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    "# Project: California Equity Research\n",
    "### Data: postgis db `calif_equity` with california climate investment and california enviroscreen data\n",
    "#### Goal: Analyze the relationship between climate investment and environmental justice in California\n",
    "#### This notebook: second take \n",
    "##### Author: [dpadams](dpadams@fullerton.edu)\n",
    "##### Date: 2024-11-24"
   ]
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    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'scipy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "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",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scipy'"
     ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Read the data (I see you already have this loaded as 'data')\n",
    "\n",
    "# 1. First, let's create our core analytical metrics\n",
    "def calculate_program_metrics(df):\n",
    "    metrics = df.groupby('Program Name').agg({\n",
    "        'Total Project Cost': ['count', 'sum', 'mean'],\n",
    "        'Total Project GHGReductions': ['sum', 'mean'],\n",
    "        'Total GGRFDisadvantaged Community Funding': ['sum', 'mean'],\n",
    "        'Is Benefit Disadvantaged Communities': 'mean',\n",
    "        'Is Low Income Communities': 'mean'\n",
    "    }).round(2)\n",
    "    \n",
    "    # Add efficiency metrics\n",
    "    metrics['GHG_per_dollar'] = (metrics[('Total Project GHGReductions', 'sum')] / \n",
    "                                metrics[('Total Project Cost', 'sum')]).round(4)\n",
    "    \n",
    "    metrics['DAC_funding_ratio'] = (metrics[('Total GGRFDisadvantaged Community Funding', 'sum')] / \n",
    "                                   metrics[('Total Project Cost', 'sum')]).round(4)\n",
    "    \n",
    "    return metrics\n",
    "\n",
    "program_metrics = calculate_program_metrics(data)\n",
    "\n",
    "# Display top programs by different metrics\n",
    "print(\"\\nTop 5 Programs by Total Investment:\")\n",
    "print(program_metrics.sort_values(('Total Project Cost', 'sum'), ascending=False).head())\n",
    "\n",
    "print(\"\\nTop 5 Programs by GHG Reduction Efficiency:\")\n",
    "print(program_metrics.sort_values('GHG_per_dollar', ascending=False).head())\n",
    "\n",
    "print(\"\\nTop 5 Programs by DAC Funding Ratio:\")\n",
    "print(program_metrics.sort_values('DAC_funding_ratio', ascending=False).head())"
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