips_codes
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

data/data-pipeline/data_pipeline/etl/sources/census/etl_utils.py

@@ -5,13 +5,11 @@ from pathlib import Path
 
 import pandas as pd
 from data_pipeline.config import settings
-from data_pipeline.utils import (
-    get_module_logger,
-    remove_all_dirs_from_dir,
-    remove_files_from_dir,
-    unzip_file_from_url,
-    zip_directory,
-)
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import remove_all_dirs_from_dir
+from data_pipeline.utils import remove_files_from_dir
+from data_pipeline.utils import unzip_file_from_url
+from data_pipeline.utils import zip_directory
 
 logger = get_module_logger(__name__)
 

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

@@ -1,19 +1,19 @@
-from collections import namedtuple
 import os
-import pandas as pd
-import geopandas as gpd
+from collections import namedtuple
 
+import geopandas as gpd
+import pandas as pd
 from data_pipeline.config import settings
 from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.etl.sources.census_acs.etl_utils import (
-    retrieve_census_acs_data,
-)
 from data_pipeline.etl.sources.census_acs.etl_imputations import (
     calculate_income_measures,
 )
-
-from data_pipeline.utils import get_module_logger, unzip_file_from_url
+from data_pipeline.etl.sources.census_acs.etl_utils import (
+    retrieve_census_acs_data,
+)
 from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

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

@@ -1,7 +1,10 @@
-from typing import Any, List, NamedTuple, Tuple
-import pandas as pd
-import geopandas as gpd
+from typing import Any
+from typing import List
+from typing import NamedTuple
+from typing import Tuple
 
+import geopandas as gpd
+import pandas as pd
 from data_pipeline.score import field_names
 from data_pipeline.utils import get_module_logger
 

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

@@ -1,10 +1,9 @@
 import os
 from pathlib import Path
 from typing import List
+
 import censusdata
 import pandas as pd
-
-
 from data_pipeline.etl.sources.census.etl_utils import get_state_fips_codes
 from data_pipeline.utils import get_module_logger
 

data/data-pipeline/data_pipeline/etl/sources/census_acs_2010/etl.py

@@ -1,11 +1,10 @@
 import pandas as pd
-
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.etl.sources.census_acs.etl_utils import (
     retrieve_census_acs_data,
 )
-from data_pipeline.utils import get_module_logger
 from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

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

@@ -1,13 +1,14 @@
 import json
 from pathlib import Path
+
 import numpy as np
 import pandas as pd
 import requests
-
-from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.utils import get_module_logger
 from data_pipeline.config import settings
-from data_pipeline.utils import unzip_file_from_url, download_file_from_url
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.utils import download_file_from_url
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

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

@@ -1,14 +1,13 @@
 import json
 from typing import List
-import requests
 
 import numpy as np
 import pandas as pd
-
-from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.utils import get_module_logger
-from data_pipeline.score import field_names
+import requests
 from data_pipeline.config import settings
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
 
 pd.options.mode.chained_assignment = "raise"
 

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

@@ -1,7 +1,8 @@
 from pathlib import Path
-import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+import pandas as pd
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

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

@@ -1,8 +1,9 @@
 from pathlib import Path
-import pandas as pd
 
+import pandas as pd
 from data_pipeline.config import settings
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/README.md

@@ -1,6 +1,6 @@
 # DOT travel barriers
 
-The below description is taken from DOT directly: 
+The below description is taken from DOT directly:
 
 Consistent with OMB’s Interim Guidance for the Justice40 Initiative, DOT’s interim definition of DACs includes (a) certain qualifying census tracts, (b) any Tribal land, or (c) any territory or possession of the United States. DOT has provided a mapping tool to assist applicants in identifying whether a project is located in a Disadvantaged Community, available at Transportation Disadvantaged Census Tracts (arcgis.com). A shapefile of the geospatial data is available  Transportation Disadvantaged Census Tracts shapefile (version 2 .0, posted 5/10/22).
 
@@ -13,4 +13,4 @@ The DOT interim definition for DACs was developed by an internal and external co
 Resilience disadvantage identifies communities vulnerable to hazards caused by climate change. (1)
 - Equity disadvantage identifies communities with a with a high percentile of persons (age 5+) who speak English "less than well." (1)
 
-The CEJST uses only Transportation Access Disadvantage. 
+The CEJST uses only Transportation Access Disadvantage.

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

@@ -1,10 +1,9 @@
 # pylint: disable=unsubscriptable-object
 # pylint: disable=unsupported-assignment-operation
-
-import pandas as pd
 import geopandas as gpd
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+import pandas as pd
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

data/data-pipeline/data_pipeline/etl/sources/eamlis/README.md

@@ -1,4 +1,4 @@
-The following is the description from eAMLIS as of August 16, 2022. 
+The following is the description from eAMLIS as of August 16, 2022.
 ---
 
 e-AMLIS is not a comprehensive database of all AML features or all AML grant activities. e-AMLIS is a national inventory that provides information about known abandoned mine land (AML) features including polluted waters. The majority of the data in e-AMLIS provides information about known coal AML features for the 25 states and 3 tribal SMCRA-approved AML Programs. e-AMLIS also provides limited information on non-coal AML features, and, non-coal reclamation projects as well as AML features for states and tribes that do not have an approved AML Program. Additionally, e-AMLIS only accounts for the direct construction cost to reclaim each AML feature that has been identified by states and Tribes. Other project costs such as planning, design, permitting, and construction oversight are not tracked in e-AMLIS.

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

@@ -1,9 +1,10 @@
 from pathlib import Path
+
 import geopandas as gpd
 import pandas as pd
 from data_pipeline.config import settings
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.etl.sources.geo_utils import add_tracts_for_geometries
 from data_pipeline.utils import get_module_logger
 

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

@@ -1,6 +1,6 @@
 import pandas as pd
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.score import field_names
 from data_pipeline.utils import get_module_logger
 

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

@@ -1,5 +1,4 @@
 import pandas as pd
-
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.utils import get_module_logger
 
@@ -58,7 +57,6 @@ class EJSCREENAreasOfConcernETL(ExtractTransformLoad):
 
         # TO DO: As a one off we did all the processing in a separate Notebook
         # Can add here later for a future PR
-        pass
 
     def load(self) -> None:
         if self.ejscreen_areas_of_concern_data_exists():

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

@@ -1,10 +1,11 @@
 from pathlib import Path
-import pandas as pd
 
+import pandas as pd
 from data_pipeline.config import settings
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.score import field_names
-from data_pipeline.utils import get_module_logger, unzip_file_from_url
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

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

@@ -1,9 +1,10 @@
 from pathlib import Path
-import pandas as pd
 
+import pandas as pd
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.score import field_names
-from data_pipeline.utils import get_module_logger, unzip_file_from_url
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

data/data-pipeline/data_pipeline/etl/sources/fsf_flood_risk/README.md

@@ -1,3 +1,3 @@
 # FSF flood risk data
 
-Flood risk computed as 1 in 100 year flood zone
+Flood risk computed as 1 in 100 year flood zone

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

@@ -1,10 +1,9 @@
 # pylint: disable=unsubscriptable-object
 # pylint: disable=unsupported-assignment-operation
-
 import pandas as pd
 from data_pipeline.config import settings
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/README.md

@@ -1,3 +1,3 @@
 # FSF wildfire risk data
 
-Fire risk computed as >= 0.003 burn risk probability
+Fire risk computed as >= 0.003 burn risk probability

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

@@ -1,10 +1,9 @@
 # pylint: disable=unsubscriptable-object
 # pylint: disable=unsupported-assignment-operation
-
 import pandas as pd
 from data_pipeline.config import settings
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

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

@@ -1,11 +1,12 @@
 """Utililities for turning geographies into tracts, using census data"""
-
+from functools import lru_cache
 from pathlib import Path
 from typing import Optional
-from functools import lru_cache
+
 import geopandas as gpd
 from data_pipeline.etl.sources.tribal.etl import TribalETL
 from data_pipeline.utils import get_module_logger
+
 from .census.etl import CensusETL
 
 logger = get_module_logger(__name__)

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

@@ -1,11 +1,9 @@
 import pandas as pd
-
 from data_pipeline.config import settings
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
-from data_pipeline.utils import (
-    get_module_logger,
-    unzip_file_from_url,
-)
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

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

@@ -1,8 +1,8 @@
 import pandas as pd
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
-from data_pipeline.utils import get_module_logger
 from data_pipeline.config import settings
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

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

@@ -1,9 +1,9 @@
 import pandas as pd
-from pandas.errors import EmptyDataError
-
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.etl.sources.census.etl_utils import get_state_fips_codes
-from data_pipeline.utils import get_module_logger, unzip_file_from_url
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
+from pandas.errors import EmptyDataError
 
 logger = get_module_logger(__name__)
 

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

@@ -1,5 +1,6 @@
 import pandas as pd
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

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

@@ -1,9 +1,8 @@
 import pandas as pd
 import requests
-
+from data_pipeline.config import settings
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.utils import get_module_logger
-from data_pipeline.config import settings
 
 logger = get_module_logger(__name__)
 

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

@@ -1,10 +1,9 @@
-import pandas as pd
 import geopandas as gpd
-
-from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.utils import get_module_logger
-from data_pipeline.score import field_names
+import pandas as pd
 from data_pipeline.config import settings
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 
@@ -96,4 +95,3 @@ class MappingForEJETL(ExtractTransformLoad):
 
     def validate(self) -> None:
         logger.info("Validating Mapping For EJ Data")
-        pass

data/data-pipeline/data_pipeline/etl/sources/mapping_inequality/data/holc_grades_manually_mapped.csv

@@ -37,4 +37,4 @@ Oklahoma City,90R,D
 Milwaukee Co.,S-D1,D
 Milwaukee Co.,S-D2,D
 Milwaukee Co.,S-D3,D
-Milwaukee Co.,S-D4,D
+Milwaukee Co.,S-D4,D

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

@@ -1,10 +1,11 @@
 import pathlib
+
 import numpy as np
 import pandas as pd
-
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.score import field_names
-from data_pipeline.utils import download_file_from_url, get_module_logger
+from data_pipeline.utils import download_file_from_url
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

data/data-pipeline/data_pipeline/etl/sources/maryland_ejscreen/README.md

@@ -8,7 +8,7 @@ According to the documentation:
 
 There exist two data categories: Population Burden and Population Characteristics.
 
-There are two indicators within Population Burden: Exposure, and Socioeconomic. Within Population Characteristics, there exist two indicators: Sensitive, Environmental Effects. Each respective indicator contains several relevant covariates, and an averaged score. 
+There are two indicators within Population Burden: Exposure, and Socioeconomic. Within Population Characteristics, there exist two indicators: Sensitive, Environmental Effects. Each respective indicator contains several relevant covariates, and an averaged score.
 
 The two "Pollution Burden" average scores are then averaged together and the result is multiplied by the average of the "Population Characteristics" categories to get the total EJ Score for each tract.
 
@@ -20,4 +20,4 @@ Furthermore, it was determined that Bladensburg residents are at a higher risk o
 
 Source:
 
-Driver, A.; Mehdizadeh, C.; Bara-Garcia, S.; Bodenreider, C.; Lewis, J.; Wilson, S. Utilization of the Maryland Environmental Justice Screening Tool: A Bladensburg, Maryland Case Study. Int. J. Environ. Res. Public Health 2019, 16, 348. 
+Driver, A.; Mehdizadeh, C.; Bara-Garcia, S.; Bodenreider, C.; Lewis, J.; Wilson, S. Utilization of the Maryland Environmental Justice Screening Tool: A Bladensburg, Maryland Case Study. Int. J. Environ. Res. Public Health 2019, 16, 348.

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

@@ -1,11 +1,11 @@
 from glob import glob
+
 import geopandas as gpd
 import pandas as pd
-
-from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.utils import get_module_logger
-from data_pipeline.score import field_names
 from data_pipeline.config import settings
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

data/data-pipeline/data_pipeline/etl/sources/michigan_ejscreen/README.md

@@ -29,4 +29,4 @@ Sources:
 * Minnesota Pollution Control Agency. (2015, December 15). Environmental Justice Framework Report.
 Retrieved from https://www.pca.state.mn.us/sites/default/files/p-gen5-05.pdf.
 
-* Faust, J., L. August, K. Bangia, V. Galaviz, J. Leichty, S. Prasad… and L. Zeise. (2017, January). Update to the California Communities Environmental Health Screening Tool CalEnviroScreen 3.0. Retrieved from OEHHA website: https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf
+* Faust, J., L. August, K. Bangia, V. Galaviz, J. Leichty, S. Prasad… and L. Zeise. (2017, January). Update to the California Communities Environmental Health Screening Tool CalEnviroScreen 3.0. Retrieved from OEHHA website: https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf

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

@@ -1,9 +1,8 @@
 import pandas as pd
-
-from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.utils import get_module_logger
-from data_pipeline.score import field_names
 from data_pipeline.config import settings
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 

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

@@ -2,10 +2,9 @@
 # but it may be a known bug. https://github.com/PyCQA/pylint/issues/1498
 # pylint: disable=unsubscriptable-object
 # pylint: disable=unsupported-assignment-operation
-
 import pandas as pd
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/README.md

@@ -2,7 +2,7 @@
 
 The following dataset was compiled by TPL (Trust for Public Lands) using NCLD data. We define as: AREA - [CROPLAND] - [IMPERVIOUS SURFACES].
 
-## Codebook 
+## Codebook
 - GEOID10 – Census tract ID
 - SF – State Name
 - CF – County Name
@@ -13,7 +13,7 @@ The following dataset was compiled by TPL (Trust for Public Lands) using NCLD da
 - AcresCrops – Acres crops calculated by summing all cells in the NLCD Cropland Data Layer crop classes.
 - PctCrops – Formula: AcresCrops/TractAcres*100.
 - PctImperv – Mean imperviousness for each census tract.
-    - CAVEAT: Where tracts extend into open water, mean imperviousness may be underestimated.  
+    - CAVEAT: Where tracts extend into open water, mean imperviousness may be underestimated.
 - __TO USE__ PctNatural – Formula: 100 – PctCrops – PctImperv.
 - PctNat90 – Tract in or below 10th percentile for PctNatural. 1 = True,  0 = False.
     - PctNatural 10th percentile = 28.6439%
@@ -24,7 +24,7 @@ The following dataset was compiled by TPL (Trust for Public Lands) using NCLD da
     - P200_PFS 65th percentile = 64.0%
 - NatureDep – ImpOrCrp = 1 AND LowInAndEd = 1.
 
-We added `GEOID10_TRACT` before converting shapefile to csv. 
+We added `GEOID10_TRACT` before converting shapefile to csv.
 
 ## Instructions to recreate
 

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

@@ -1,10 +1,9 @@
 # pylint: disable=unsubscriptable-object
 # pylint: disable=unsupported-assignment-operation
-
 import pandas as pd
 from data_pipeline.config import settings
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)

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

@@ -1,12 +1,11 @@
 import functools
-import pandas as pd
 
+import pandas as pd
 from data_pipeline.config import settings
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
-from data_pipeline.utils import (
-    get_module_logger,
-    unzip_file_from_url,
-)
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

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

@@ -1,11 +1,12 @@
 from pathlib import Path
+
 import geopandas as gpd
 import pandas as pd
-
 from data_pipeline.config import settings
 from data_pipeline.etl.base import ExtractTransformLoad
 from data_pipeline.score import field_names
-from data_pipeline.utils import get_module_logger, unzip_file_from_url
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import unzip_file_from_url
 
 logger = get_module_logger(__name__)
 

data/data-pipeline/data_pipeline/etl/sources/tribal/etl_utils.py

@@ -1,10 +1,8 @@
 from pathlib import Path
 
-from data_pipeline.utils import (
-    get_module_logger,
-    remove_all_from_dir,
-    remove_files_from_dir,
-)
+from data_pipeline.utils import get_module_logger
+from data_pipeline.utils import remove_all_from_dir
+from data_pipeline.utils import remove_files_from_dir
 
 
 logger = get_module_logger(__name__)

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

@@ -1,12 +1,11 @@
 import geopandas as gpd
 import numpy as np
 import pandas as pd
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
-from data_pipeline.etl.sources.geo_utils import (
-    add_tracts_for_geometries,
-    get_tribal_geojson,
-    get_tract_geojson,
-)
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
+from data_pipeline.etl.sources.geo_utils import add_tracts_for_geometries
+from data_pipeline.etl.sources.geo_utils import get_tract_geojson
+from data_pipeline.etl.sources.geo_utils import get_tribal_geojson
 from data_pipeline.score import field_names
 from data_pipeline.utils import get_module_logger
 

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

@@ -1,11 +1,13 @@
 from pathlib import Path
-import geopandas as gpd
-import pandas as pd
-import numpy as np
 
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
-from data_pipeline.utils import get_module_logger, download_file_from_url
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ValidGeoLevel
 from data_pipeline.etl.sources.geo_utils import add_tracts_for_geometries
+from data_pipeline.utils import download_file_from_url
+from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)