856 lines
207 KiB
Plaintext
856 lines
207 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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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: descriptives and visualizations of the data\n",
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"##### Author: [dpadams](dpadams@fullerton.edu)\n",
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"##### Date: 2024-09-22\n"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# set working directory\n",
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"import os\n",
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"os.chdir('/home/dadams/CSU Fullerton Dropbox/David Adams/Research Projects/California Equity/california_equity_git')"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: geopandas in ./.venv/lib/python3.12/site-packages (1.0.1)\n",
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"Requirement already satisfied: requests in ./.venv/lib/python3.12/site-packages (2.32.3)\n",
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"Collecting scikit-learn\n",
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" 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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"Requirement already satisfied: numpy>=1.22 in ./.venv/lib/python3.12/site-packages (from geopandas) (2.1.1)\n",
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"Requirement already satisfied: pyogrio>=0.7.2 in ./.venv/lib/python3.12/site-packages (from geopandas) (0.10.0)\n",
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"Requirement already satisfied: packaging in ./.venv/lib/python3.12/site-packages (from geopandas) (24.1)\n",
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"Requirement already satisfied: pandas>=1.4.0 in ./.venv/lib/python3.12/site-packages (from geopandas) (2.2.3)\n",
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"Requirement already satisfied: pyproj>=3.3.0 in ./.venv/lib/python3.12/site-packages (from geopandas) (3.6.1)\n",
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"Requirement already satisfied: shapely>=2.0.0 in ./.venv/lib/python3.12/site-packages (from geopandas) (2.0.6)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in ./.venv/lib/python3.12/site-packages (from requests) (3.3.2)\n",
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"Requirement already satisfied: idna<4,>=2.5 in ./.venv/lib/python3.12/site-packages (from requests) (3.10)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in ./.venv/lib/python3.12/site-packages (from requests) (2.2.3)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in ./.venv/lib/python3.12/site-packages (from requests) (2024.8.30)\n",
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"Collecting scipy>=1.6.0 (from scikit-learn)\n",
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" 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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"Collecting joblib>=1.2.0 (from scikit-learn)\n",
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" Using cached joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n",
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"Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
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" Using cached threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 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: pytz>=2020.1 in ./.venv/lib/python3.12/site-packages (from pandas>=1.4.0->geopandas) (2024.2)\n",
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"Requirement already satisfied: tzdata>=2022.7 in ./.venv/lib/python3.12/site-packages (from pandas>=1.4.0->geopandas) (2024.2)\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",
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"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 joblib-1.4.2-py3-none-any.whl (301 kB)\n",
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"Using cached scipy-1.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (40.8 MB)\n",
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"Using cached threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
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"Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
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"Successfully installed joblib-1.4.2 scikit-learn-1.5.2 scipy-1.14.1 threadpoolctl-3.5.0\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install geopandas requests scikit-learn"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"zsh:1: no such file or directory: /home/dadams/CSU\n"
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]
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},
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'sklearn'",
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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[7], line 13\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mshapely\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeometry\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Point\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
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]
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}
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],
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"source": [
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"# Install necessary libraries if not already installed\n",
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"import sys\n",
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"!{sys.executable} -m pip install geopandas shapely fiona pyproj matplotlib pandas numpy seaborn psycopg2-binary scikit-learn requests\n",
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"\n",
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"# Importing the installed libraries for spatial and demographic data analysis\n",
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"import geopandas as gpd\n",
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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 shapely.geometry import Point\n",
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"import requests\n",
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"from sklearn.preprocessing import StandardScaler"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set up some default plot aesthetics using seaborn\n",
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"sns.set(style=\"whitegrid\")"
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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": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: psycopg2-binary in ./.venv/lib/python3.12/site-packages (2.9.9)\n",
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"Requirement already satisfied: sqlalchemy in ./.venv/lib/python3.12/site-packages (2.0.35)\n",
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"Requirement already satisfied: typing-extensions>=4.6.0 in ./.venv/lib/python3.12/site-packages (from sqlalchemy) (4.12.2)\n",
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"Requirement already satisfied: greenlet!=0.4.17 in ./.venv/lib/python3.12/site-packages (from sqlalchemy) (3.1.1)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install psycopg2-binary sqlalchemy"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# connect and authenticate with the database\n",
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"from sqlalchemy import create_engine\n",
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"import psycopg2\n",
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"import pandas as pd\n",
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"\n",
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"# Database connection string\n",
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"db_connection_url = 'postgresql+psycopg2://postgres:MandyLinkToby3@localhost:5432/calif_equity'\n"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create sqlalchemy engine\n",
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"engine = create_engine(db_connection_url)\n"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Read in the data from the database\n",
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"sql = 'SELECT * FROM california_climate_investment'\n",
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"climate_investment = pd.read_sql(sql, engine)\n"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['Tract', 'ZIP', 'County_x', 'ApproxLoc', 'TotPop19', 'CIscore',\n",
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" 'CIscoreP', 'Ozone', 'OzoneP', 'PM2_5',\n",
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" ...\n",
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" 'Net Density DUA', 'Applicants Assisted', 'Invasive Cover 12 Months',\n",
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" 'Invasive Cover 36 Months', 'Project Acreage', 'IS IAE',\n",
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" 'Intermediary Admin Expenses Calc', 'PRIMARY_FUNDING_RECIPIENT_TYPE',\n",
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" 'TRIBAL AFFILIATION', 'PROJECT PARTNERS'],\n",
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" dtype='object', length=194)"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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" # list columns in the dataframe\n",
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"climate_investment.columns\n",
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"\n"
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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": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"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",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"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>222</th>\n",
|
||
" <td>6083980000.0</td>\n",
|
||
" <td>93117</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.039755</td>\n",
|
||
" <td>20.846297</td>\n",
|
||
" <td>8.035979</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>223</th>\n",
|
||
" <td>6083980000.0</td>\n",
|
||
" <td>93117</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.039755</td>\n",
|
||
" <td>20.846297</td>\n",
|
||
" <td>8.035979</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>224</th>\n",
|
||
" <td>6083980000.0</td>\n",
|
||
" <td>93117</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.039755</td>\n",
|
||
" <td>20.846297</td>\n",
|
||
" <td>8.035979</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>225</th>\n",
|
||
" <td>6083980000.0</td>\n",
|
||
" <td>93117</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.039755</td>\n",
|
||
" <td>20.846297</td>\n",
|
||
" <td>8.035979</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>226</th>\n",
|
||
" <td>6083980000.0</td>\n",
|
||
" <td>93117</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>Santa Barbara</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.039755</td>\n",
|
||
" <td>20.846297</td>\n",
|
||
" <td>8.035979</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>116353</th>\n",
|
||
" <td>6037910811.0</td>\n",
|
||
" <td>93510</td>\n",
|
||
" <td>Los Angeles</td>\n",
|
||
" <td>Unincorporated Los Angeles County area</td>\n",
|
||
" <td>179</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.066049</td>\n",
|
||
" <td>95.308027</td>\n",
|
||
" <td>9.386662</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>118390</th>\n",
|
||
" <td>6037901003.0</td>\n",
|
||
" <td>93536</td>\n",
|
||
" <td>Los Angeles</td>\n",
|
||
" <td>Lancaster</td>\n",
|
||
" <td>4895</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.062365</td>\n",
|
||
" <td>88.699440</td>\n",
|
||
" <td>7.135657</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>118983</th>\n",
|
||
" <td>6037401901.0</td>\n",
|
||
" <td>91711</td>\n",
|
||
" <td>Los Angeles</td>\n",
|
||
" <td>Claremont</td>\n",
|
||
" <td>3945</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.061338</td>\n",
|
||
" <td>84.579963</td>\n",
|
||
" <td>13.145812</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>118984</th>\n",
|
||
" <td>6037401901.0</td>\n",
|
||
" <td>91711</td>\n",
|
||
" <td>Los Angeles</td>\n",
|
||
" <td>Claremont</td>\n",
|
||
" <td>3945</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.061338</td>\n",
|
||
" <td>84.579963</td>\n",
|
||
" <td>13.145812</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>118985</th>\n",
|
||
" <td>6037401901.0</td>\n",
|
||
" <td>91711</td>\n",
|
||
" <td>Los Angeles</td>\n",
|
||
" <td>Claremont</td>\n",
|
||
" <td>3945</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>-999.0</td>\n",
|
||
" <td>0.061338</td>\n",
|
||
" <td>84.579963</td>\n",
|
||
" <td>13.145812</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 \\\n",
|
||
"222 6083980000.0 93117 Santa Barbara \n",
|
||
"223 6083980000.0 93117 Santa Barbara \n",
|
||
"224 6083980000.0 93117 Santa Barbara \n",
|
||
"225 6083980000.0 93117 Santa Barbara \n",
|
||
"226 6083980000.0 93117 Santa Barbara \n",
|
||
"... ... ... ... \n",
|
||
"116353 6037910811.0 93510 Los Angeles \n",
|
||
"118390 6037901003.0 93536 Los Angeles \n",
|
||
"118983 6037401901.0 91711 Los Angeles \n",
|
||
"118984 6037401901.0 91711 Los Angeles \n",
|
||
"118985 6037401901.0 91711 Los Angeles \n",
|
||
"\n",
|
||
" ApproxLoc TotPop19 CIscore CIscoreP \\\n",
|
||
"222 Santa Barbara 0 -999.0 -999.0 \n",
|
||
"223 Santa Barbara 0 -999.0 -999.0 \n",
|
||
"224 Santa Barbara 0 -999.0 -999.0 \n",
|
||
"225 Santa Barbara 0 -999.0 -999.0 \n",
|
||
"226 Santa Barbara 0 -999.0 -999.0 \n",
|
||
"... ... ... ... ... \n",
|
||
"116353 Unincorporated Los Angeles County area 179 -999.0 -999.0 \n",
|
||
"118390 Lancaster 4895 -999.0 -999.0 \n",
|
||
"118983 Claremont 3945 -999.0 -999.0 \n",
|
||
"118984 Claremont 3945 -999.0 -999.0 \n",
|
||
"118985 Claremont 3945 -999.0 -999.0 \n",
|
||
"\n",
|
||
" Ozone OzoneP PM2_5 ... Net Density DUA \\\n",
|
||
"222 0.039755 20.846297 8.035979 ... 0.0 \n",
|
||
"223 0.039755 20.846297 8.035979 ... 0.0 \n",
|
||
"224 0.039755 20.846297 8.035979 ... 0.0 \n",
|
||
"225 0.039755 20.846297 8.035979 ... 0.0 \n",
|
||
"226 0.039755 20.846297 8.035979 ... 0.0 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"116353 0.066049 95.308027 9.386662 ... 0.0 \n",
|
||
"118390 0.062365 88.699440 7.135657 ... 0.0 \n",
|
||
"118983 0.061338 84.579963 13.145812 ... 0.0 \n",
|
||
"118984 0.061338 84.579963 13.145812 ... 0.0 \n",
|
||
"118985 0.061338 84.579963 13.145812 ... 0.0 \n",
|
||
"\n",
|
||
" Applicants Assisted Invasive Cover 12 Months \\\n",
|
||
"222 0 0 \n",
|
||
"223 0 0 \n",
|
||
"224 0 0 \n",
|
||
"225 0 0 \n",
|
||
"226 0 0 \n",
|
||
"... ... ... \n",
|
||
"116353 0 0 \n",
|
||
"118390 0 0 \n",
|
||
"118983 0 0 \n",
|
||
"118984 0 0 \n",
|
||
"118985 0 0 \n",
|
||
"\n",
|
||
" Invasive Cover 36 Months Project Acreage IS IAE \\\n",
|
||
"222 0 0 False \n",
|
||
"223 0 0 False \n",
|
||
"224 0 0 False \n",
|
||
"225 0 0 False \n",
|
||
"226 0 0 False \n",
|
||
"... ... ... ... \n",
|
||
"116353 0 0 False \n",
|
||
"118390 0 0 False \n",
|
||
"118983 0 0 False \n",
|
||
"118984 0 0 False \n",
|
||
"118985 0 0 False \n",
|
||
"\n",
|
||
" Intermediary Admin Expenses Calc PRIMARY_FUNDING_RECIPIENT_TYPE \\\n",
|
||
"222 0 None \n",
|
||
"223 0 None \n",
|
||
"224 0 None \n",
|
||
"225 0 None \n",
|
||
"226 0 None \n",
|
||
"... ... ... \n",
|
||
"116353 0 None \n",
|
||
"118390 0 None \n",
|
||
"118983 0 None \n",
|
||
"118984 0 None \n",
|
||
"118985 0 None \n",
|
||
"\n",
|
||
" TRIBAL AFFILIATION PROJECT PARTNERS \n",
|
||
"222 None None \n",
|
||
"223 None None \n",
|
||
"224 None None \n",
|
||
"225 None None \n",
|
||
"226 None None \n",
|
||
"... ... ... \n",
|
||
"116353 None None \n",
|
||
"118390 None None \n",
|
||
"118983 None None \n",
|
||
"118984 None None \n",
|
||
"118985 None None \n",
|
||
"\n",
|
||
"[409 rows x 194 columns]"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"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": 20,
|
||
"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": 21,
|
||
"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": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: shapely in ./.venv/lib/python3.12/site-packages (2.0.6)\n",
|
||
"Requirement already satisfied: fiona in ./.venv/lib/python3.12/site-packages (1.10.1)\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",
|
||
"Requirement already satisfied: attrs>=19.2.0 in ./.venv/lib/python3.12/site-packages (from fiona) (24.2.0)\n",
|
||
"Requirement already satisfied: certifi in ./.venv/lib/python3.12/site-packages (from fiona) (2024.8.30)\n",
|
||
"Requirement already satisfied: click~=8.0 in ./.venv/lib/python3.12/site-packages (from fiona) (8.1.7)\n",
|
||
"Requirement already satisfied: click-plugins>=1.0 in ./.venv/lib/python3.12/site-packages (from fiona) (1.1.1)\n",
|
||
"Requirement already satisfied: cligj>=0.5 in ./.venv/lib/python3.12/site-packages (from fiona) (0.7.2)\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": 32,
|
||
"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": 39,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_477537/3570709754.py:7: FutureWarning: The provided callable <function sum at 0x773adbf88400> 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": {
|
||
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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",
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||
"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",
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||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
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"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",
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||
"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",
|
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"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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",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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||
},
|
||
"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"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "spatial_modeling",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|