california-equity-git/initial_view/some_descriptives.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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: descriptives and visualizations of the data\n",
    "##### Author: [dpadams](dpadams@fullerton.edu)\n",
    "##### Date: 2024-09-22\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set working directory\n",
    "import os\n",
    "os.chdir(\"/home/dadams/CSU Fullerton Dropbox/David Adams/Research Projects/California Equity/california_equity_git\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopandas in ./.venv/lib/python3.12/site-packages (1.0.1)\n",
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      "Collecting scikit-learn\n",
      "  Using cached scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
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      "Collecting scipy>=1.6.0 (from scikit-learn)\n",
      "  Using cached scipy-1.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
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      "Requirement already satisfied: python-dateutil>=2.8.2 in ./.venv/lib/python3.12/site-packages (from pandas>=1.4.0->geopandas) (2.9.0.post0)\n",
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      "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.4.0->geopandas) (1.16.0)\n",
      "Using cached scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.9 MB)\n",
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      "Using cached threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
      "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
      "Successfully installed joblib-1.4.2 scikit-learn-1.5.2 scipy-1.14.1 threadpoolctl-3.5.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install geopandas requests scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: no such file or directory: /home/dadams/CSU\n"
     ]
    }
   ],
   "source": [
    "# Install necessary libraries if not already installed\n",
    "import sys\n",
    "!{sys.executable} -m pip install geopandas shapely fiona pyproj matplotlib pandas numpy seaborn psycopg2-binary scikit-learn requests\n",
    "\n",
    "# Importing the installed libraries for spatial and demographic data analysis\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from shapely.geometry import Point\n",
    "import requests\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up some default plot aesthetics using seaborn\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting psycopg2-binary\n",
      "  Using cached psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)\n",
      "Collecting sqlalchemy\n",
      "  Using cached SQLAlchemy-2.0.35-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB)\n",
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      "Using cached psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
      "Using cached SQLAlchemy-2.0.35-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.2 MB)\n",
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      "Using cached typing_extensions-4.12.2-py3-none-any.whl (37 kB)\n",
      "Installing collected packages: typing-extensions, psycopg2-binary, greenlet, sqlalchemy\n",
      "Successfully installed greenlet-3.1.1 psycopg2-binary-2.9.9 sqlalchemy-2.0.35 typing-extensions-4.12.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install psycopg2-binary sqlalchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# connect and authenticate with the database\n",
    "from sqlalchemy import create_engine\n",
    "import psycopg2\n",
    "import pandas as pd\n",
    "\n",
    "# Database connection string\n",
    "db_connection_url = 'postgresql+psycopg2://postgres:MandyLinkToby3@localhost:5432/calif_equity'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create sqlalchemy engine\n",
    "engine = create_engine(db_connection_url)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data from the database\n",
    "sql = 'SELECT * FROM california_climate_investment'\n",
    "climate_investment = pd.read_sql(sql, engine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Tract', 'ZIP', 'County_x', 'ApproxLoc', 'TotPop19', 'CIscore',\n",
       "       'CIscoreP', 'Ozone', 'OzoneP', 'PM2_5',\n",
       "       ...\n",
       "       'Net Density  DUA', 'Applicants  Assisted', 'Invasive Cover 12 Months',\n",
       "       'Invasive Cover 36 Months', 'Project Acreage', 'IS IAE',\n",
       "       'Intermediary Admin Expenses Calc', 'PRIMARY_FUNDING_RECIPIENT_TYPE',\n",
       "       'TRIBAL AFFILIATION', 'PROJECT PARTNERS'],\n",
       "      dtype='object', length=194)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # list columns in the dataframe\n",
    "climate_investment.columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using climate investment data, create a scatter plot of \"Total Project Cost\" vs \"Poverty\"\n",
    "sns.scatterplot(data=climate_investment, x='Total Project Cost', y='Poverty')\n",
    "plt.title('Total Project Cost vs Poverty')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tract</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>County_x</th>\n",
       "      <th>ApproxLoc</th>\n",
       "      <th>TotPop19</th>\n",
       "      <th>CIscore</th>\n",
       "      <th>CIscoreP</th>\n",
       "      <th>Ozone</th>\n",
       "      <th>OzoneP</th>\n",
       "      <th>PM2_5</th>\n",
       "      <th>...</th>\n",
       "      <th>Net Density  DUA</th>\n",
       "      <th>Applicants  Assisted</th>\n",
       "      <th>Invasive Cover 12 Months</th>\n",
       "      <th>Invasive Cover 36 Months</th>\n",
       "      <th>Project Acreage</th>\n",
       "      <th>IS IAE</th>\n",
       "      <th>Intermediary Admin Expenses Calc</th>\n",
       "      <th>PRIMARY_FUNDING_RECIPIENT_TYPE</th>\n",
       "      <th>TRIBAL AFFILIATION</th>\n",
       "      <th>PROJECT PARTNERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4662</th>\n",
       "      <td>6067988300.0</td>\n",
       "      <td>95630</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>Folsom</td>\n",
       "      <td>4860</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.054629</td>\n",
       "      <td>71.661481</td>\n",
       "      <td>7.607284</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4663</th>\n",
       "      <td>6067988300.0</td>\n",
       "      <td>95630</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>Folsom</td>\n",
       "      <td>4860</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.054629</td>\n",
       "      <td>71.661481</td>\n",
       "      <td>7.607284</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7377</th>\n",
       "      <td>6073009901.0</td>\n",
       "      <td>92106</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>767</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.043205</td>\n",
       "      <td>32.146858</td>\n",
       "      <td>10.161979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8943</th>\n",
       "      <td>6073006300.0</td>\n",
       "      <td>92140</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>3760</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.042599</td>\n",
       "      <td>29.894213</td>\n",
       "      <td>10.270812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12609</th>\n",
       "      <td>6073006200.0</td>\n",
       "      <td>92101</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>23</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.042599</td>\n",
       "      <td>29.894213</td>\n",
       "      <td>10.338105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101197</th>\n",
       "      <td>6059021813.0</td>\n",
       "      <td>92807</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Anaheim</td>\n",
       "      <td>4</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.048278</td>\n",
       "      <td>55.382701</td>\n",
       "      <td>12.156761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101198</th>\n",
       "      <td>6059021813.0</td>\n",
       "      <td>92807</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Anaheim</td>\n",
       "      <td>4</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.048278</td>\n",
       "      <td>55.382701</td>\n",
       "      <td>12.156761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101199</th>\n",
       "      <td>6059021813.0</td>\n",
       "      <td>92807</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Anaheim</td>\n",
       "      <td>4</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.048278</td>\n",
       "      <td>55.382701</td>\n",
       "      <td>12.156761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101200</th>\n",
       "      <td>6059021813.0</td>\n",
       "      <td>92807</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Anaheim</td>\n",
       "      <td>4</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.048278</td>\n",
       "      <td>55.382701</td>\n",
       "      <td>12.156761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118597</th>\n",
       "      <td>6029006002.0</td>\n",
       "      <td>93561</td>\n",
       "      <td>Kern</td>\n",
       "      <td>Tehachapi</td>\n",
       "      <td>4228</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.064647</td>\n",
       "      <td>93.627878</td>\n",
       "      <td>7.132276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>409 rows × 194 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Tract    ZIP    County_x  ApproxLoc  TotPop19  CIscore  \\\n",
       "4662    6067988300.0  95630  Sacramento     Folsom      4860   -999.0   \n",
       "4663    6067988300.0  95630  Sacramento     Folsom      4860   -999.0   \n",
       "7377    6073009901.0  92106   San Diego  San Diego       767   -999.0   \n",
       "8943    6073006300.0  92140   San Diego  San Diego      3760   -999.0   \n",
       "12609   6073006200.0  92101   San Diego  San Diego        23   -999.0   \n",
       "...              ...    ...         ...        ...       ...      ...   \n",
       "101197  6059021813.0  92807      Orange    Anaheim         4   -999.0   \n",
       "101198  6059021813.0  92807      Orange    Anaheim         4   -999.0   \n",
       "101199  6059021813.0  92807      Orange    Anaheim         4   -999.0   \n",
       "101200  6059021813.0  92807      Orange    Anaheim         4   -999.0   \n",
       "118597  6029006002.0  93561        Kern  Tehachapi      4228   -999.0   \n",
       "\n",
       "        CIscoreP     Ozone     OzoneP      PM2_5  ...  Net Density  DUA  \\\n",
       "4662      -999.0  0.054629  71.661481   7.607284  ...               0.0   \n",
       "4663      -999.0  0.054629  71.661481   7.607284  ...               0.0   \n",
       "7377      -999.0  0.043205  32.146858  10.161979  ...               0.0   \n",
       "8943      -999.0  0.042599  29.894213  10.270812  ...               0.0   \n",
       "12609     -999.0  0.042599  29.894213  10.338105  ...               0.0   \n",
       "...          ...       ...        ...        ...  ...               ...   \n",
       "101197    -999.0  0.048278  55.382701  12.156761  ...               0.0   \n",
       "101198    -999.0  0.048278  55.382701  12.156761  ...               0.0   \n",
       "101199    -999.0  0.048278  55.382701  12.156761  ...               0.0   \n",
       "101200    -999.0  0.048278  55.382701  12.156761  ...               0.0   \n",
       "118597    -999.0  0.064647  93.627878   7.132276  ...               0.0   \n",
       "\n",
       "        Applicants  Assisted  Invasive Cover 12 Months  \\\n",
       "4662                       0                         0   \n",
       "4663                       0                         0   \n",
       "7377                       0                         0   \n",
       "8943                       0                         0   \n",
       "12609                      0                         0   \n",
       "...                      ...                       ...   \n",
       "101197                     0                         0   \n",
       "101198                     0                         0   \n",
       "101199                     0                         0   \n",
       "101200                     0                         0   \n",
       "118597                     0                         0   \n",
       "\n",
       "        Invasive Cover 36 Months  Project Acreage  IS IAE  \\\n",
       "4662                           0                0   False   \n",
       "4663                           0                0   False   \n",
       "7377                           0                0   False   \n",
       "8943                           0                0   False   \n",
       "12609                          0                0   False   \n",
       "...                          ...              ...     ...   \n",
       "101197                         0                0   False   \n",
       "101198                         0                0   False   \n",
       "101199                         0                0   False   \n",
       "101200                         0                0   False   \n",
       "118597                         0                0   False   \n",
       "\n",
       "        Intermediary Admin Expenses Calc  PRIMARY_FUNDING_RECIPIENT_TYPE  \\\n",
       "4662                                   0                            None   \n",
       "4663                                   0                            None   \n",
       "7377                                   0                            None   \n",
       "8943                                   0                            None   \n",
       "12609                                  0                            None   \n",
       "...                                  ...                             ...   \n",
       "101197                                 0                            None   \n",
       "101198                                 0                            None   \n",
       "101199                                 0                            None   \n",
       "101200                                 0                            None   \n",
       "118597                                 0                            None   \n",
       "\n",
       "        TRIBAL AFFILIATION  PROJECT PARTNERS  \n",
       "4662                  None              None  \n",
       "4663                  None              None  \n",
       "7377                  None              None  \n",
       "8943                  None              None  \n",
       "12609                 None              None  \n",
       "...                    ...               ...  \n",
       "101197                None              None  \n",
       "101198                None              None  \n",
       "101199                None              None  \n",
       "101200                None              None  \n",
       "118597                None              None  \n",
       "\n",
       "[409 rows x 194 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look for negative values in the \"Poverty\" column\n",
    "climate_investment[climate_investment['Poverty'] < 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove rows with negative values in the \"Poverty\" column\n",
    "climate_investment = climate_investment[climate_investment['Poverty'] >= 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using climate investment data, create a scatter plot of \"Total Project Cost\" vs \"Poverty\"\n",
    "sns.scatterplot(data=climate_investment, x='Total Project Cost', y='Poverty')\n",
    "plt.title('Total Project Cost vs Poverty')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: shapely in ./.venv/lib/python3.12/site-packages (2.0.6)\n",
      "Collecting fiona\n",
      "  Using cached fiona-1.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (56 kB)\n",
      "Requirement already satisfied: pyproj in ./.venv/lib/python3.12/site-packages (3.6.1)\n",
      "Requirement already satisfied: numpy<3,>=1.14 in ./.venv/lib/python3.12/site-packages (from shapely) (2.1.1)\n",
      "Collecting attrs>=19.2.0 (from fiona)\n",
      "  Using cached attrs-24.2.0-py3-none-any.whl.metadata (11 kB)\n",
      "Requirement already satisfied: certifi in ./.venv/lib/python3.12/site-packages (from fiona) (2024.8.30)\n",
      "Collecting click~=8.0 (from fiona)\n",
      "  Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)\n",
      "Collecting click-plugins>=1.0 (from fiona)\n",
      "  Using cached click_plugins-1.1.1-py2.py3-none-any.whl.metadata (6.4 kB)\n",
      "Collecting cligj>=0.5 (from fiona)\n",
      "  Using cached cligj-0.7.2-py3-none-any.whl.metadata (5.0 kB)\n",
      "Using cached fiona-1.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.2 MB)\n",
      "Using cached attrs-24.2.0-py3-none-any.whl (63 kB)\n",
      "Using cached click-8.1.7-py3-none-any.whl (97 kB)\n",
      "Using cached click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
      "Using cached cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
      "Installing collected packages: click, attrs, cligj, click-plugins, fiona\n",
      "Successfully installed attrs-24.2.0 click-8.1.7 click-plugins-1.1.1 cligj-0.7.2 fiona-1.10.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install shapely fiona pyproj\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## set up the environment\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_513476/3570709754.py:7: FutureWarning: The provided callable <function sum at 0x73272b515300> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  pivot_table = pd.pivot_table(climate_investment, values='Total Project Cost', index='County_y', aggfunc=np.sum)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Project Cost heatmap by County\n",
    "# read in the data\n",
    "sql = 'SELECT * FROM california_climate_investment'\n",
    "climate_investment = pd.read_sql(sql, engine)\n",
    "\n",
    "# create a pivot table\n",
    "pivot_table = pd.pivot_table(climate_investment, values='Total Project Cost', index='County_y', aggfunc=np.sum)\n",
    "\n",
    "# create a heatmap\n",
    "sns.heatmap(pivot_table, cmap='coolwarm')\n",
    "plt.title('Total Project Cost by County')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Line2D.set() got an unexpected keyword argument 'edgecolor'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[41], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Plot a choropleth map of Total Project Cost by Census Tract\u001b[39;00m\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 7\u001b[0m \u001b[43mclimate_investment\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;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[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcoolwarm\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.8\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m0.8\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Project Cost by Census Tract\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/plotting/_core.py:1030\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1027\u001b[0m             label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m   1028\u001b[0m             data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[0;32m-> 1030\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m         kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[1;32m     70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py:501\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_plot_data()\n\u001b[1;32m    500\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfig\n\u001b[0;32m--> 501\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_table()\n\u001b[1;32m    503\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_legend()\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py:1550\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m   1547\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mark_right_label(label, index\u001b[38;5;241m=\u001b[39mi)\n\u001b[1;32m   1548\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m-> 1550\u001b[0m newlines \u001b[38;5;241m=\u001b[39m \u001b[43mplotf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1551\u001b[0m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1552\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1553\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1554\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1555\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumn_num\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1556\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstacking_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstacking_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1557\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_errorbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_errorbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1558\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1559\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1560\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_append_legend_handles_labels(newlines[\u001b[38;5;241m0\u001b[39m], label)\n\u001b[1;32m   1562\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_ts_plot():\n\u001b[1;32m   1563\u001b[0m     \u001b[38;5;66;03m# reset of xlim should be used for ts data\u001b[39;00m\n\u001b[1;32m   1564\u001b[0m     \u001b[38;5;66;03m# TODO: GH28021, should find a way to change view limit on xaxis\u001b[39;00m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py:1586\u001b[0m, in \u001b[0;36mLinePlot._plot\u001b[0;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[1;32m   1584\u001b[0m     \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_stacker(ax, stacking_id, \u001b[38;5;28mlen\u001b[39m(y))\n\u001b[1;32m   1585\u001b[0m y_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_get_stacked_values(ax, stacking_id, y, kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m-> 1586\u001b[0m lines \u001b[38;5;241m=\u001b[39m \u001b[43mMPLPlot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1587\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_update_stacker(ax, stacking_id, y)\n\u001b[1;32m   1588\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lines\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/converter.py:95\u001b[0m, in \u001b[0;36mregister_pandas_matplotlib_converters.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     94\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m pandas_converters():\n\u001b[0;32m---> 95\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py:981\u001b[0m, in \u001b[0;36mMPLPlot._plot\u001b[0;34m(cls, ax, x, y, style, is_errorbar, **kwds)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    979\u001b[0m     \u001b[38;5;66;03m# prevent style kwarg from going to errorbar, where it is unsupported\u001b[39;00m\n\u001b[1;32m    980\u001b[0m     args \u001b[38;5;241m=\u001b[39m (x, y, style) \u001b[38;5;28;01mif\u001b[39;00m style \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m (x, y)\n\u001b[0;32m--> 981\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py:1779\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1776\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1777\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1778\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1779\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1780\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1781\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/axes/_base.py:296\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    295\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 296\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/axes/_base.py:534\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[1;32m    533\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 534\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m[\u001b[49m\u001b[43ml\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ml\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/axes/_base.py:527\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    523\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    524\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel must be scalar or have the same length as the input \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    525\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata, but found \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(label)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_datasets\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m datasets.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 527\u001b[0m result \u001b[38;5;241m=\u001b[39m (\u001b[43mmake_artist\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncy\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    528\u001b[0m \u001b[43m                      \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    529\u001b[0m           \u001b[38;5;28;01mfor\u001b[39;00m j, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(labels))\n\u001b[1;32m    531\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_kwargs:\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/axes/_base.py:335\u001b[0m, in \u001b[0;36m_process_plot_var_args._makeline\u001b[0;34m(self, axes, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m    333\u001b[0m kw \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs}  \u001b[38;5;66;03m# Don't modify the original kw.\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setdefaults(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getdefaults(kw), kw)\n\u001b[0;32m--> 335\u001b[0m seg \u001b[38;5;241m=\u001b[39m \u001b[43mmlines\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLine2D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m seg, kw\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/lines.py:407\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_markeredgewidth(markeredgewidth)\n\u001b[1;32m    405\u001b[0m \u001b[38;5;66;03m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[1;32m    406\u001b[0m \u001b[38;5;66;03m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[0;32m--> 407\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_internal_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    408\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpickradius \u001b[38;5;241m=\u001b[39m pickradius\n\u001b[1;32m    409\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mind_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/artist.py:1216\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m   1209\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[1;32m   1210\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[1;32m   1212\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[1;32m   1213\u001b[0m \n\u001b[1;32m   1214\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[1;32m   1215\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1216\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[43m_update_props\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1217\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{cls.__name__}\u001b[39;49;00m\u001b[38;5;124;43m.set() got an unexpected keyword argument \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1218\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{prop_name!r}\u001b[39;49;00m\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/matplotlib/artist.py:1190\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m   1188\u001b[0m             func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m   1189\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(func):\n\u001b[0;32m-> 1190\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m   1191\u001b[0m                     errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[1;32m   1192\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[1;32m   1193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n",
      "\u001b[0;31mAttributeError\u001b[0m: Line2D.set() got an unexpected keyword argument 'edgecolor'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot a choropleth map of Total Project Cost by Census Tract\n",
    "plt.figure(figsize=(12, 8))\n",
    "climate_investment.plot(column='Total Project Cost', cmap='coolwarm', legend=True, linewidth=0.8, edgecolor='0.8')\n",
    "plt.title('Total Project Cost by Census Tract')\n",
    "plt.show()\n"
   ]
  }
 ],
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