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fixing comp tool bug

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lucasmbrown-usds 2021-09-22 17:49:15 -05:00
parent c42065377b
commit c9f9f02770
2 changed files with 26 additions and 11 deletions
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12
data/data-pipeline/data_pipeline/etl/score/etl_score.py
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@ -106,22 +106,22 @@ class ScoreETL(ExtractTransformLoad):
"EJSCREEN Areas of Concern, National, 95th percentile (communities)" "EJSCREEN Areas of Concern, National, 95th percentile (communities)"
) )
self.EJSCREEN_AREAS_OF_CONCERN_STATE_70TH_PERCENTILE_COMMUNITIES_FIELD_NAME = ( self.EJSCREEN_AREAS_OF_CONCERN_STATE_70TH_PERCENTILE_COMMUNITIES_FIELD_NAME = (
"EJSCREEN Areas of Concern, National, 70th percentile (communities)" "EJSCREEN Areas of Concern, State, 70th percentile (communities)"
) )
self.EJSCREEN_AREAS_OF_CONCERN_STATE_75TH_PERCENTILE_COMMUNITIES_FIELD_NAME = ( self.EJSCREEN_AREAS_OF_CONCERN_STATE_75TH_PERCENTILE_COMMUNITIES_FIELD_NAME = (
"EJSCREEN Areas of Concern, National, 75th percentile (communities)" "EJSCREEN Areas of Concern, State, 75th percentile (communities)"
) )
self.EJSCREEN_AREAS_OF_CONCERN_STATE_80TH_PERCENTILE_COMMUNITIES_FIELD_NAME = ( self.EJSCREEN_AREAS_OF_CONCERN_STATE_80TH_PERCENTILE_COMMUNITIES_FIELD_NAME = (
"EJSCREEN Areas of Concern, National, 80th percentile (communities)" "EJSCREEN Areas of Concern, State, 80th percentile (communities)"
) )
self.EJSCREEN_AREAS_OF_CONCERN_STATE_85TH_PERCENTILE_COMMUNITIES_FIELD_NAME = ( self.EJSCREEN_AREAS_OF_CONCERN_STATE_85TH_PERCENTILE_COMMUNITIES_FIELD_NAME = (
"EJSCREEN Areas of Concern, National, 85th percentile (communities)" "EJSCREEN Areas of Concern, State, 85th percentile (communities)"
) )
self.EJSCREEN_AREAS_OF_CONCERN_STATE_90TH_PERCENTILE_COMMUNITIES_FIELD_NAME = ( self.EJSCREEN_AREAS_OF_CONCERN_STATE_90TH_PERCENTILE_COMMUNITIES_FIELD_NAME = (
"EJSCREEN Areas of Concern, National, 90th percentile (communities)" "EJSCREEN Areas of Concern, State, 90th percentile (communities)"
) )
self.EJSCREEN_AREAS_OF_CONCERN_STATE_95TH_PERCENTILE_COMMUNITIES_FIELD_NAME = ( self.EJSCREEN_AREAS_OF_CONCERN_STATE_95TH_PERCENTILE_COMMUNITIES_FIELD_NAME = (
"EJSCREEN Areas of Concern, National, 95th percentile (communities)" "EJSCREEN Areas of Concern, State, 95th percentile (communities)"
) )
# dataframes # dataframes

25
data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb
View file

@ -337,10 +337,20 @@
" other_census_tract_fields_to_keep=[],\n", " other_census_tract_fields_to_keep=[],\n",
" ),\n", " ),\n",
" Index(\n", " Index(\n",
" method_name=\"EJSCREEN Areas of Concern, National, 80th percentile (communities)\",\n", " method_name=\"EJSCREEN Areas of Concern, National, 80th percentile\",\n",
" priority_communities_field=\"EJSCREEN Areas of Concern, National, 80th percentile (communities)\",\n", " priority_communities_field=\"EJSCREEN Areas of Concern, National, 80th percentile (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n", " other_census_tract_fields_to_keep=[],\n",
" ),\n", " ),\n",
" Index(\n",
" method_name=\"EJSCREEN Areas of Concern, National, 90th percentile\",\n",
" priority_communities_field=\"EJSCREEN Areas of Concern, National, 90th percentile (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
" ),\n",
" Index(\n",
" method_name=\"EJSCREEN Areas of Concern, National, 95th percentile\",\n",
" priority_communities_field=\"EJSCREEN Areas of Concern, National, 95th percentile (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
" ),\n",
"]\n", "]\n",
"\n", "\n",
"census_tract_indices = [\n", "census_tract_indices = [\n",
@ -376,9 +386,6 @@
"\n", "\n",
" # Ensure each field is boolean.\n", " # Ensure each field is boolean.\n",
" for priority_communities_field in priority_communities_fields:\n", " for priority_communities_field in priority_communities_fields:\n",
" if df[priority_communities_field].dtype != bool:\n",
" print(f\"Converting {priority_communities_field} to boolean.\")\n",
"\n",
" # Calculate the population included as priority communities per CBG. Will either be 0 or the population.\n", " # Calculate the population included as priority communities per CBG. Will either be 0 or the population.\n",
" df[f\"{priority_communities_field}{POPULATION_SUFFIX}\"] = (\n", " df[f\"{priority_communities_field}{POPULATION_SUFFIX}\"] = (\n",
" df[priority_communities_field]\n", " df[priority_communities_field]\n",
@ -608,6 +615,14 @@
" for index in census_block_group_indices + census_tract_indices\n", " for index in census_block_group_indices + census_tract_indices\n",
"]\n", "]\n",
"\n", "\n",
"# Convert all indices to boolean\n",
"for field_to_analyze in fields_to_analyze: \n",
" if \"Areas of Concern\" in field_to_analyze:\n",
" print(f\"Converting {field_to_analyze} to boolean.\")\n",
"\n",
" merged_df[field_to_analyze] = merged_df[field_to_analyze].fillna(value=0)\n",
" merged_df[field_to_analyze] = merged_df[field_to_analyze].astype(bool)\n",
"\n",
"state_fips_codes = get_state_information(DATA_DIR)\n", "state_fips_codes = get_state_information(DATA_DIR)\n",
"\n", "\n",
"merged_with_state_information_df = merged_df.merge(\n", "merged_with_state_information_df = merged_df.merge(\n",
@ -1499,7 +1514,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.12" "version": "3.9.6"
} }
}, },
"nbformat": 4, "nbformat": 4,