105 lines
4.4 KiB
Plaintext
105 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Project: California Equity Research\n",
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"### Data: postgis db `calif_equity` with california climate investment and california enviroscreen data\n",
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"#### Goal: Analyze the relationship between climate investment and environmental justice in California\n",
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"#### This notebook: second take \n",
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"##### Author: [dpadams](dpadams@fullerton.edu)\n",
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"##### Date: 2024-11-24"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'scipy'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m stats\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Read the data (I see you already have this loaded as 'data')\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# 1. First, let's create our core analytical metrics\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_program_metrics\u001b[39m(df):\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scipy'"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from scipy import stats\n",
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"\n",
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"# Read the data (I see you already have this loaded as 'data')\n",
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"\n",
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"# 1. First, let's create our core analytical metrics\n",
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"def calculate_program_metrics(df):\n",
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" metrics = df.groupby('Program Name').agg({\n",
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" 'Total Project Cost': ['count', 'sum', 'mean'],\n",
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" 'Total Project GHGReductions': ['sum', 'mean'],\n",
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" 'Total GGRFDisadvantaged Community Funding': ['sum', 'mean'],\n",
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" 'Is Benefit Disadvantaged Communities': 'mean',\n",
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" 'Is Low Income Communities': 'mean'\n",
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" }).round(2)\n",
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" \n",
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" # Add efficiency metrics\n",
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" metrics['GHG_per_dollar'] = (metrics[('Total Project GHGReductions', 'sum')] / \n",
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" metrics[('Total Project Cost', 'sum')]).round(4)\n",
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" \n",
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" metrics['DAC_funding_ratio'] = (metrics[('Total GGRFDisadvantaged Community Funding', 'sum')] / \n",
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" metrics[('Total Project Cost', 'sum')]).round(4)\n",
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" \n",
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" return metrics\n",
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"\n",
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"program_metrics = calculate_program_metrics(data)\n",
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"\n",
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"# Display top programs by different metrics\n",
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"print(\"\\nTop 5 Programs by Total Investment:\")\n",
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"print(program_metrics.sort_values(('Total Project Cost', 'sum'), ascending=False).head())\n",
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"\n",
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"print(\"\\nTop 5 Programs by GHG Reduction Efficiency:\")\n",
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"print(program_metrics.sort_values('GHG_per_dollar', ascending=False).head())\n",
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"\n",
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"print(\"\\nTop 5 Programs by DAC Funding Ratio:\")\n",
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"print(program_metrics.sort_values('DAC_funding_ratio', ascending=False).head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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