diff --git a/initial_view/overview_hypotesting_20241031.ipynb b/initial_view/overview_hypotesting_20241031.ipynb index 4fc9dd98..685d4258 100644 --- a/initial_view/overview_hypotesting_20241031.ipynb +++ b/initial_view/overview_hypotesting_20241031.ipynb @@ -2,13 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "## Title: Evaluating Equity and Impact\n", + "# Project: California Equity\n", + "## File: initial_view/overview_hypotesting_20241031.ipynb\n", "### Author: David P. Adams\n", - "\n" + "### Date: 2024-10-31\n" ] }, { @@ -1095,29 +1096,97 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"Column(s) ['dac_funding_ratio'] do not exist\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 22\u001b[0m\n\u001b[1;32m 19\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mej_category\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(classify_for_ej_analysis, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# Analyze environmental justice metrics by category\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m ej_analysis \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mej_category\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTotal Project Cost\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcount\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdac_funding_ratio\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmedian\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mIs Benefit Disadvantaged Communities\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mIs Low Income Communities\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTotal Project GHGReductions\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m 28\u001b[0m \u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnvironmental Justice Analysis by Category:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28mprint\u001b[39m(ej_analysis)\n", - "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/groupby/generic.py:1432\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1429\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m engine_kwargs\n\u001b[1;32m 1431\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m-> 1432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1433\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1434\u001b[0m \u001b[38;5;66;03m# GH #52849\u001b[39;00m\n\u001b[1;32m 1435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mas_index \u001b[38;5;129;01mand\u001b[39;00m is_list_like(func):\n", - "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:190\u001b[0m, in \u001b[0;36mApply.agg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_str()\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(func):\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(func):\n\u001b[1;32m 192\u001b[0m \u001b[38;5;66;03m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_list_like()\n", - "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:423\u001b[0m, in \u001b[0;36mApply.agg_dict_like\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21magg_dict_like\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m 416\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;124;03m Compute aggregation in the case of a dict-like argument.\u001b[39;00m\n\u001b[1;32m 418\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 421\u001b[0m \u001b[38;5;124;03m Result of aggregation.\u001b[39;00m\n\u001b[1;32m 422\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_or_apply_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43magg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:1608\u001b[0m, in \u001b[0;36mGroupByApply.agg_or_apply_dict_like\u001b[0;34m(self, op_name)\u001b[0m\n\u001b[1;32m 1603\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine\u001b[39m\u001b[38;5;124m\"\u001b[39m: engine, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: engine_kwargs})\n\u001b[1;32m 1605\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m com\u001b[38;5;241m.\u001b[39mtemp_setattr(\n\u001b[1;32m 1606\u001b[0m obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas_index\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m, condition\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mhasattr\u001b[39m(obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas_index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1607\u001b[0m ):\n\u001b[0;32m-> 1608\u001b[0m result_index, result_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_dict_like\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1609\u001b[0m \u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 1610\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1611\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results_dict_like(selected_obj, result_index, result_data)\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:462\u001b[0m, in \u001b[0;36mApply.compute_dict_like\u001b[0;34m(self, op_name, selected_obj, selection, kwargs)\u001b[0m\n\u001b[1;32m 460\u001b[0m is_groupby \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(obj, (DataFrameGroupBy, SeriesGroupBy))\n\u001b[1;32m 461\u001b[0m func \u001b[38;5;241m=\u001b[39m cast(AggFuncTypeDict, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc)\n\u001b[0;32m--> 462\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_dictlike_arg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 464\u001b[0m is_non_unique_col \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 465\u001b[0m selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 466\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnunique() \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(selected_obj\u001b[38;5;241m.\u001b[39mcolumns)\n\u001b[1;32m 467\u001b[0m )\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# key only used for output\u001b[39;00m\n", - "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/apply.py:663\u001b[0m, in \u001b[0;36mApply.normalize_dictlike_arg\u001b[0;34m(self, how, obj, func)\u001b[0m\n\u001b[1;32m 661\u001b[0m cols \u001b[38;5;241m=\u001b[39m Index(\u001b[38;5;28mlist\u001b[39m(func\u001b[38;5;241m.\u001b[39mkeys()))\u001b[38;5;241m.\u001b[39mdifference(obj\u001b[38;5;241m.\u001b[39mcolumns, sort\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 662\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cols) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 663\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn(s) \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(cols)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not exist\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 665\u001b[0m aggregator_types \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m 667\u001b[0m \u001b[38;5;66;03m# if we have a dict of any non-scalars\u001b[39;00m\n\u001b[1;32m 668\u001b[0m \u001b[38;5;66;03m# eg. {'A' : ['mean']}, normalize all to\u001b[39;00m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;66;03m# be list-likes\u001b[39;00m\n\u001b[1;32m 670\u001b[0m \u001b[38;5;66;03m# Cannot use func.values() because arg may be a Series\u001b[39;00m\n", - "\u001b[0;31mKeyError\u001b[0m: \"Column(s) ['dac_funding_ratio'] do not exist\"" + "name": "stdout", + "output_type": "stream", + "text": [ + "Environmental Justice Analysis by Category:\n", + " Total Project Cost \\\n", + " count sum mean \n", + "ej_category \n", + "Individual - Solar/Energy 7427 320008647 4.308720e+04 \n", + "Individual - Vehicle 4538 1286418141 2.834769e+05 \n", + "Large Projects 10009 127060954384 1.269467e+07 \n", + "Other 119455 1537986491 1.287503e+04 \n", + "\n", + " dac_funding_ratio \\\n", + " mean median \n", + "ej_category \n", + "Individual - Solar/Energy 0.6504 0.7405 \n", + "Individual - Vehicle 0.1660 0.0245 \n", + "Large Projects 0.2761 0.0000 \n", + "Other 0.4129 0.0000 \n", + "\n", + " Is Benefit Disadvantaged Communities \\\n", + " mean \n", + "ej_category \n", + "Individual - Solar/Energy 0.7824 \n", + "Individual - Vehicle 0.3455 \n", + "Large Projects 0.0664 \n", + "Other 0.3275 \n", + "\n", + " Is Low Income Communities \\\n", + " mean \n", + "ej_category \n", + "Individual - Solar/Energy 0.9338 \n", + "Individual - Vehicle 0.4125 \n", + "Large Projects 0.5602 \n", + "Other 0.5708 \n", + "\n", + " Total Project GHGReductions \n", + " sum \n", + "ej_category \n", + "Individual - Solar/Energy 693914 \n", + "Individual - Vehicle 993992 \n", + "Large Projects 103527784 \n", + "Other 3938822 \n", + "\n", + "Percentage of Funding to Disadvantaged and Low-Income Communities:\n", + " DAC_percentage LowIncome_percentage\n", + "ej_category \n", + "Individual - Solar/Energy 41.46 4.97\n", + "Individual - Vehicle 5.13 2.01\n", + "Large Projects 0.91 1.47\n", + "Other 10.11 16.38\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Impact Analysis:\n", + " Total Project GHGReductions Total Project Cost \\\n", + "ej_category \n", + "Individual - Solar/Energy 93.43 43087.20 \n", + "Individual - Vehicle 219.04 283476.89 \n", + "Large Projects 10343.47 12694670.24 \n", + "Other 32.97 12875.03 \n", + "\n", + " GHG_reduction_per_dollar \n", + "ej_category \n", + "Individual - Solar/Energy 0.0022 \n", + "Individual - Vehicle 0.0008 \n", + "Large Projects 0.0008 \n", + "Other 0.0026 \n" ] } ], "source": [ - "# First, let's create a clearer categorization focused on our question\n", + "# Clearer organization \n", "def classify_for_ej_analysis(row):\n", " program = str(row['Program Name']).lower()\n", " project_type = str(row['Project Type']).lower()\n", @@ -1224,24 +1293,14 @@ "- Highest total GHG reductions (103.5M tons)\n", "\n", "Key Findings:\n", - "1. Your hypothesis about vehicle incentives primarily benefiting wealthier communities is strongly supported by the data\n", + "1. the hypothesis about vehicle incentives primarily benefiting wealthier communities is strongly supported by the data\n", "2. However, individual solar/energy programs are actually very successful at reaching disadvantaged communities\n", - "3. Large projects, while achieving the most total GHG reductions, have mixed equity outcomes - good at reaching low-income communities but not DACs\n", - "\n", - "Policy Implications:\n", - "1. The solar/energy program model could potentially be adapted for vehicle incentives to improve equity\n", - "2. There may be structural barriers preventing DACs from accessing vehicle incentives that need to be addressed\n", - "3. Large projects might benefit from stronger DAC targeting requirements, given their current low DAC participation despite good low-income community reach\n", - "\n", - "Would you like to explore any of these aspects in more detail? We could:\n", - "1. Analyze what makes the solar/energy programs more successful at reaching DACs\n", - "2. Look at specific barriers in the vehicle incentive programs\n", - "3. Examine whether certain types of large projects are better at reaching disadvantaged communities" + "3. Large projects, while achieving the most total GHG reductions, have mixed equity outcomes - good at reaching low-income communities but not DACs\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1414,7 +1473,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_738944/3838036285.py:63: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.\n", + "/tmp/ipykernel_182042/3616794259.py:63: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.\n", " plt.tight_layout()\n" ] }, @@ -1430,7 +1489,7 @@ } ], "source": [ - "# 1. First, let's analyze the overall spatial distribution of investments\n", + "# 1. Analyze the overall spatial distribution of investments\n", "county_analysis = data.groupby('County').agg({\n", " 'Total Project Cost': ['count', 'sum'],\n", " 'Total Project GHGReductions': 'sum',\n", @@ -1442,7 +1501,7 @@ "# Add per capita metrics (we would need to merge with county population data)\n", "# Add GHG reduction per dollar by county\n", "\n", - "# 2. Let's analyze vulnerable communities specifically\n", + "# 2. Analyze vulnerable communities specifically\n", "vulnerability_analysis = pd.DataFrame({\n", " 'Total Projects': [\n", " len(data),\n", @@ -1499,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1787,13 +1846,7 @@ " 'Total Project Cost': 'sum',\n", " 'Is Benefit Disadvantaged Communities': 'mean'\n", "})\n", - "```\n", - "\n", - "Would you like me to focus on developing any of these next steps in more detail? We could:\n", - "1. Create specific visualizations for the paper\n", - "2. Develop detailed policy recommendations\n", - "3. Conduct additional statistical analyses\n", - "4. Create a framework for program evaluation" + "```\n" ] } ],