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Minor updates to scoring comparison tool (#686)

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* Formatting updates for output XLSX
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Lucas Merrill Brown 2021-09-16 14:06:33 -05:00 • committed by GitHub
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1 changed files with 39 additions and 9 deletions
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48
data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb
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@ -263,21 +263,26 @@
"# (`census_tract_indices`).\n",
"census_block_group_indices = [\n",
" Index(\n",
" method_name=\"Score H\",\n",
" priority_communities_field=\"Score H (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
" ),\n",
" Index(\n",
" method_name=\"Score G\",\n",
" priority_communities_field=\"Score G (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
" ),\n",
" Index(\n",
" method_name=\"Score H\",\n",
" priority_communities_field=\"Score H (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
" ),\n",
" Index(\n",
" method_name=\"NMTC\",\n",
" priority_communities_field=\"NMTC (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
" ),\n",
" Index(\n",
" method_name=\"NMTC modified\",\n",
" priority_communities_field=\"NMTC modified (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
" ),\n",
" Index(\n",
" method_name=\"Score F\",\n",
" priority_communities_field=\"Score F (communities)\",\n",
" other_census_tract_fields_to_keep=[],\n",
@ -383,8 +388,8 @@
" summary_dict[\"division\"] = division_id\n",
" summary_dict[\"Geography name\"] = division_id\n",
"\n",
" summary_dict[\"Total CBGs in geography\"] = len(frame)\n",
" summary_dict[\"Total population in geography\"] = frame[\n",
" total_cbgs_in_geography = len(frame)\n",
" total_population_in_geography = frame[\n",
" CENSUS_BLOCK_GROUP_POPULATION_FIELD\n",
" ].sum()\n",
"\n",
@ -400,14 +405,23 @@
" # Calculate some combinations of other variables.\n",
" summary_dict[f\"{priority_communities_field} (percent CBGs)\"] = (\n",
" summary_dict[f\"{priority_communities_field} (total CBGs)\"]\n",
" / summary_dict[\"Total CBGs in geography\"]\n",
" / total_cbgs_in_geography\n",
" )\n",
"\n",
" summary_dict[f\"{priority_communities_field} (percent population)\"] = (\n",
" summary_dict[f\"{priority_communities_field}{POPULATION_SUFFIX}\"]\n",
" / summary_dict[\"Total population in geography\"]\n",
" / total_population_in_geography\n",
" )\n",
"\n",
" unwanted_keys = [\n",
" f\"{priority_communities_field}{POPULATION_SUFFIX}\",\n",
" f\"{priority_communities_field} (total CBGs)\",\n",
" ]\n",
"\n",
" # Remove unneeded columns:\n",
" for unwanted_key in unwanted_keys:\n",
" del summary_dict[unwanted_key]\n",
"\n",
" df = pd.DataFrame(summary_dict, index=[0])\n",
"\n",
" return df\n",
@ -609,6 +623,22 @@
" # Run the comparison function on the groups.\n",
" comparison_df = grouped_df.mean().reset_index()\n",
"\n",
" criteria_description_field_name = \"Description of criteria\"\n",
" comparison_df[criteria_description_field_name] = comparison_df.apply(\n",
" func=lambda row: f\"CBGs that are {'not' if row[method_a_priority_census_block_groups_field] is False else ''} \" + \n",
" f\"prioritized by {method_a_priority_census_block_groups_field} \" + \n",
" f\"and are {'not' if row[method_b_priority_census_block_groups_field] is False else ''} \" + \n",
" f\"prioritized by {method_b_priority_census_block_groups_field}\",\n",
" axis=1,\n",
" )\n",
"\n",
" # Put criteria description column first.\n",
" new_column_order = [criteria_description_field_name] + [\n",
" col for col in comparison_df.columns if col != criteria_description_field_name\n",
" ]\n",
" \n",
" comparison_df = comparison_df[new_column_order]\n",
"\n",
" # Rename fields to reflect the mean aggregation\n",
" comparison_df.rename(\n",
" mapper={\n",