Analyzing Colorado Cannabis Sales Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Colorado Cannabis Sales Data 🌳 🚬\n",
    "\n",
    "Every month, the Colorado Department of Revenue [publishes](https://www.colorado.gov/pacific/revenue/colorado-marijuana-sales-reports) county-level data on retail and medical cannabis sales in the state.\n",
    "\n",
    "This notebook has a few things you might do in the course of gathering and analyzing data, maybe in a newsroom. It contains, or links to, code that accomplishes the following:\n",
    "- Downloads a CSV version of [the state's Google Sheet](https://docs.google.com/spreadsheets/d/1br_cwfHy24d2R2bcXacb2KarOIBKGrbR/edit#gid=1659782909) and flattens the data a little bit (`download.py`)\n",
    "- Loads this parsed data into a `pandas` dataframe for analysis\n",
    "- Downloads the Census TIGER/LINE shapefile of Colorado counties into the `raw-data` directory\n",
    "- Loads this shapefile into `geopandas`\n",
    "- Hits the Census Bureau API to get the most recent population estimates for Colorado counties\n",
    "- Draws a choropleth map with per-capita sales data by county for the most recent year of retail sales\n",
    "\n",
    "### Prerequisites\n",
    "\n",
    "If you don't have one already, [grab an API key from the Census Bureau](https://www.census.gov/data/developers/guidance/api-user-guide.Query_Components.html) and save it as an [environment variable](https://en.wikipedia.org/wiki/Environment_variable) called `CENSUS_API_KEY`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "from datetime import date\n",
    "import csv\n",
    "import zipfile\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from matplotlib.ticker import FuncFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display plots inline\n",
    "%matplotlib inline\n",
    "\n",
    "# display floats with thousand-separator commas and no decimal points\n",
    "pd.options.display.float_format = '{:,.0f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the spreadsheets\n",
    "\n",
    "Running the next cell fires a separate script, `download.py`, that grabs the latest data from the state's Google Sheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the CSV into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_file = 'co-cannabis-sales.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    csv_file,\n",
    "    dtype={'county_fips': str}\n",
    ").fillna(0).sort_values(['monthyear', 'county_name', 'sales_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monthyear</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>amount</th>\n",
       "      <th>sales_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5613</th>\n",
       "      <td>201401</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>08001</td>\n",
       "      <td>ADAMS</td>\n",
       "      <td>683098</td>\n",
       "      <td>medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5614</th>\n",
       "      <td>201401</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>08005</td>\n",
       "      <td>ARAPAHOE</td>\n",
       "      <td>1188232</td>\n",
       "      <td>medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5615</th>\n",
       "      <td>201401</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>08013</td>\n",
       "      <td>BOULDER</td>\n",
       "      <td>3417873</td>\n",
       "      <td>medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5616</th>\n",
       "      <td>201401</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>08015</td>\n",
       "      <td>CHAFFEE</td>\n",
       "      <td>83714</td>\n",
       "      <td>medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5617</th>\n",
       "      <td>201401</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>08019</td>\n",
       "      <td>CLEAR CREEK</td>\n",
       "      <td>143736</td>\n",
       "      <td>medical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      monthyear  month  year county_fips  county_name   amount sales_type\n",
       "5613     201401      1  2014       08001        ADAMS   683098    medical\n",
       "5614     201401      1  2014       08005     ARAPAHOE  1188232    medical\n",
       "5615     201401      1  2014       08013      BOULDER  3417873    medical\n",
       "5616     201401      1  2014       08015      CHAFFEE    83714    medical\n",
       "5617     201401      1  2014       08019  CLEAR CREEK   143736    medical"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check month values\n",
    "sorted(df.month.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check year values\n",
    "sorted(df.year.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ADAMS',\n",
       " 'ARAPAHOE',\n",
       " 'ARCHULETA',\n",
       " 'BENT',\n",
       " 'BOULDER',\n",
       " 'BROOMFIELD',\n",
       " 'CHAFFEE',\n",
       " 'CLEAR CREEK',\n",
       " 'CONEJOS',\n",
       " 'COSTILLA',\n",
       " 'CROWLEY',\n",
       " 'DELTA',\n",
       " 'DENVER',\n",
       " 'EAGLE',\n",
       " 'EL PASO',\n",
       " 'FREMONT',\n",
       " 'GARFIELD',\n",
       " 'GILPIN',\n",
       " 'GRAND',\n",
       " 'GUNNISON',\n",
       " 'HUERFANO',\n",
       " 'JEFFERSON',\n",
       " 'LA PLATA',\n",
       " 'LAKE',\n",
       " 'LARIMER',\n",
       " 'LAS ANIMAS',\n",
       " 'MESA',\n",
       " 'MOFFAT',\n",
       " 'MONTEZUMA',\n",
       " 'MORGAN',\n",
       " 'OTERO',\n",
       " 'OURAY',\n",
       " 'PARK',\n",
       " 'PITKIN',\n",
       " 'PUEBLO',\n",
       " 'ROUTT',\n",
       " 'SAGUACHE',\n",
       " 'SAN JUAN',\n",
       " 'SAN MIGUEL',\n",
       " 'SEDGWICK',\n",
       " 'SUM OF NR COUNTIES',\n",
       " 'SUMMIT',\n",
       " 'WELD']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check county values\n",
    "sorted(df.county_name.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['medical', 'retail'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check type values\n",
    "df.sales_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a date column to group by later when we plot\n",
    "df['date'] = df.apply(lambda x: date(x.year, x.month, 1), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create filtered dataframes for medical and retail data\n",
    "retail = df[df.sales_type == 'retail']\n",
    "med = df[df.sales_type == 'medical']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do some basic analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16209334865"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum\n",
    "df.amount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>683523739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>995591255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1307203473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1507702219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1545691081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1747990628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2191091679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2228994553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>1768688837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>1529324554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>703532847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          amount\n",
       "year            \n",
       "2014   683523739\n",
       "2015   995591255\n",
       "2016  1307203473\n",
       "2017  1507702219\n",
       "2018  1545691081\n",
       "2019  1747990628\n",
       "2020  2191091679\n",
       "2021  2228994553\n",
       "2022  1768688837\n",
       "2023  1529324554\n",
       "2024   703532847"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum by year\n",
    "df[['year', 'amount']].groupby('year').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lj/z6tr_fnx73v_v3nct2cw3dd80000gn/T/ipykernel_74153/3000503992.py:2: FutureWarning: The provided callable <built-in function sum> 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",
      "  pd.pivot_table(df,\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADAMS</th>\n",
       "      <td>8,531,797</td>\n",
       "      <td>24,826,654</td>\n",
       "      <td>49,810,527</td>\n",
       "      <td>73,319,607</td>\n",
       "      <td>89,595,114</td>\n",
       "      <td>115,030,166</td>\n",
       "      <td>155,274,744</td>\n",
       "      <td>159,915,797</td>\n",
       "      <td>139,731,225</td>\n",
       "      <td>119,542,135</td>\n",
       "      <td>51,777,262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARAPAHOE</th>\n",
       "      <td>19,083,212</td>\n",
       "      <td>68,533,185</td>\n",
       "      <td>113,890,135</td>\n",
       "      <td>124,313,953</td>\n",
       "      <td>129,801,179</td>\n",
       "      <td>153,255,179</td>\n",
       "      <td>188,314,099</td>\n",
       "      <td>199,947,743</td>\n",
       "      <td>181,646,516</td>\n",
       "      <td>160,475,693</td>\n",
       "      <td>71,239,956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARCHULETA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6,397,396</td>\n",
       "      <td>8,848,341</td>\n",
       "      <td>10,248,743</td>\n",
       "      <td>15,536,321</td>\n",
       "      <td>16,992,084</td>\n",
       "      <td>12,398,961</td>\n",
       "      <td>9,389,966</td>\n",
       "      <td>4,030,701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BENT</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>504,288</td>\n",
       "      <td>5,526,743</td>\n",
       "      <td>5,946,691</td>\n",
       "      <td>4,275,606</td>\n",
       "      <td>1,781,995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOULDER</th>\n",
       "      <td>62,935,522</td>\n",
       "      <td>82,465,474</td>\n",
       "      <td>95,408,126</td>\n",
       "      <td>99,923,326</td>\n",
       "      <td>108,277,854</td>\n",
       "      <td>116,859,122</td>\n",
       "      <td>138,333,017</td>\n",
       "      <td>143,139,122</td>\n",
       "      <td>123,282,951</td>\n",
       "      <td>102,124,942</td>\n",
       "      <td>45,976,643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BROOMFIELD</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7,837,483</td>\n",
       "      <td>7,578,159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAFFEE</th>\n",
       "      <td>438,160</td>\n",
       "      <td>2,067,869</td>\n",
       "      <td>3,492,699</td>\n",
       "      <td>5,422,335</td>\n",
       "      <td>5,360,100</td>\n",
       "      <td>4,330,415</td>\n",
       "      <td>6,830,041</td>\n",
       "      <td>4,353,188</td>\n",
       "      <td>6,231,401</td>\n",
       "      <td>8,188,760</td>\n",
       "      <td>3,085,707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLEAR CREEK</th>\n",
       "      <td>6,095,765</td>\n",
       "      <td>6,435,080</td>\n",
       "      <td>7,414,079</td>\n",
       "      <td>7,262,477</td>\n",
       "      <td>7,425,243</td>\n",
       "      <td>7,950,847</td>\n",
       "      <td>8,852,029</td>\n",
       "      <td>10,669,939</td>\n",
       "      <td>9,775,008</td>\n",
       "      <td>8,229,908</td>\n",
       "      <td>3,428,940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONEJOS</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3,522,183</td>\n",
       "      <td>5,426,288</td>\n",
       "      <td>6,033,662</td>\n",
       "      <td>1,314,361</td>\n",
       "      <td>0</td>\n",
       "      <td>10,348,829</td>\n",
       "      <td>6,785,181</td>\n",
       "      <td>4,491,591</td>\n",
       "      <td>1,887,907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COSTILLA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2,836,421</td>\n",
       "      <td>3,874,954</td>\n",
       "      <td>4,172,958</td>\n",
       "      <td>4,730,915</td>\n",
       "      <td>6,844,327</td>\n",
       "      <td>7,092,827</td>\n",
       "      <td>4,300,334</td>\n",
       "      <td>2,760,622</td>\n",
       "      <td>1,116,876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROWLEY</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>898,993</td>\n",
       "      <td>217,342</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELTA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3,412,366</td>\n",
       "      <td>4,906,033</td>\n",
       "      <td>1,378,592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DENVER</th>\n",
       "      <td>325,930,878</td>\n",
       "      <td>407,204,194</td>\n",
       "      <td>501,611,664</td>\n",
       "      <td>577,537,470</td>\n",
       "      <td>536,631,891</td>\n",
       "      <td>579,676,047</td>\n",
       "      <td>709,477,874</td>\n",
       "      <td>682,125,808</td>\n",
       "      <td>475,484,281</td>\n",
       "      <td>398,982,406</td>\n",
       "      <td>183,471,135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAGLE</th>\n",
       "      <td>4,494,510</td>\n",
       "      <td>11,685,385</td>\n",
       "      <td>14,839,943</td>\n",
       "      <td>15,510,137</td>\n",
       "      <td>18,083,147</td>\n",
       "      <td>19,144,708</td>\n",
       "      <td>22,612,151</td>\n",
       "      <td>23,726,784</td>\n",
       "      <td>21,320,338</td>\n",
       "      <td>19,057,385</td>\n",
       "      <td>9,124,328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL PASO</th>\n",
       "      <td>67,026,306</td>\n",
       "      <td>86,100,007</td>\n",
       "      <td>101,137,059</td>\n",
       "      <td>102,115,436</td>\n",
       "      <td>92,786,795</td>\n",
       "      <td>104,698,718</td>\n",
       "      <td>143,725,552</td>\n",
       "      <td>126,378,365</td>\n",
       "      <td>81,805,544</td>\n",
       "      <td>98,033,319</td>\n",
       "      <td>57,336,531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREMONT</th>\n",
       "      <td>2,530,928</td>\n",
       "      <td>2,392,649</td>\n",
       "      <td>2,077,835</td>\n",
       "      <td>1,663,232</td>\n",
       "      <td>1,090,121</td>\n",
       "      <td>463,876</td>\n",
       "      <td>223,258</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARFIELD</th>\n",
       "      <td>8,744,520</td>\n",
       "      <td>14,035,557</td>\n",
       "      <td>20,923,125</td>\n",
       "      <td>25,399,951</td>\n",
       "      <td>22,282,865</td>\n",
       "      <td>25,664,808</td>\n",
       "      <td>29,092,496</td>\n",
       "      <td>29,524,888</td>\n",
       "      <td>25,106,708</td>\n",
       "      <td>22,318,579</td>\n",
       "      <td>9,107,818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILPIN</th>\n",
       "      <td>1,747,910</td>\n",
       "      <td>2,014,746</td>\n",
       "      <td>2,654,538</td>\n",
       "      <td>2,553,596</td>\n",
       "      <td>2,527,083</td>\n",
       "      <td>2,493,023</td>\n",
       "      <td>3,223,435</td>\n",
       "      <td>3,515,163</td>\n",
       "      <td>2,831,164</td>\n",
       "      <td>1,806,605</td>\n",
       "      <td>787,401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRAND</th>\n",
       "      <td>0</td>\n",
       "      <td>1,380,291</td>\n",
       "      <td>3,292,625</td>\n",
       "      <td>3,957,091</td>\n",
       "      <td>5,079,017</td>\n",
       "      <td>6,906,577</td>\n",
       "      <td>8,027,996</td>\n",
       "      <td>9,056,750</td>\n",
       "      <td>8,103,973</td>\n",
       "      <td>7,462,068</td>\n",
       "      <td>3,513,525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUNNISON</th>\n",
       "      <td>1,454,681</td>\n",
       "      <td>3,262,218</td>\n",
       "      <td>5,421,591</td>\n",
       "      <td>6,880,647</td>\n",
       "      <td>7,011,215</td>\n",
       "      <td>8,374,178</td>\n",
       "      <td>9,464,381</td>\n",
       "      <td>10,016,955</td>\n",
       "      <td>8,865,764</td>\n",
       "      <td>7,949,851</td>\n",
       "      <td>3,338,983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUERFANO</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2,364,227</td>\n",
       "      <td>3,438,327</td>\n",
       "      <td>4,477,578</td>\n",
       "      <td>3,518,764</td>\n",
       "      <td>2,912,264</td>\n",
       "      <td>1,224,725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEFFERSON</th>\n",
       "      <td>41,063,757</td>\n",
       "      <td>54,332,131</td>\n",
       "      <td>65,614,236</td>\n",
       "      <td>68,443,885</td>\n",
       "      <td>65,663,561</td>\n",
       "      <td>72,246,497</td>\n",
       "      <td>84,597,280</td>\n",
       "      <td>90,248,182</td>\n",
       "      <td>79,442,261</td>\n",
       "      <td>78,097,805</td>\n",
       "      <td>36,712,672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA PLATA</th>\n",
       "      <td>8,212,502</td>\n",
       "      <td>21,593,046</td>\n",
       "      <td>27,020,459</td>\n",
       "      <td>27,798,516</td>\n",
       "      <td>26,666,593</td>\n",
       "      <td>29,719,829</td>\n",
       "      <td>40,057,769</td>\n",
       "      <td>43,799,180</td>\n",
       "      <td>34,432,178</td>\n",
       "      <td>24,648,473</td>\n",
       "      <td>10,623,855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAKE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1,559,663</td>\n",
       "      <td>2,521,582</td>\n",
       "      <td>2,421,641</td>\n",
       "      <td>1,673,372</td>\n",
       "      <td>2,816,394</td>\n",
       "      <td>3,027,998</td>\n",
       "      <td>3,708,230</td>\n",
       "      <td>3,341,134</td>\n",
       "      <td>1,414,806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LARIMER</th>\n",
       "      <td>22,378,764</td>\n",
       "      <td>44,559,155</td>\n",
       "      <td>60,127,853</td>\n",
       "      <td>72,260,207</td>\n",
       "      <td>85,309,747</td>\n",
       "      <td>101,184,590</td>\n",
       "      <td>119,964,954</td>\n",
       "      <td>126,845,268</td>\n",
       "      <td>115,473,016</td>\n",
       "      <td>95,184,808</td>\n",
       "      <td>46,662,613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAS ANIMAS</th>\n",
       "      <td>0</td>\n",
       "      <td>8,598,642</td>\n",
       "      <td>21,456,457</td>\n",
       "      <td>44,252,628</td>\n",
       "      <td>50,757,413</td>\n",
       "      <td>56,236,877</td>\n",
       "      <td>71,160,898</td>\n",
       "      <td>66,531,557</td>\n",
       "      <td>38,426,122</td>\n",
       "      <td>25,793,893</td>\n",
       "      <td>9,271,685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3,965,601</td>\n",
       "      <td>18,456,449</td>\n",
       "      <td>22,357,262</td>\n",
       "      <td>29,926,599</td>\n",
       "      <td>34,642,212</td>\n",
       "      <td>31,308,746</td>\n",
       "      <td>30,061,141</td>\n",
       "      <td>16,257,551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOFFAT</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2,085,945</td>\n",
       "      <td>9,227,995</td>\n",
       "      <td>12,789,062</td>\n",
       "      <td>16,985,030</td>\n",
       "      <td>14,890,624</td>\n",
       "      <td>13,833,168</td>\n",
       "      <td>6,251,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTEZUMA</th>\n",
       "      <td>1,045,648</td>\n",
       "      <td>6,528,779</td>\n",
       "      <td>13,110,536</td>\n",
       "      <td>20,988,379</td>\n",
       "      <td>23,195,653</td>\n",
       "      <td>23,436,437</td>\n",
       "      <td>30,119,144</td>\n",
       "      <td>30,793,927</td>\n",
       "      <td>18,964,287</td>\n",
       "      <td>15,175,049</td>\n",
       "      <td>5,804,337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MORGAN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4,138,315</td>\n",
       "      <td>7,918,703</td>\n",
       "      <td>6,964,223</td>\n",
       "      <td>16,013,001</td>\n",
       "      <td>22,497,768</td>\n",
       "      <td>19,957,393</td>\n",
       "      <td>15,096,356</td>\n",
       "      <td>12,975,956</td>\n",
       "      <td>6,033,265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTERO</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3,503,978</td>\n",
       "      <td>3,537,141</td>\n",
       "      <td>2,028,711</td>\n",
       "      <td>1,510,621</td>\n",
       "      <td>797,339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OURAY</th>\n",
       "      <td>0</td>\n",
       "      <td>342,064</td>\n",
       "      <td>2,877,027</td>\n",
       "      <td>3,916,922</td>\n",
       "      <td>5,542,993</td>\n",
       "      <td>6,874,935</td>\n",
       "      <td>10,591,968</td>\n",
       "      <td>12,507,555</td>\n",
       "      <td>9,271,190</td>\n",
       "      <td>8,545,862</td>\n",
       "      <td>3,956,430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARK</th>\n",
       "      <td>0</td>\n",
       "      <td>1,815,652</td>\n",
       "      <td>3,331,577</td>\n",
       "      <td>4,250,202</td>\n",
       "      <td>4,570,748</td>\n",
       "      <td>5,031,104</td>\n",
       "      <td>7,150,642</td>\n",
       "      <td>8,056,545</td>\n",
       "      <td>7,220,758</td>\n",
       "      <td>6,535,750</td>\n",
       "      <td>2,806,809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PITKIN</th>\n",
       "      <td>4,540,654</td>\n",
       "      <td>8,702,783</td>\n",
       "      <td>10,628,750</td>\n",
       "      <td>12,299,200</td>\n",
       "      <td>13,059,595</td>\n",
       "      <td>13,681,310</td>\n",
       "      <td>14,527,219</td>\n",
       "      <td>14,624,543</td>\n",
       "      <td>13,362,448</td>\n",
       "      <td>11,386,497</td>\n",
       "      <td>5,150,386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUEBLO</th>\n",
       "      <td>19,643,161</td>\n",
       "      <td>25,943,197</td>\n",
       "      <td>40,905,704</td>\n",
       "      <td>47,385,855</td>\n",
       "      <td>63,902,670</td>\n",
       "      <td>86,299,074</td>\n",
       "      <td>122,105,674</td>\n",
       "      <td>123,042,699</td>\n",
       "      <td>97,451,509</td>\n",
       "      <td>81,152,418</td>\n",
       "      <td>35,401,982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROUTT</th>\n",
       "      <td>2,010,323</td>\n",
       "      <td>7,375,897</td>\n",
       "      <td>10,531,432</td>\n",
       "      <td>10,807,633</td>\n",
       "      <td>13,350,761</td>\n",
       "      <td>14,425,058</td>\n",
       "      <td>13,616,680</td>\n",
       "      <td>13,937,384</td>\n",
       "      <td>12,789,548</td>\n",
       "      <td>11,747,153</td>\n",
       "      <td>5,859,587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAGUACHE</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>464,254</td>\n",
       "      <td>1,851,675</td>\n",
       "      <td>3,012,769</td>\n",
       "      <td>4,777,160</td>\n",
       "      <td>5,124,284</td>\n",
       "      <td>4,332,448</td>\n",
       "      <td>4,189,786</td>\n",
       "      <td>1,893,221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAN JUAN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135,117</td>\n",
       "      <td>332,709</td>\n",
       "      <td>135,381</td>\n",
       "      <td>190,667</td>\n",
       "      <td>335,609</td>\n",
       "      <td>102,629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAN MIGUEL</th>\n",
       "      <td>4,933,578</td>\n",
       "      <td>3,868,704</td>\n",
       "      <td>3,769,099</td>\n",
       "      <td>4,528,036</td>\n",
       "      <td>4,798,649</td>\n",
       "      <td>5,132,275</td>\n",
       "      <td>5,516,859</td>\n",
       "      <td>5,836,809</td>\n",
       "      <td>5,296,270</td>\n",
       "      <td>4,552,874</td>\n",
       "      <td>2,099,342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDGWICK</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3,680,109</td>\n",
       "      <td>18,360,242</td>\n",
       "      <td>16,658,167</td>\n",
       "      <td>13,960,100</td>\n",
       "      <td>6,171,648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUM OF NR COUNTIES</th>\n",
       "      <td>42,852,740</td>\n",
       "      <td>57,468,800</td>\n",
       "      <td>61,876,897</td>\n",
       "      <td>55,345,614</td>\n",
       "      <td>53,605,936</td>\n",
       "      <td>59,616,176</td>\n",
       "      <td>76,060,330</td>\n",
       "      <td>70,234,829</td>\n",
       "      <td>59,736,384</td>\n",
       "      <td>38,547,730</td>\n",
       "      <td>10,832,911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUMMIT</th>\n",
       "      <td>13,510,892</td>\n",
       "      <td>17,722,280</td>\n",
       "      <td>21,872,338</td>\n",
       "      <td>24,259,822</td>\n",
       "      <td>25,356,874</td>\n",
       "      <td>26,135,213</td>\n",
       "      <td>28,690,470</td>\n",
       "      <td>31,139,802</td>\n",
       "      <td>27,327,814</td>\n",
       "      <td>22,196,328</td>\n",
       "      <td>10,633,598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELD</th>\n",
       "      <td>14,317,531</td>\n",
       "      <td>24,336,816</td>\n",
       "      <td>30,050,580</td>\n",
       "      <td>34,737,284</td>\n",
       "      <td>33,123,370</td>\n",
       "      <td>32,045,827</td>\n",
       "      <td>36,833,677</td>\n",
       "      <td>42,818,029</td>\n",
       "      <td>39,834,940</td>\n",
       "      <td>34,589,841</td>\n",
       "      <td>17,607,506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year                      2014        2015        2016        2017  \\\n",
       "county_name                                                          \n",
       "ADAMS                8,531,797  24,826,654  49,810,527  73,319,607   \n",
       "ARAPAHOE            19,083,212  68,533,185 113,890,135 124,313,953   \n",
       "ARCHULETA                    0           0           0   6,397,396   \n",
       "BENT                         0           0           0           0   \n",
       "BOULDER             62,935,522  82,465,474  95,408,126  99,923,326   \n",
       "BROOMFIELD                   0           0           0           0   \n",
       "CHAFFEE                438,160   2,067,869   3,492,699   5,422,335   \n",
       "CLEAR CREEK          6,095,765   6,435,080   7,414,079   7,262,477   \n",
       "CONEJOS                      0           0   3,522,183   5,426,288   \n",
       "COSTILLA                     0           0   2,836,421   3,874,954   \n",
       "CROWLEY                      0           0           0           0   \n",
       "DELTA                        0           0           0           0   \n",
       "DENVER             325,930,878 407,204,194 501,611,664 577,537,470   \n",
       "EAGLE                4,494,510  11,685,385  14,839,943  15,510,137   \n",
       "EL PASO             67,026,306  86,100,007 101,137,059 102,115,436   \n",
       "FREMONT              2,530,928   2,392,649   2,077,835   1,663,232   \n",
       "GARFIELD             8,744,520  14,035,557  20,923,125  25,399,951   \n",
       "GILPIN               1,747,910   2,014,746   2,654,538   2,553,596   \n",
       "GRAND                        0   1,380,291   3,292,625   3,957,091   \n",
       "GUNNISON             1,454,681   3,262,218   5,421,591   6,880,647   \n",
       "HUERFANO                     0           0           0           0   \n",
       "JEFFERSON           41,063,757  54,332,131  65,614,236  68,443,885   \n",
       "LA PLATA             8,212,502  21,593,046  27,020,459  27,798,516   \n",
       "LAKE                         0           0   1,559,663   2,521,582   \n",
       "LARIMER             22,378,764  44,559,155  60,127,853  72,260,207   \n",
       "LAS ANIMAS                   0   8,598,642  21,456,457  44,252,628   \n",
       "MESA                         0           0           0   3,965,601   \n",
       "MOFFAT                       0           0           0           0   \n",
       "MONTEZUMA            1,045,648   6,528,779  13,110,536  20,988,379   \n",
       "MORGAN                       0           0   4,138,315   7,918,703   \n",
       "OTERO                        0           0           0           0   \n",
       "OURAY                        0     342,064   2,877,027   3,916,922   \n",
       "PARK                         0   1,815,652   3,331,577   4,250,202   \n",
       "PITKIN               4,540,654   8,702,783  10,628,750  12,299,200   \n",
       "PUEBLO              19,643,161  25,943,197  40,905,704  47,385,855   \n",
       "ROUTT                2,010,323   7,375,897  10,531,432  10,807,633   \n",
       "SAGUACHE                     0           0           0     464,254   \n",
       "SAN JUAN                     0           0           0           0   \n",
       "SAN MIGUEL           4,933,578   3,868,704   3,769,099   4,528,036   \n",
       "SEDGWICK                     0           0           0           0   \n",
       "SUM OF NR COUNTIES  42,852,740  57,468,800  61,876,897  55,345,614   \n",
       "SUMMIT              13,510,892  17,722,280  21,872,338  24,259,822   \n",
       "WELD                14,317,531  24,336,816  30,050,580  34,737,284   \n",
       "\n",
       "year                      2018        2019        2020        2021  \\\n",
       "county_name                                                          \n",
       "ADAMS               89,595,114 115,030,166 155,274,744 159,915,797   \n",
       "ARAPAHOE           129,801,179 153,255,179 188,314,099 199,947,743   \n",
       "ARCHULETA            8,848,341  10,248,743  15,536,321  16,992,084   \n",
       "BENT                         0           0     504,288   5,526,743   \n",
       "BOULDER            108,277,854 116,859,122 138,333,017 143,139,122   \n",
       "BROOMFIELD                   0           0           0           0   \n",
       "CHAFFEE              5,360,100   4,330,415   6,830,041   4,353,188   \n",
       "CLEAR CREEK          7,425,243   7,950,847   8,852,029  10,669,939   \n",
       "CONEJOS              6,033,662   1,314,361           0  10,348,829   \n",
       "COSTILLA             4,172,958   4,730,915   6,844,327   7,092,827   \n",
       "CROWLEY                      0           0           0           0   \n",
       "DELTA                        0           0           0           0   \n",
       "DENVER             536,631,891 579,676,047 709,477,874 682,125,808   \n",
       "EAGLE               18,083,147  19,144,708  22,612,151  23,726,784   \n",
       "EL PASO             92,786,795 104,698,718 143,725,552 126,378,365   \n",
       "FREMONT              1,090,121     463,876     223,258           0   \n",
       "GARFIELD            22,282,865  25,664,808  29,092,496  29,524,888   \n",
       "GILPIN               2,527,083   2,493,023   3,223,435   3,515,163   \n",
       "GRAND                5,079,017   6,906,577   8,027,996   9,056,750   \n",
       "GUNNISON             7,011,215   8,374,178   9,464,381  10,016,955   \n",
       "HUERFANO                     0   2,364,227   3,438,327   4,477,578   \n",
       "JEFFERSON           65,663,561  72,246,497  84,597,280  90,248,182   \n",
       "LA PLATA            26,666,593  29,719,829  40,057,769  43,799,180   \n",
       "LAKE                 2,421,641   1,673,372   2,816,394   3,027,998   \n",
       "LARIMER             85,309,747 101,184,590 119,964,954 126,845,268   \n",
       "LAS ANIMAS          50,757,413  56,236,877  71,160,898  66,531,557   \n",
       "MESA                18,456,449  22,357,262  29,926,599  34,642,212   \n",
       "MOFFAT               2,085,945   9,227,995  12,789,062  16,985,030   \n",
       "MONTEZUMA           23,195,653  23,436,437  30,119,144  30,793,927   \n",
       "MORGAN               6,964,223  16,013,001  22,497,768  19,957,393   \n",
       "OTERO                        0           0   3,503,978   3,537,141   \n",
       "OURAY                5,542,993   6,874,935  10,591,968  12,507,555   \n",
       "PARK                 4,570,748   5,031,104   7,150,642   8,056,545   \n",
       "PITKIN              13,059,595  13,681,310  14,527,219  14,624,543   \n",
       "PUEBLO              63,902,670  86,299,074 122,105,674 123,042,699   \n",
       "ROUTT               13,350,761  14,425,058  13,616,680  13,937,384   \n",
       "SAGUACHE             1,851,675   3,012,769   4,777,160   5,124,284   \n",
       "SAN JUAN                     0     135,117     332,709     135,381   \n",
       "SAN MIGUEL           4,798,649   5,132,275   5,516,859   5,836,809   \n",
       "SEDGWICK                     0           0   3,680,109  18,360,242   \n",
       "SUM OF NR COUNTIES  53,605,936  59,616,176  76,060,330  70,234,829   \n",
       "SUMMIT              25,356,874  26,135,213  28,690,470  31,139,802   \n",
       "WELD                33,123,370  32,045,827  36,833,677  42,818,029   \n",
       "\n",
       "year                      2022        2023        2024  \n",
       "county_name                                             \n",
       "ADAMS              139,731,225 119,542,135  51,777,262  \n",
       "ARAPAHOE           181,646,516 160,475,693  71,239,956  \n",
       "ARCHULETA           12,398,961   9,389,966   4,030,701  \n",
       "BENT                 5,946,691   4,275,606   1,781,995  \n",
       "BOULDER            123,282,951 102,124,942  45,976,643  \n",
       "BROOMFIELD                   0   7,837,483   7,578,159  \n",
       "CHAFFEE              6,231,401   8,188,760   3,085,707  \n",
       "CLEAR CREEK          9,775,008   8,229,908   3,428,940  \n",
       "CONEJOS              6,785,181   4,491,591   1,887,907  \n",
       "COSTILLA             4,300,334   2,760,622   1,116,876  \n",
       "CROWLEY                898,993     217,342           0  \n",
       "DELTA                3,412,366   4,906,033   1,378,592  \n",
       "DENVER             475,484,281 398,982,406 183,471,135  \n",
       "EAGLE               21,320,338  19,057,385   9,124,328  \n",
       "EL PASO             81,805,544  98,033,319  57,336,531  \n",
       "FREMONT                      0           0           0  \n",
       "GARFIELD            25,106,708  22,318,579   9,107,818  \n",
       "GILPIN               2,831,164   1,806,605     787,401  \n",
       "GRAND                8,103,973   7,462,068   3,513,525  \n",
       "GUNNISON             8,865,764   7,949,851   3,338,983  \n",
       "HUERFANO             3,518,764   2,912,264   1,224,725  \n",
       "JEFFERSON           79,442,261  78,097,805  36,712,672  \n",
       "LA PLATA            34,432,178  24,648,473  10,623,855  \n",
       "LAKE                 3,708,230   3,341,134   1,414,806  \n",
       "LARIMER            115,473,016  95,184,808  46,662,613  \n",
       "LAS ANIMAS          38,426,122  25,793,893   9,271,685  \n",
       "MESA                31,308,746  30,061,141  16,257,551  \n",
       "MOFFAT              14,890,624  13,833,168   6,251,491  \n",
       "MONTEZUMA           18,964,287  15,175,049   5,804,337  \n",
       "MORGAN              15,096,356  12,975,956   6,033,265  \n",
       "OTERO                2,028,711   1,510,621     797,339  \n",
       "OURAY                9,271,190   8,545,862   3,956,430  \n",
       "PARK                 7,220,758   6,535,750   2,806,809  \n",
       "PITKIN              13,362,448  11,386,497   5,150,386  \n",
       "PUEBLO              97,451,509  81,152,418  35,401,982  \n",
       "ROUTT               12,789,548  11,747,153   5,859,587  \n",
       "SAGUACHE             4,332,448   4,189,786   1,893,221  \n",
       "SAN JUAN               190,667     335,609     102,629  \n",
       "SAN MIGUEL           5,296,270   4,552,874   2,099,342  \n",
       "SEDGWICK            16,658,167  13,960,100   6,171,648  \n",
       "SUM OF NR COUNTIES  59,736,384  38,547,730  10,832,911  \n",
       "SUMMIT              27,327,814  22,196,328  10,633,598  \n",
       "WELD                39,834,940  34,589,841  17,607,506  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum by county by year\n",
    "pd.pivot_table(\n",
    "    df,\n",
    "    index='county_name',\n",
    "    values='amount',\n",
    "    columns='year',\n",
    "    aggfunc=sum\n",
    ").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.616301050487305"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for all years, what percentage of total sales do the \"NR Counties\" contribute?\n",
    "(df[df.county_name == 'SUM OF NR COUNTIES'].amount.sum() / df.amount.sum()) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's make a line chart\n",
    "\n",
    "Specifically, let's replicate the line chart from the state webpage:\n",
    "\n",
    "![official chart](https://cdor.colorado.gov/sites/revenue/files/styles/tablet_x2_1024px/public/2023-01/1122_MarijuanaSalesGraph.png.webp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# chart totals by month, each type\n",
    "\n",
    "# groupby objects for total, medical and retail dataframes\n",
    "total_grouped = df[['date', 'amount']].groupby('date').sum()\n",
    "med_grouped = med[['date', 'amount']].groupby('date').sum()\n",
    "retail_grouped = retail[['date', 'amount']].groupby('date').sum()\n",
    "\n",
    "# start with totals to create the chart\n",
    "linechart = total_grouped.plot(\n",
    "    color='black',\n",
    "    figsize=(17, 6),\n",
    "    title='Colorado cannabis sales',\n",
    "    linestyle='--'\n",
    ")\n",
    "\n",
    "# plot retail\n",
    "retail_grouped.plot(ax=linechart, color='DarkOliveGreen')\n",
    "\n",
    "# plot medical\n",
    "med_grouped.plot(ax=linechart, color='LimeGreen')\n",
    "\n",
    "# set the legend\n",
    "linechart.legend(['Total', 'Retail', 'Medical'])\n",
    "\n",
    "# turn major gridlines on\n",
    "linechart.grid(True, which='major', axis='y')\n",
    "\n",
    "# blank axis labels -- seems like it's implied?\n",
    "linechart.set_xlabel('')\n",
    "linechart.set_ylabel('')\n",
    "\n",
    "# format y axis values\n",
    "linechart.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '${:,.0f}'.format(y))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A function to download a GIS file of Colorado counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_co_county_gis_data():\n",
    "    '''download a zipped shapefile of Colorado counties'''\n",
    "\n",
    "    url = 'https://www2.census.gov/geo/tiger/TIGER2010/COUNTY/2000/tl_2010_08_county00.zip'\n",
    "    fname = os.path.join('raw-data', url.split('/')[-1])    \n",
    "    \n",
    "    # if it's not here already ...\n",
    "    if not os.path.isfile(fname):\n",
    "        \n",
    "        # fetch it\n",
    "        r = requests.get(url)\n",
    "\n",
    "        # and write it to file\n",
    "        with open(fname, 'wb') as f:\n",
    "\n",
    "            for block in r.iter_content(1024):\n",
    "                f.write(block)\n",
    "\n",
    "    return fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fetch the GIS data and assign what's returned (the filename) to a variable\n",
    "geodata = get_co_county_gis_data()\n",
    "\n",
    "# unzip the file\n",
    "with zipfile.ZipFile(geodata, 'r') as z:\n",
    "    z.extractall('raw-data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the GIS data into geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the shapefile naming convention is the same, so get the root filename + '.shp'\n",
    "shp = geodata.split('.zip')[0] + '.shp'\n",
    "\n",
    "# and load it into a geodataframe\n",
    "gdf = gpd.read_file(shp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP00</th>\n",
       "      <th>COUNTYFP00</th>\n",
       "      <th>CNTYIDFP00</th>\n",
       "      <th>NAME00</th>\n",
       "      <th>NAMELSAD00</th>\n",
       "      <th>LSAD00</th>\n",
       "      <th>CLASSFP00</th>\n",
       "      <th>MTFCC00</th>\n",
       "      <th>UR00</th>\n",
       "      <th>FUNCSTAT00</th>\n",
       "      <th>ALAND00</th>\n",
       "      <th>AWATER00</th>\n",
       "      <th>INTPTLAT00</th>\n",
       "      <th>INTPTLON00</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08</td>\n",
       "      <td>013</td>\n",
       "      <td>08013</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>Boulder County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>1908733293</td>\n",
       "      <td>36715760</td>\n",
       "      <td>+40.0866321</td>\n",
       "      <td>-105.3590729</td>\n",
       "      <td>POLYGON ((-105.16615 39.91405, -105.16657 39.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08</td>\n",
       "      <td>069</td>\n",
       "      <td>08069</td>\n",
       "      <td>Larimer</td>\n",
       "      <td>Larimer County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>6723614521</td>\n",
       "      <td>98295250</td>\n",
       "      <td>+40.6630912</td>\n",
       "      <td>-105.4821309</td>\n",
       "      <td>POLYGON ((-105.05672 40.34928, -105.05670 40.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08</td>\n",
       "      <td>071</td>\n",
       "      <td>08071</td>\n",
       "      <td>Las Animas</td>\n",
       "      <td>Las Animas County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>12361162110</td>\n",
       "      <td>6929979</td>\n",
       "      <td>+37.3188308</td>\n",
       "      <td>-104.0441103</td>\n",
       "      <td>POLYGON ((-104.14318 37.75833, -104.12615 37.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08</td>\n",
       "      <td>043</td>\n",
       "      <td>08043</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Fremont County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>3970618782</td>\n",
       "      <td>2235223</td>\n",
       "      <td>+38.4556576</td>\n",
       "      <td>-105.4214383</td>\n",
       "      <td>POLYGON ((-105.36889 38.25940, -105.36909 38.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08</td>\n",
       "      <td>041</td>\n",
       "      <td>08041</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>El Paso County</td>\n",
       "      <td>06</td>\n",
       "      <td>H1</td>\n",
       "      <td>G4020</td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>5508385644</td>\n",
       "      <td>7111582</td>\n",
       "      <td>+38.8273831</td>\n",
       "      <td>-104.5274718</td>\n",
       "      <td>POLYGON ((-104.93899 38.79201, -104.93898 38.7...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP00 COUNTYFP00 CNTYIDFP00      NAME00         NAMELSAD00 LSAD00  \\\n",
       "0        08        013      08013     Boulder     Boulder County     06   \n",
       "1        08        069      08069     Larimer     Larimer County     06   \n",
       "2        08        071      08071  Las Animas  Las Animas County     06   \n",
       "3        08        043      08043     Fremont     Fremont County     06   \n",
       "4        08        041      08041     El Paso     El Paso County     06   \n",
       "\n",
       "  CLASSFP00 MTFCC00 UR00 FUNCSTAT00      ALAND00  AWATER00   INTPTLAT00  \\\n",
       "0        H1   G4020    M          A   1908733293  36715760  +40.0866321   \n",
       "1        H1   G4020    M          A   6723614521  98295250  +40.6630912   \n",
       "2        H1   G4020    M          A  12361162110   6929979  +37.3188308   \n",
       "3        H1   G4020    M          A   3970618782   2235223  +38.4556576   \n",
       "4        H1   G4020    M          A   5508385644   7111582  +38.8273831   \n",
       "\n",
       "     INTPTLON00                                           geometry  \n",
       "0  -105.3590729  POLYGON ((-105.16615 39.91405, -105.16657 39.9...  \n",
       "1  -105.4821309  POLYGON ((-105.05672 40.34928, -105.05670 40.3...  \n",
       "2  -104.0441103  POLYGON ((-104.14318 37.75833, -104.12615 37.7...  \n",
       "3  -105.4214383  POLYGON ((-105.36889 38.25940, -105.36909 38.2...  \n",
       "4  -104.5274718  POLYGON ((-104.93899 38.79201, -104.93898 38.7...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grab population data from the Census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grab environmental variable with API key\n",
    "census_api_key = os.environ.get('CENSUS_API_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_county_pop(target_year):\n",
    "    \n",
    "    # endpoint for latest-ish population estimates\n",
    "    endpoint = f'https://api.census.gov/data/{target_year}/pep/population'\n",
    "    \n",
    "\n",
    "    # URL parameters to target info we want\n",
    "    params = {\n",
    "        'get': 'NAME,POP',\n",
    "        'for': 'county:*',\n",
    "        'in': 'state:08',\n",
    "        'key': census_api_key\n",
    "    }\n",
    "\n",
    "    # request data from the API\n",
    "    r = requests.get(endpoint, params=params)\n",
    "    r.raise_for_status()\n",
    "    \n",
    "    # turn the response into Python data objects\n",
    "    j = r.json()\n",
    "    \n",
    "    # get the headers from the first record\n",
    "    headers = j[0]\n",
    "    \n",
    "    # return a dict of the list of data\n",
    "    return [dict(zip(headers, x)) for x in j[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_year = date.today().year\n",
    "\n",
    "while True:\n",
    "    try:\n",
    "        cpop = get_county_pop(target_year)\n",
    "        break\n",
    "    except requests.exceptions.HTTPError:\n",
    "        target_year -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "popfile = os.path.join('processed-data', f'colorado-county-pop-estimates-{target_year}.csv')\n",
    "\n",
    "# write out population data to file\n",
    "with open(popfile, 'w') as o:\n",
    "    headers = list(cpop[0].keys())\n",
    "    writer = csv.DictWriter(o, fieldnames=headers)\n",
    "    writer.writeheader()\n",
    "    writer.writerows(cpop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data into a dataframe, preserving the FIPS codes as strings\n",
    "pop_df = pd.read_csv(\n",
    "    popfile,\n",
    "    dtype={\n",
    "        'state': str,\n",
    "        'county': str,\n",
    "        'POP': float}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>NAME</th>\n",
       "      <th>POP</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Phillips County, Colorado</td>\n",
       "      <td>4,265</td>\n",
       "      <td>08</td>\n",
       "      <td>095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Archuleta County, Colorado</td>\n",
       "      <td>14,029</td>\n",
       "      <td>08</td>\n",
       "      <td>007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denver County, Colorado</td>\n",
       "      <td>727,211</td>\n",
       "      <td>08</td>\n",
       "      <td>031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baca County, Colorado</td>\n",
       "      <td>3,581</td>\n",
       "      <td>08</td>\n",
       "      <td>009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ouray County, Colorado</td>\n",
       "      <td>4,952</td>\n",
       "      <td>08</td>\n",
       "      <td>091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         NAME     POP state county\n",
       "0   Phillips County, Colorado   4,265    08    095\n",
       "1  Archuleta County, Colorado  14,029    08    007\n",
       "2     Denver County, Colorado 727,211    08    031\n",
       "3       Baca County, Colorado   3,581    08    009\n",
       "4      Ouray County, Colorado   4,952    08    091"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine county and state FIPS\n",
    "pop_df['fips'] = pop_df['state'] + pop_df['county']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>NAME</th>\n",
       "      <th>POP</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>fips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Phillips County, Colorado</td>\n",
       "      <td>4,265</td>\n",
       "      <td>08</td>\n",
       "      <td>095</td>\n",
       "      <td>08095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Archuleta County, Colorado</td>\n",
       "      <td>14,029</td>\n",
       "      <td>08</td>\n",
       "      <td>007</td>\n",
       "      <td>08007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denver County, Colorado</td>\n",
       "      <td>727,211</td>\n",
       "      <td>08</td>\n",
       "      <td>031</td>\n",
       "      <td>08031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baca County, Colorado</td>\n",
       "      <td>3,581</td>\n",
       "      <td>08</td>\n",
       "      <td>009</td>\n",
       "      <td>08009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ouray County, Colorado</td>\n",
       "      <td>4,952</td>\n",
       "      <td>08</td>\n",
       "      <td>091</td>\n",
       "      <td>08091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         NAME     POP state county   fips\n",
       "0   Phillips County, Colorado   4,265    08    095  08095\n",
       "1  Archuleta County, Colorado  14,029    08    007  08007\n",
       "2     Denver County, Colorado 727,211    08    031  08031\n",
       "3       Baca County, Colorado   3,581    08    009  08009\n",
       "4      Ouray County, Colorado   4,952    08    091  08091"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map: Per-capita sales analysis\n",
    "\n",
    "We'll look at the most recent retail sales data -- [checking this researcher's work, basically](https://www.thecannabist.co/2018/02/09/colorado-marijuana-sales-southern-border/98669/). Our steps:\n",
    "\n",
    "1. Merge our GIS dataframe with the population data\n",
    "2. Merge the result with the cannabis sales data\n",
    "3. Calculate the per-capita rate\n",
    "4. Plot on a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_pop_df = pd.merge(\n",
    "    gdf,\n",
    "    pop_df,\n",
    "    how='left',\n",
    "    left_on='CNTYIDFP00',\n",
    "    right_on='fips'\n",
    ")[['fips', 'NAME00', 'geometry', 'POP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>fips</th>\n",
       "      <th>NAME00</th>\n",
       "      <th>geometry</th>\n",
       "      <th>POP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08013</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>POLYGON ((-105.16615 39.91405, -105.16657 39.9...</td>\n",
       "      <td>326,196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08069</td>\n",
       "      <td>Larimer</td>\n",
       "      <td>POLYGON ((-105.05672 40.34928, -105.05670 40.3...</td>\n",
       "      <td>356,899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08071</td>\n",
       "      <td>Las Animas</td>\n",
       "      <td>POLYGON ((-104.14318 37.75833, -104.12615 37.7...</td>\n",
       "      <td>14,506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08043</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>POLYGON ((-105.36889 38.25940, -105.36909 38.2...</td>\n",
       "      <td>47,839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08041</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>POLYGON ((-104.93899 38.79201, -104.93898 38.7...</td>\n",
       "      <td>720,403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fips      NAME00                                           geometry  \\\n",
       "0  08013     Boulder  POLYGON ((-105.16615 39.91405, -105.16657 39.9...   \n",
       "1  08069     Larimer  POLYGON ((-105.05672 40.34928, -105.05670 40.3...   \n",
       "2  08071  Las Animas  POLYGON ((-104.14318 37.75833, -104.12615 37.7...   \n",
       "3  08043     Fremont  POLYGON ((-105.36889 38.25940, -105.36909 38.2...   \n",
       "4  08041     El Paso  POLYGON ((-104.93899 38.79201, -104.93898 38.7...   \n",
       "\n",
       "      POP  \n",
       "0 326,196  \n",
       "1 356,899  \n",
       "2  14,506  \n",
       "3  47,839  \n",
       "4 720,403  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_pop_df['NAME00'] = geo_pop_df['NAME00'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter to get latest retail sales data\n",
    "latest_retail = retail[retail['year'] == target_year]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>monthyear</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>amount</th>\n",
       "      <th>sales_type</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3057</th>\n",
       "      <td>201901</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>08001</td>\n",
       "      <td>ADAMS</td>\n",
       "      <td>7196545</td>\n",
       "      <td>retail</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3059</th>\n",
       "      <td>201901</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>08005</td>\n",
       "      <td>ARAPAHOE</td>\n",
       "      <td>9767681</td>\n",
       "      <td>retail</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>201901</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>08007</td>\n",
       "      <td>ARCHULETA</td>\n",
       "      <td>674209</td>\n",
       "      <td>retail</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3062</th>\n",
       "      <td>201901</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>08013</td>\n",
       "      <td>BOULDER</td>\n",
       "      <td>7108509</td>\n",
       "      <td>retail</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3063</th>\n",
       "      <td>201901</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>08015</td>\n",
       "      <td>CHAFFEE</td>\n",
       "      <td>414799</td>\n",
       "      <td>retail</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      monthyear  month  year county_fips county_name   amount sales_type  \\\n",
       "3057     201901      1  2019       08001       ADAMS  7196545     retail   \n",
       "3059     201901      1  2019       08005    ARAPAHOE  9767681     retail   \n",
       "3060     201901      1  2019       08007   ARCHULETA   674209     retail   \n",
       "3062     201901      1  2019       08013     BOULDER  7108509     retail   \n",
       "3063     201901      1  2019       08015     CHAFFEE   414799     retail   \n",
       "\n",
       "            date  \n",
       "3057  2019-01-01  \n",
       "3059  2019-01-01  \n",
       "3060  2019-01-01  \n",
       "3062  2019-01-01  \n",
       "3063  2019-01-01  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_retail.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by county and sum the amounts\n",
    "latest_retail_group = latest_retail[['county_fips', 'amount']].groupby('county_fips').sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fips</th>\n",
       "      <th>NAME00</th>\n",
       "      <th>geometry</th>\n",
       "      <th>POP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08013</td>\n",
       "      <td>BOULDER</td>\n",
       "      <td>POLYGON ((-105.16615 39.91405, -105.16657 39.9...</td>\n",
       "      <td>326,196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08069</td>\n",
       "      <td>LARIMER</td>\n",
       "      <td>POLYGON ((-105.05672 40.34928, -105.05670 40.3...</td>\n",
       "      <td>356,899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08071</td>\n",
       "      <td>LAS ANIMAS</td>\n",
       "      <td>POLYGON ((-104.14318 37.75833, -104.12615 37.7...</td>\n",
       "      <td>14,506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08043</td>\n",
       "      <td>FREMONT</td>\n",
       "      <td>POLYGON ((-105.36889 38.25940, -105.36909 38.2...</td>\n",
       "      <td>47,839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08041</td>\n",
       "      <td>EL PASO</td>\n",
       "      <td>POLYGON ((-104.93899 38.79201, -104.93898 38.7...</td>\n",
       "      <td>720,403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fips      NAME00                                           geometry  \\\n",
       "0  08013     BOULDER  POLYGON ((-105.16615 39.91405, -105.16657 39.9...   \n",
       "1  08069     LARIMER  POLYGON ((-105.05672 40.34928, -105.05670 40.3...   \n",
       "2  08071  LAS ANIMAS  POLYGON ((-104.14318 37.75833, -104.12615 37.7...   \n",
       "3  08043     FREMONT  POLYGON ((-105.36889 38.25940, -105.36909 38.2...   \n",
       "4  08041     EL PASO  POLYGON ((-104.93899 38.79201, -104.93898 38.7...   \n",
       "\n",
       "      POP  \n",
       "0 326,196  \n",
       "1 356,899  \n",
       "2  14,506  \n",
       "3  47,839  \n",
       "4 720,403  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge that with the dataframe we merged earlier\n",
    "latest_retail_map = pd.merge(\n",
    "    geo_pop_df,\n",
    "    latest_retail_group,\n",
    "    how='left',\n",
    "    left_on='fips',\n",
    "    right_on='county_fips'\n",
    ")[['fips', 'NAME00', 'geometry', 'POP', 'amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fips</th>\n",
       "      <th>NAME00</th>\n",
       "      <th>geometry</th>\n",
       "      <th>POP</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08013</td>\n",
       "      <td>BOULDER</td>\n",
       "      <td>POLYGON ((-105.16615 39.91405, -105.16657 39.9...</td>\n",
       "      <td>326,196</td>\n",
       "      <td>102,617,150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08069</td>\n",
       "      <td>LARIMER</td>\n",
       "      <td>POLYGON ((-105.05672 40.34928, -105.05670 40.3...</td>\n",
       "      <td>356,899</td>\n",
       "      <td>90,982,853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08071</td>\n",
       "      <td>LAS ANIMAS</td>\n",
       "      <td>POLYGON ((-104.14318 37.75833, -104.12615 37.7...</td>\n",
       "      <td>14,506</td>\n",
       "      <td>55,815,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08043</td>\n",
       "      <td>FREMONT</td>\n",
       "      <td>POLYGON ((-105.36889 38.25940, -105.36909 38.2...</td>\n",
       "      <td>47,839</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08041</td>\n",
       "      <td>EL PASO</td>\n",
       "      <td>POLYGON ((-104.93899 38.79201, -104.93898 38.7...</td>\n",
       "      <td>720,403</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fips      NAME00                                           geometry  \\\n",
       "0  08013     BOULDER  POLYGON ((-105.16615 39.91405, -105.16657 39.9...   \n",
       "1  08069     LARIMER  POLYGON ((-105.05672 40.34928, -105.05670 40.3...   \n",
       "2  08071  LAS ANIMAS  POLYGON ((-104.14318 37.75833, -104.12615 37.7...   \n",
       "3  08043     FREMONT  POLYGON ((-105.36889 38.25940, -105.36909 38.2...   \n",
       "4  08041     EL PASO  POLYGON ((-104.93899 38.79201, -104.93898 38.7...   \n",
       "\n",
       "      POP      amount  \n",
       "0 326,196 102,617,150  \n",
       "1 356,899  90,982,853  \n",
       "2  14,506  55,815,043  \n",
       "3  47,839         NaN  \n",
       "4 720,403         NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_retail_map.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the per-capita rate\n",
    "latest_retail_map['per_capita'] = latest_retail_map['amount'] / latest_retail_map['POP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fips</th>\n",
       "      <th>NAME00</th>\n",
       "      <th>geometry</th>\n",
       "      <th>POP</th>\n",
       "      <th>amount</th>\n",
       "      <th>per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08071</td>\n",
       "      <td>LAS ANIMAS</td>\n",
       "      <td>POLYGON ((-104.14318 37.75833, -104.12615 37.7...</td>\n",
       "      <td>14,506</td>\n",
       "      <td>55,815,043</td>\n",
       "      <td>3,848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>08091</td>\n",
       "      <td>OURAY</td>\n",
       "      <td>POLYGON ((-107.95565 38.22649, -107.95659 38.2...</td>\n",
       "      <td>4,952</td>\n",
       "      <td>6,874,935</td>\n",
       "      <td>1,388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>08023</td>\n",
       "      <td>COSTILLA</td>\n",
       "      <td>POLYGON ((-105.73841 37.30676, -105.73842 37.3...</td>\n",
       "      <td>3,887</td>\n",
       "      <td>4,730,915</td>\n",
       "      <td>1,217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>08083</td>\n",
       "      <td>MONTEZUMA</td>\n",
       "      <td>POLYGON ((-109.04583 37.32684, -109.04584 37.3...</td>\n",
       "      <td>26,183</td>\n",
       "      <td>23,177,836</td>\n",
       "      <td>885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>08019</td>\n",
       "      <td>CLEAR CREEK</td>\n",
       "      <td>POLYGON ((-105.72413 39.81902, -105.72388 39.8...</td>\n",
       "      <td>9,700</td>\n",
       "      <td>7,939,056</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     fips       NAME00                                           geometry  \\\n",
       "2   08071   LAS ANIMAS  POLYGON ((-104.14318 37.75833, -104.12615 37.7...   \n",
       "44  08091        OURAY  POLYGON ((-107.95565 38.22649, -107.95659 38.2...   \n",
       "24  08023     COSTILLA  POLYGON ((-105.73841 37.30676, -105.73842 37.3...   \n",
       "29  08083    MONTEZUMA  POLYGON ((-109.04583 37.32684, -109.04584 37.3...   \n",
       "56  08019  CLEAR CREEK  POLYGON ((-105.72413 39.81902, -105.72388 39.8...   \n",
       "\n",
       "      POP     amount  per_capita  \n",
       "2  14,506 55,815,043       3,848  \n",
       "44  4,952  6,874,935       1,388  \n",
       "24  3,887  4,730,915       1,217  \n",
       "29 26,183 23,177,836         885  \n",
       "56  9,700  7,939,056         818  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_retail_map.sort_values('per_capita', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on a map\n",
    "latest_retail_map.plot(\n",
    "    column='per_capita',\n",
    "    edgecolor='black',\n",
    "    cmap='YlGn', \n",
    "    missing_kwds={\n",
    "        'color': 'white'\n",
    "    },\n",
    "    figsize=(13,7)\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}