Finding Where to Thrive - Population Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1b39730-300b-4f51-bdec-011776cb64bc",
   "metadata": {},
   "source": [
    "# Finding Where to Thrive - Part IV\n",
    "## Exploring Population Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04e7db8-8b6d-48b8-b152-6f7e841c08bf",
   "metadata": {},
   "source": [
    "In this notebook, we'll load the population data that we collected from the U.S. Census Bureau (https://www.census.gov/data.html) for 2010 - 2021, create and view data profile reports, view the descriptive statistics, clean and prep the data for processing, and complete exploratory data analysis.\n",
    "\n",
    "We'll look to answer the following questions:\n",
    "\n",
    "1. Which states have the largest population increases and decreases?\n",
    "\n",
    "2. Which metro areas have the largest population increases and decreases?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90215b50-0f5c-49bb-830c-d96761d64c36",
   "metadata": {},
   "source": [
    "First, we'll start with importing the libraries and tools that we'll need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2580e192-acc9-49de-a4da-bd75c019c1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from dataprep.eda import plot, create_report\n",
    "\n",
    "pd.set_option(\"display.max.columns\", 100) # change jupyter settings to see all columns\n",
    "pd.set_option(\"display.precision\", 2) # change jupyter settings to only show two decimal places\n",
    "pd.set_option('display.float_format', str) # disable scientific notation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3696acad-6ca7-4a15-995e-9d892b38a3ad",
   "metadata": {},
   "source": [
    "### Dataset Profiling\n",
    "We will use the DataPrep library in Python create and view a data profile report for each data set to perform structure and content discovery, as well as view basic descriptive statistics. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c285649-e606-4286-98ed-bca537b63996",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### 20210 - 2019 City and Town Level Population Data Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "666f012d-6d4a-44ae-97d3-1bf036dd4aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create 2010 - 2019 Pop. DataFrame\n",
    "\n",
    "city_town_est2010_2019 = pd.read_csv(\"Population Data/city_town-est2010-2019_all.csv\",encoding='latin-1') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2c309136-6092-4f2f-8bd9-e223b67f5914",
   "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>SUMLEV</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>PLACE</th>\n",
       "      <th>COUSUB</th>\n",
       "      <th>CONCIT</th>\n",
       "      <th>PRIMGEO_FLAG</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>NAME</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>POPESTIMATE2013</th>\n",
       "      <th>POPESTIMATE2014</th>\n",
       "      <th>POPESTIMATE2015</th>\n",
       "      <th>POPESTIMATE2016</th>\n",
       "      <th>POPESTIMATE2017</th>\n",
       "      <th>POPESTIMATE2018</th>\n",
       "      <th>POPESTIMATE2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4779736</td>\n",
       "      <td>4780125</td>\n",
       "      <td>4785437</td>\n",
       "      <td>4799069</td>\n",
       "      <td>4815588</td>\n",
       "      <td>4830081</td>\n",
       "      <td>4841799</td>\n",
       "      <td>4852347</td>\n",
       "      <td>4863525</td>\n",
       "      <td>4874486</td>\n",
       "      <td>4887681</td>\n",
       "      <td>4903185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Abbeville city</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2688</td>\n",
       "      <td>2705</td>\n",
       "      <td>2699</td>\n",
       "      <td>2694</td>\n",
       "      <td>2643</td>\n",
       "      <td>2628</td>\n",
       "      <td>2608</td>\n",
       "      <td>2600</td>\n",
       "      <td>2584</td>\n",
       "      <td>2575</td>\n",
       "      <td>2571</td>\n",
       "      <td>2560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Adamsville city</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4522</td>\n",
       "      <td>4506</td>\n",
       "      <td>4500</td>\n",
       "      <td>4493</td>\n",
       "      <td>4471</td>\n",
       "      <td>4449</td>\n",
       "      <td>4420</td>\n",
       "      <td>4390</td>\n",
       "      <td>4356</td>\n",
       "      <td>4327</td>\n",
       "      <td>4308</td>\n",
       "      <td>4281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>484</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Addison town</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>758</td>\n",
       "      <td>754</td>\n",
       "      <td>751</td>\n",
       "      <td>750</td>\n",
       "      <td>743</td>\n",
       "      <td>742</td>\n",
       "      <td>739</td>\n",
       "      <td>734</td>\n",
       "      <td>731</td>\n",
       "      <td>726</td>\n",
       "      <td>723</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>676</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Akron town</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>356</td>\n",
       "      <td>356</td>\n",
       "      <td>355</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>343</td>\n",
       "      <td>338</td>\n",
       "      <td>339</td>\n",
       "      <td>333</td>\n",
       "      <td>332</td>\n",
       "      <td>331</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUMLEV  STATE  COUNTY  PLACE  COUSUB  CONCIT  PRIMGEO_FLAG FUNCSTAT  \\\n",
       "0      40      1       0      0       0       0             0        A   \n",
       "1     162      1       0    124       0       0             0        A   \n",
       "2     162      1       0    460       0       0             0        A   \n",
       "3     162      1       0    484       0       0             0        A   \n",
       "4     162      1       0    676       0       0             0        A   \n",
       "\n",
       "              NAME   STNAME CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010  \\\n",
       "0          Alabama  Alabama       4779736            4780125          4785437   \n",
       "1   Abbeville city  Alabama          2688               2705             2699   \n",
       "2  Adamsville city  Alabama          4522               4506             4500   \n",
       "3     Addison town  Alabama           758                754              751   \n",
       "4       Akron town  Alabama           356                356              355   \n",
       "\n",
       "   POPESTIMATE2011  POPESTIMATE2012  POPESTIMATE2013  POPESTIMATE2014  \\\n",
       "0          4799069          4815588          4830081          4841799   \n",
       "1             2694             2643             2628             2608   \n",
       "2             4493             4471             4449             4420   \n",
       "3              750              743              742              739   \n",
       "4              347              347              343              338   \n",
       "\n",
       "   POPESTIMATE2015  POPESTIMATE2016  POPESTIMATE2017  POPESTIMATE2018  \\\n",
       "0          4852347          4863525          4874486          4887681   \n",
       "1             2600             2584             2575             2571   \n",
       "2             4390             4356             4327             4308   \n",
       "3              734              731              726              723   \n",
       "4              339              333              332              331   \n",
       "\n",
       "   POPESTIMATE2019  \n",
       "0          4903185  \n",
       "1             2560  \n",
       "2             4281  \n",
       "3              718  \n",
       "4              328  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_town_est2010_2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "13504fe0-ba37-443c-858e-33db235bf639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3740 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/robynwhite/opt/anaconda3/lib/python3.9/site-packages/dataprep/eda/distribution/render.py:274: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/Users/robynwhite/opt/anaconda3/lib/python3.9/site-packages/dataprep/eda/distribution/render.py:274: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/Users/robynwhite/opt/anaconda3/lib/python3.9/site-packages/dataprep/eda/distribution/render.py:274: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create data profile report using the DataPrep Library and view in browser\n",
    "\n",
    "city_town_est2010_2019report = create_report(city_town_est2010_2019, title='2010-2019 City and Town Population Data Profile Report')\n",
    "city_town_est2010_2019report.show_browser()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f51d1f8-3a43-426b-9359-d6fab9610a48",
   "metadata": {},
   "source": [
    "A review of the [2010 - 2019 profile report](https://drive.google.com/file/d/1DzhwrkeVAPBH12K26dosXdrAehyk4TsQ/view?usp=share_link) shows that there are 22 columns: 15 numerical and 7 categorical. There are 81,434 observations, and no missing values. No duplicate rows are present.\n",
    "\n",
    "Columns of interest are: \n",
    "\n",
    ">**SUMLEV**: geographic summary level\n",
    "\n",
    ">**NAME**: area name\n",
    "\n",
    ">**STNAME**: State name\n",
    "\n",
    ">**POPESTIMATE2017**: 7/1/2017 resident population estimate\n",
    "\n",
    ">**POPESTIMATE2018**: 7/1/2018 resident population estimate\n",
    "\n",
    ">**POPESTIMATE2019**: 7/1/2019 resident population estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a1449c-a567-43f2-b171-fc41ec5a9f85",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### 2020 - 2021 City and Town Level Population Data Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "e64a6aef-33cf-49d3-bf93-a3a739ac3692",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create 2020 - 2021 Pop. DataFrame\n",
    "\n",
    "city_town_est2020_2021 = pd.read_csv(\"Population Data/city_town-est2020-2021_all.csv\",encoding='latin-1') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5375c064-fab1-4fb5-a1cf-57600a411a16",
   "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>SUMLEV</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>PLACE</th>\n",
       "      <th>COUSUB</th>\n",
       "      <th>CONCIT</th>\n",
       "      <th>PRIMGEO_FLAG</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>NAME</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>ESTIMATESBASE2020</th>\n",
       "      <th>POPESTIMATE2020</th>\n",
       "      <th>POPESTIMATE2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>5024279</td>\n",
       "      <td>5024803</td>\n",
       "      <td>5039877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Abbeville city</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2368</td>\n",
       "      <td>2368</td>\n",
       "      <td>2379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Adamsville city</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4372</td>\n",
       "      <td>4356</td>\n",
       "      <td>4294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>484</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Addison town</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>664</td>\n",
       "      <td>664</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>676</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Akron town</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUMLEV  STATE  COUNTY  PLACE  COUSUB  CONCIT  PRIMGEO_FLAG FUNCSTAT  \\\n",
       "0      40      1       0      0       0       0             0        A   \n",
       "1     162      1       0    124       0       0             0        A   \n",
       "2     162      1       0    460       0       0             0        A   \n",
       "3     162      1       0    484       0       0             0        A   \n",
       "4     162      1       0    676       0       0             0        A   \n",
       "\n",
       "              NAME   STNAME  ESTIMATESBASE2020  POPESTIMATE2020  \\\n",
       "0          Alabama  Alabama            5024279          5024803   \n",
       "1   Abbeville city  Alabama               2368             2368   \n",
       "2  Adamsville city  Alabama               4372             4356   \n",
       "3     Addison town  Alabama                664              664   \n",
       "4       Akron town  Alabama                227              227   \n",
       "\n",
       "   POPESTIMATE2021  \n",
       "0          5039877  \n",
       "1             2379  \n",
       "2             4294  \n",
       "3              668  \n",
       "4              226  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_town_est2020_2021.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "493e784b-fd11-4c4e-82b7-103d5462e67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1922 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/robynwhite/opt/anaconda3/lib/python3.9/site-packages/dataprep/eda/distribution/render.py:274: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/Users/robynwhite/opt/anaconda3/lib/python3.9/site-packages/dataprep/eda/distribution/render.py:274: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create data profile report using the DataPrep Library and view in browser\n",
    "\n",
    "city_town_est2020_2021report = create_report(city_town_est2020_2021, title='2020-2021 City and Town Population Data Profile Report')\n",
    "city_town_est2020_2021report.show_browser()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc60773-bf0a-4e61-ba64-4b87fdf2c4fb",
   "metadata": {},
   "source": [
    "A review of the [2020 - 2021 profile report](https://drive.google.com/file/d/1__QI5cEHURKOzFrMjwL2uI81mX0QJqVw/view?usp=share_link) shows that there are 13 columns, 7 numerical and 6 categorical. There are 81,416 observations, and no missing values. No duplicate rows are present.\n",
    "\n",
    "Columns of interest are: \n",
    "\n",
    ">**SUMLEV**: geographic summary level\n",
    "\n",
    ">**NAME**: area name\n",
    "\n",
    ">**STNAME**: State name\n",
    "\n",
    ">**POPESTIMATE2020**: 7/1/2020 resident population estimate\n",
    "\n",
    ">**POPESTIMATE2021**: 7/1/2021 resident population estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e297c8-c849-409e-aea2-9b0afe6bbaaf",
   "metadata": {},
   "source": [
    "### Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f0a6af-852c-4eaa-aad0-03e1a3d783b3",
   "metadata": {},
   "source": [
    "#### Cleaning and Merging Population Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9eb01ae-e867-465b-b244-040143aeb3b1",
   "metadata": {},
   "source": [
    "Now that we taken a preliminary look at our data, and identified our columns of interest, we will clean the data sets and merge them into one dataframe. Our cleaning steps will be to:\n",
    "\n",
    "- convert column names to lowercase\n",
    "- update column names\n",
    "- drop unused columns\n",
    "- add columns for state abbreviations and state-county keys (*these will help with merging with other data later*)\n",
    "- subset data for state and county levels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50faff56-d9a4-4cd7-b895-0410154b0bc3",
   "metadata": {},
   "source": [
    "First we'll create a dictionary to map State names to their abbreviations and vice versa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "276488e3-448f-4447-a542-b004fccab26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create abbreviation to state name dictionary\n",
    "abbrev_state = {\n",
    "            'AL': 'Alabama',\n",
    "            'AK': 'Alaska',\n",
    "            'AZ': 'Arizona',\n",
    "            'AR': 'Arkansas',\n",
    "            'CA': 'California',\n",
    "            'CO': 'Colorado',\n",
    "            'CT': 'Connecticut',\n",
    "            'DE': 'Delaware',\n",
    "            'FL': 'Florida',\n",
    "            'GA': 'Georgia',\n",
    "            'HI': 'Hawaii',\n",
    "            'ID': 'Idaho',\n",
    "            'IL': 'Illinois',\n",
    "            'IN': 'Indiana',\n",
    "            'IA': 'Iowa',\n",
    "            'KS': 'Kansas',\n",
    "            'KY': 'Kentucky',\n",
    "            'LA': 'Louisiana',\n",
    "            'ME': 'Maine',\n",
    "            'MD': 'Maryland',\n",
    "            'MA': 'Massachusetts',\n",
    "            'MI': 'Michigan',\n",
    "            'MN': 'Minnesota',\n",
    "            'MS': 'Mississippi',\n",
    "            'MO': 'Missouri',\n",
    "            'MT': 'Montana',\n",
    "            'NE': 'Nebraska',\n",
    "            'NV': 'Nevada',\n",
    "            'NH': 'New Hampshire',\n",
    "            'NJ': 'New Jersey',\n",
    "            'NM': 'New Mexico',\n",
    "            'NY': 'New York',\n",
    "            'NC': 'North Carolina',\n",
    "            'ND': 'North Dakota',\n",
    "            'OH': 'Ohio',\n",
    "            'OK': 'Oklahoma',\n",
    "            'OR': 'Oregon',\n",
    "            'PA': 'Pennsylvania',\n",
    "            'RI': 'Rhode Island',\n",
    "            'SC': 'South Carolina',\n",
    "            'SD': 'South Dakota',\n",
    "            'TN': 'Tennessee',\n",
    "            'TX': 'Texas',\n",
    "            'UT': 'Utah',\n",
    "            'VT': 'Vermont',\n",
    "            'VA': 'Virginia',\n",
    "            'WA': 'Washington',\n",
    "            'WV': 'West Virginia',\n",
    "            'WI': 'Wisconsin',\n",
    "            'WY': 'Wyoming',\n",
    "            'DC': 'District of Columbia',\n",
    "            }\n",
    "\n",
    "# create inverse, state to abbreviation dictionary\n",
    "state_abbrev = {v: k for k, v in abbrev_state.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d399845c-dd00-454f-9470-6e7d0e62bc09",
   "metadata": {},
   "source": [
    "Next we'll create a dictionary to map state-county combinations to their metro areas. We will use the cleaned FMR data to create the dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b4f7f691-3a20-4ab4-8016-95fd4192c8d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in fmr data\n",
    "metro_dict = pd.read_csv(\"FMR Data/FWT_fmr_data.csv\")\n",
    "\n",
    "# drop unused columns\n",
    "metro_dict = metro_dict.drop(columns=(['year','type','fmr']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "4ae26b62-f80b-4633-90d1-25053427445d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load population data csvs and create dataframes\n",
    "pop_2019 = pd.read_csv(\"Population Data/city_town-est2010-2019_all.csv\",encoding='latin-1') \n",
    "pop_2021 = pd.read_csv(\"Population Data/city_town-est2020-2021_all.csv\",encoding='latin-1') \n",
    "\n",
    "# drop unused columns\n",
    "pop_2019 = pop_2019.drop(columns=['COUSUB','CONCIT','PRIMGEO_FLAG','FUNCSTAT','CENSUS2010POP','ESTIMATESBASE2010','PLACE','STATE','COUNTY','POPESTIMATE2010', \n",
    "                                  'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013', 'POPESTIMATE2014','POPESTIMATE2015', 'POPESTIMATE2016']) \n",
    "\n",
    "pop_2021 = pop_2021.drop(columns=['COUSUB','CONCIT','PRIMGEO_FLAG','FUNCSTAT','PLACE','STATE','COUNTY'])\n",
    "\n",
    "# merge into one dataset for years 2010 - 2021\n",
    "pop_data = pd.merge(pop_2019, pop_2021) \n",
    "\n",
    "# change column names to lowercase\n",
    "pop_data.columns = pop_data.columns.str.lower().str.strip() \n",
    "\n",
    "# change column names\n",
    "pop_data = pop_data.rename(columns={'name':'county_name', 'stname':'state'}) \n",
    "\n",
    "# map state abbreviations from dictionary to create state_alpha column\n",
    "pop_data['state_alpha'] = pop_data['state'].map(state_abbrev) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "dd43ae0b-51a3-4fa1-8116-73abaaee4e13",
   "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>sumlev</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state</th>\n",
       "      <th>popestimate2017</th>\n",
       "      <th>popestimate2018</th>\n",
       "      <th>popestimate2019</th>\n",
       "      <th>estimatesbase2020</th>\n",
       "      <th>popestimate2020</th>\n",
       "      <th>popestimate2021</th>\n",
       "      <th>state_alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4874486</td>\n",
       "      <td>4887681</td>\n",
       "      <td>4903185</td>\n",
       "      <td>5024279</td>\n",
       "      <td>5024803</td>\n",
       "      <td>5039877</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162</td>\n",
       "      <td>Abbeville city</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2575</td>\n",
       "      <td>2571</td>\n",
       "      <td>2560</td>\n",
       "      <td>2368</td>\n",
       "      <td>2368</td>\n",
       "      <td>2379</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>Adamsville city</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4327</td>\n",
       "      <td>4308</td>\n",
       "      <td>4281</td>\n",
       "      <td>4372</td>\n",
       "      <td>4356</td>\n",
       "      <td>4294</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162</td>\n",
       "      <td>Addison town</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>726</td>\n",
       "      <td>723</td>\n",
       "      <td>718</td>\n",
       "      <td>664</td>\n",
       "      <td>664</td>\n",
       "      <td>668</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162</td>\n",
       "      <td>Akron town</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>332</td>\n",
       "      <td>331</td>\n",
       "      <td>328</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>226</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sumlev      county_name    state  popestimate2017  popestimate2018  \\\n",
       "0      40          Alabama  Alabama          4874486          4887681   \n",
       "1     162   Abbeville city  Alabama             2575             2571   \n",
       "2     162  Adamsville city  Alabama             4327             4308   \n",
       "3     162     Addison town  Alabama              726              723   \n",
       "4     162       Akron town  Alabama              332              331   \n",
       "\n",
       "   popestimate2019  estimatesbase2020  popestimate2020  popestimate2021  \\\n",
       "0          4903185            5024279          5024803          5039877   \n",
       "1             2560               2368             2368             2379   \n",
       "2             4281               4372             4356             4294   \n",
       "3              718                664              664              668   \n",
       "4              328                227              227              226   \n",
       "\n",
       "  state_alpha  \n",
       "0          AL  \n",
       "1          AL  \n",
       "2          AL  \n",
       "3          AL  \n",
       "4          AL  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305c19cb-dc40-476c-aedc-ee9c05f9aaae",
   "metadata": {},
   "source": [
    "Since our data contains both state, county, and other geographic level data, we will subset the dataframe into to one dataframe containing state level values, and one containing county level values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7ae0b35c-1b83-4477-a1ef-fbb3fde9faf0",
   "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>state_alpha</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>2017</td>\n",
       "      <td>4874486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>2017</td>\n",
       "      <td>739700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>2017</td>\n",
       "      <td>7044008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>2017</td>\n",
       "      <td>3001345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>2017</td>\n",
       "      <td>39358497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_alpha  year  population\n",
       "0          AL  2017     4874486\n",
       "1          AK  2017      739700\n",
       "2          AZ  2017     7044008\n",
       "3          AR  2017     3001345\n",
       "4          CA  2017    39358497"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset for state level data\n",
    "sumlev_s = [40]\n",
    "state_pop_data = pop_data[pop_data['sumlev'].astype(int).isin(sumlev_s)].drop(columns=['state','county_name','estimatesbase2020','sumlev']).copy()\n",
    "\n",
    "# reshape from wide to long format\n",
    "state_pop_data = (state_pop_data.melt(id_vars=['state_alpha'], var_name='year',value_name='population').reset_index(drop=True))\n",
    "\n",
    "# change year_data to datetime dtype\n",
    "state_pop_data['year'] = pd.to_datetime(state_pop_data['year'].str[-4:]).dt.strftime('%Y') \n",
    "\n",
    "state_pop_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3e11f0b2-d0c4-4f47-92ca-6c6d126a0657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 255 entries, 0 to 254\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   state_alpha  255 non-null    object\n",
      " 1   year         255 non-null    object\n",
      " 2   population   255 non-null    int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 6.1+ KB\n"
     ]
    }
   ],
   "source": [
    "state_pop_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "3d214a06-2e98-4ca6-b9c7-175ee0cea7c4",
   "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>state_alpha</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st_county</th>\n",
       "      <th>metro_name</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>AL-Autauga County</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>2017</td>\n",
       "      <td>55390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>AL-Baldwin County</td>\n",
       "      <td>Daphne-Fairhope-Foley, AL</td>\n",
       "      <td>2017</td>\n",
       "      <td>212521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>AL-Barbour County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>25157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>AL-Bibb County</td>\n",
       "      <td>Birmingham-Hoover, AL</td>\n",
       "      <td>2017</td>\n",
       "      <td>22550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>AL-Blount County</td>\n",
       "      <td>Birmingham-Hoover, AL</td>\n",
       "      <td>2017</td>\n",
       "      <td>57787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_alpha     county_name          st_county                 metro_name  \\\n",
       "0          AL  Autauga County  AL-Autauga County             Montgomery, AL   \n",
       "1          AL  Baldwin County  AL-Baldwin County  Daphne-Fairhope-Foley, AL   \n",
       "2          AL  Barbour County  AL-Barbour County                        NaN   \n",
       "3          AL     Bibb County     AL-Bibb County      Birmingham-Hoover, AL   \n",
       "4          AL   Blount County   AL-Blount County      Birmingham-Hoover, AL   \n",
       "\n",
       "   year  population  \n",
       "0  2017       55390  \n",
       "1  2017      212521  \n",
       "2  2017       25157  \n",
       "3  2017       22550  \n",
       "4  2017       57787  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subset for county level data\n",
    "sumlev_c = [50]\n",
    "\n",
    "metro_pop_data = pop_data[pop_data['sumlev'].isin(sumlev_c)].drop(columns=['estimatesbase2020','sumlev']).copy()\n",
    "\n",
    "#drop state column \n",
    "metro_pop_data = metro_pop_data.drop(columns={'state'})\n",
    "\n",
    "#create state-county key\n",
    "metro_pop_data['st_county'] = metro_pop_data[['state_alpha','county_name']].apply('-'.join, axis=1) \n",
    "\n",
    "#map metro from fmr data using st_county key\n",
    "area_name = dict(zip(metro_dict.st_county, metro_dict.metro_name)) #create dictionary from fmr dataframe\n",
    "metro_pop_data['metro_name'] = metro_pop_data['st_county'].map(area_name) #map values\n",
    "\n",
    "#reshape from wide to long format\n",
    "metro_pop_data = (metro_pop_data.melt(id_vars=['state_alpha','county_name', 'st_county', 'metro_name'], var_name='year', value_name='population').reset_index(drop=True))\n",
    "\n",
    "#change year_data to datetime dtype\n",
    "metro_pop_data['year'] = pd.to_datetime(metro_pop_data['year'].str[-4:]).dt.strftime('%Y') \n",
    "\n",
    "metro_pop_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "fd7286d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_alpha       0\n",
       "county_name       0\n",
       "st_county         0\n",
       "metro_name     9865\n",
       "year              0\n",
       "population        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_pop_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac7af05-a29a-4f0d-80b4-db6a8ae3482f",
   "metadata": {},
   "source": [
    "We are seeing some NaN values for the metro name. Upon further inspection, a few of these NaNs are caused by 'ñ' in the county names, as is the case with Doña Ana County. The majority of the NaNs though are due to the fact that the counties are a part of smaller, non-metropolitan areas. We will update the metro name for Doña Ana County, and then drop the counties in non-metropolitan areas for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "88cb7fc3-1021-4d05-a80a-e767cc76da45",
   "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>state_alpha</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st_county</th>\n",
       "      <th>metro_name</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14365</th>\n",
       "      <td>NM</td>\n",
       "      <td>Doña Ana County</td>\n",
       "      <td>NM-Doña Ana County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>221508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11224</th>\n",
       "      <td>NM</td>\n",
       "      <td>Doña Ana County</td>\n",
       "      <td>NM-Doña Ana County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>219899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8083</th>\n",
       "      <td>NM</td>\n",
       "      <td>Doña Ana County</td>\n",
       "      <td>NM-Doña Ana County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>218195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4942</th>\n",
       "      <td>NM</td>\n",
       "      <td>Doña Ana County</td>\n",
       "      <td>NM-Doña Ana County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>217278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>NM</td>\n",
       "      <td>Doña Ana County</td>\n",
       "      <td>NM-Doña Ana County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>216174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13109</th>\n",
       "      <td>HI</td>\n",
       "      <td>Hawaii County</td>\n",
       "      <td>HI-Hawaii County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>202906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6827</th>\n",
       "      <td>HI</td>\n",
       "      <td>Hawaii County</td>\n",
       "      <td>HI-Hawaii County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>201513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3686</th>\n",
       "      <td>HI</td>\n",
       "      <td>Hawaii County</td>\n",
       "      <td>HI-Hawaii County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>201509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9968</th>\n",
       "      <td>HI</td>\n",
       "      <td>Hawaii County</td>\n",
       "      <td>HI-Hawaii County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>200746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>HI</td>\n",
       "      <td>Hawaii County</td>\n",
       "      <td>HI-Hawaii County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>199981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      state_alpha      county_name           st_county metro_name  year  \\\n",
       "14365          NM  Doña Ana County  NM-Doña Ana County        NaN  2021   \n",
       "11224          NM  Doña Ana County  NM-Doña Ana County        NaN  2020   \n",
       "8083           NM  Doña Ana County  NM-Doña Ana County        NaN  2019   \n",
       "4942           NM  Doña Ana County  NM-Doña Ana County        NaN  2018   \n",
       "1801           NM  Doña Ana County  NM-Doña Ana County        NaN  2017   \n",
       "13109          HI    Hawaii County    HI-Hawaii County        NaN  2021   \n",
       "6827           HI    Hawaii County    HI-Hawaii County        NaN  2019   \n",
       "3686           HI    Hawaii County    HI-Hawaii County        NaN  2018   \n",
       "9968           HI    Hawaii County    HI-Hawaii County        NaN  2020   \n",
       "545            HI    Hawaii County    HI-Hawaii County        NaN  2017   \n",
       "\n",
       "       population  \n",
       "14365      221508  \n",
       "11224      219899  \n",
       "8083       218195  \n",
       "4942       217278  \n",
       "1801       216174  \n",
       "13109      202906  \n",
       "6827       201513  \n",
       "3686       201509  \n",
       "9968       200746  \n",
       "545        199981  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_pop_data[metro_pop_data['metro_name'].isna()].sort_values(by='population', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e15029d3-ef16-4374-8cff-3335afb136b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add metro name for Doña Ana County\n",
    "metro_pop_data.loc[metro_pop_data.county_name == 'Doña Ana County', 'metro_name'] = 'Las Cruces, NM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "43cfb3be-d16e-48f1-918b-bd1a9f5b1f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_alpha       0\n",
       "county_name       0\n",
       "st_county         0\n",
       "metro_name     9860\n",
       "year              0\n",
       "population        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_pop_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1eb072c8-1644-48ea-94fe-3b349b6a8329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop duplicates\n",
    "metro_pop_data = metro_pop_data.dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "01460130-9f0c-49b3-a54b-d0ae6fac4243",
   "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>state_alpha</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st_county</th>\n",
       "      <th>metro_name</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [state_alpha, county_name, st_county, metro_name, year, population]\n",
       "Index: []"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_pop_data[metro_pop_data['metro_name'].isna()].sort_values(by='population', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8b03aee6-34cf-4f44-b1c0-f1267a5971ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_alpha    0\n",
       "county_name    0\n",
       "st_county      0\n",
       "metro_name     0\n",
       "year           0\n",
       "population     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_pop_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "eeeb5055-9627-4359-bbd2-59f0685751a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5845 entries, 0 to 15694\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   state_alpha  5845 non-null   object\n",
      " 1   county_name  5845 non-null   object\n",
      " 2   st_county    5845 non-null   object\n",
      " 3   metro_name   5845 non-null   object\n",
      " 4   year         5845 non-null   object\n",
      " 5   population   5845 non-null   int64 \n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 319.6+ KB\n"
     ]
    }
   ],
   "source": [
    "metro_pop_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f97b1a5d-52f8-472c-882b-143964102317",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export cleaned data as csvs\n",
    "\n",
    "state_pop_data.to_csv('Population Data/FWT_State_Pop.csv', index=False)\n",
    "metro_pop_data.to_csv('Population Data/FWT_Metro_Pop.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c13bb9-d597-4d78-9989-fb14b1f08990",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis\n",
    "\n",
    "Now we will conduct EDA by plotting our metro and state level data to answer our initial questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0155a817-c0a8-4a40-9040-a6bb1b71f477",
   "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>state_alpha</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>TX</td>\n",
       "      <td>1232668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>FL</td>\n",
       "      <td>817515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>GA</td>\n",
       "      <td>389236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>NJ</td>\n",
       "      <td>381605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>WA</td>\n",
       "      <td>315330.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state_alpha    change\n",
       "219          TX 1232668.0\n",
       "49           FL  817515.0\n",
       "54           GA  389236.0\n",
       "159          NJ  381605.0\n",
       "239          WA  315330.0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate state population changes from 2017 to 2021 \n",
    "\n",
    "# 1 - make copy of state population dataframe\n",
    "pop_chg_st = state_pop_data\\\n",
    "             .query(\"year >= '2017'\")\\\n",
    "             .copy()\n",
    "\n",
    "# 2 - group by state and year, sum population\n",
    "pop_chg_st = pop_chg_st\\\n",
    "             .groupby(['state_alpha','year'])\\\n",
    "             .agg('sum')\\\n",
    "             .reset_index()\n",
    "\n",
    "# 3 calculate change over four periods (2017 to 2021)\n",
    "pop_chg_st['change'] = pop_chg_st\\\n",
    "                       .groupby('state_alpha')['population']\\\n",
    "                       .diff(periods=4)\n",
    "\n",
    "# 4 - drop nulls and unused columns, sort values\n",
    "pop_chg_st = pop_chg_st[['state_alpha','change']]\\\n",
    "             .dropna()\\\n",
    "             .sort_values(by='change',ascending=False)\n",
    "\n",
    "pop_chg_st.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b9d81157-def4-4d3d-ae5a-1d1cf978cbb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>Increase=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "texttemplate": "%{y:.3s}",
         "type": "bar",
         "x": [
          "TX",
          "FL",
          "GA",
          "NJ",
          "WA",
          "NC",
          "TN",
          "NY",
          "UT",
          "AZ",
          "CO",
          "ID",
          "VA",
          "PA",
          "NV",
          "SC",
          "AL",
          "IN",
          "MD",
          "MN",
          "MA",
          "OH",
          "WI",
          "OR",
          "MI",
          "MO",
          "KY",
          "OK",
          "MT",
          "IA",
          "NE",
          "DE",
          "NH",
          "RI",
          "ME",
          "CT",
          "KS",
          "AR",
          "NM",
          "SD",
          "VT",
          "ND",
          "HI",
          "WY",
          "AK",
          "DC",
          "WV",
          "MS",
          "LA",
          "IL",
          "CA"
         ],
         "xaxis": "x",
         "y": [
          1232668,
          817515,
          389236,
          381605,
          315330,
          282929,
          266419,
          246341,
          236933,
          232308,
          200184,
          183208,
          178687,
          176415,
          174086,
          169437,
          165391,
          147907,
          141261,
          141160,
          124934,
          120367,
          105722,
          102530,
          77697,
          61517,
          57126,
          55323,
          51789,
          51529,
          47745,
          46561,
          40205,
          39937,
          37635,
          32300,
          25864,
          24546,
          24093,
          22508,
          21226,
          20006,
          17160,
          -128,
          -7027,
          -24856,
          -34045,
          -38545,
          -46513,
          -107359,
          -120661
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Total State Population Change: 2017 to 2021"
        },
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -0.5,
          50.5
         ],
         "title": {
          "text": "State"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -195845.94444444444,
          1307852.9444444445
         ],
         "title": {
          "text": "Increase"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"fa02e581-6e9a-4beb-aae8-a041a3d43a86\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"fa02e581-6e9a-4beb-aae8-a041a3d43a86\")) {                    Plotly.newPlot(                        \"fa02e581-6e9a-4beb-aae8-a041a3d43a86\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"State=%{x}<br>Increase=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"texttemplate\":\"%{y:.3s}\",\"x\":[\"TX\",\"FL\",\"GA\",\"NJ\",\"WA\",\"NC\",\"TN\",\"NY\",\"UT\",\"AZ\",\"CO\",\"ID\",\"VA\",\"PA\",\"NV\",\"SC\",\"AL\",\"IN\",\"MD\",\"MN\",\"MA\",\"OH\",\"WI\",\"OR\",\"MI\",\"MO\",\"KY\",\"OK\",\"MT\",\"IA\",\"NE\",\"DE\",\"NH\",\"RI\",\"ME\",\"CT\",\"KS\",\"AR\",\"NM\",\"SD\",\"VT\",\"ND\",\"HI\",\"WY\",\"AK\",\"DC\",\"WV\",\"MS\",\"LA\",\"IL\",\"CA\"],\"xaxis\":\"x\",\"y\":[1232668.0,817515.0,389236.0,381605.0,315330.0,282929.0,266419.0,246341.0,236933.0,232308.0,200184.0,183208.0,178687.0,176415.0,174086.0,169437.0,165391.0,147907.0,141261.0,141160.0,124934.0,120367.0,105722.0,102530.0,77697.0,61517.0,57126.0,55323.0,51789.0,51529.0,47745.0,46561.0,40205.0,39937.0,37635.0,32300.0,25864.0,24546.0,24093.0,22508.0,21226.0,20006.0,17160.0,-128.0,-7027.0,-24856.0,-34045.0,-38545.0,-46513.0,-107359.0,-120661.0],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"State\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Increase\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Total State Population Change: 2017 to 2021\"},\"barmode\":\"relative\",\"height\":600},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('fa02e581-6e9a-4beb-aae8-a041a3d43a86');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar chart\n",
    "\n",
    "fig = px.bar(pop_chg_st,\n",
    "             x='state_alpha',\n",
    "             y='change',\n",
    "             text_auto='.3s',\n",
    "             labels={\"change\": \"Increase\",\"state_alpha\": \"State\"},\n",
    "             title=\"Total State Population Change: 2017 to 2021\", height=600)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6c10a377-d015-4575-9f87-6776de009943",
   "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>state_alpha</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>ID</td>\n",
       "      <td>10.665797294661793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>UT</td>\n",
       "      <td>7.640431829043259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>NV</td>\n",
       "      <td>5.861668975943668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>MT</td>\n",
       "      <td>4.920654224965371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>DE</td>\n",
       "      <td>4.866208274675676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state_alpha         pct_change\n",
       "216          ID 10.665797294661793\n",
       "248          UT  7.640431829043259\n",
       "232          NV  5.861668975943668\n",
       "230          MT  4.920654224965371\n",
       "211          DE  4.866208274675676"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate population percent change from 2017 to 2021 by state\n",
    "\n",
    "# 1 - make copy of state population dataframe\n",
    "pct_inc_st = state_pop_data.copy()\n",
    "\n",
    "# 2 - calculate percentage change over four periods (2017 to 2021)\n",
    "pct_inc_st['pct_change'] = pct_inc_st\\\n",
    "                           .groupby('state_alpha')['population']\\\n",
    "                           .pct_change(periods=4)\\\n",
    "                           .mul(100)\n",
    "\n",
    "# 3 - drop unused columns and null values; sort values\n",
    "pct_inc_st = pct_inc_st[['state_alpha','pct_change']]\\\n",
    "             .dropna()\\\n",
    "             .sort_values(by='pct_change',ascending=False)\n",
    "\n",
    "pct_inc_st.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "44917ac3-522e-48c2-8c88-d00f468d81d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "=%{x}<br>Percent Change=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "texttemplate": "%{y:.3s}",
         "type": "bar",
         "x": [
          "ID",
          "UT",
          "NV",
          "MT",
          "DE",
          "TX",
          "NJ",
          "WA",
          "TN",
          "FL",
          "RI",
          "GA",
          "CO",
          "VT",
          "AL",
          "SC",
          "AZ",
          "NH",
          "ME",
          "NC",
          "ND",
          "SD",
          "MN",
          "NE",
          "OR",
          "MD",
          "IN",
          "VA",
          "WI",
          "MA",
          "IA",
          "OK",
          "PA",
          "KY",
          "NY",
          "HI",
          "NM",
          "OH",
          "MO",
          "CT",
          "KS",
          "AR",
          "MI",
          "WY",
          "CA",
          "IL",
          "AK",
          "LA",
          "MS",
          "WV",
          "DC"
         ],
         "xaxis": "x",
         "y": [
          10.665797294661793,
          7.640431829043259,
          5.861668975943668,
          4.920654224965371,
          4.866208274675676,
          4.356444979343377,
          4.29468151853718,
          4.247805778567715,
          3.9711876894806375,
          3.899685612398973,
          3.7830843452470564,
          3.7389400720246213,
          3.567143660285277,
          3.399728354881293,
          3.392993640765396,
          3.374386708695898,
          3.2979519614401376,
          2.9808264759372705,
          2.8199206960524803,
          2.755381573441107,
          2.6500049010387494,
          2.57862586324622,
          2.536007315543909,
          2.4919791622628473,
          2.4744034510844948,
          2.3450215044552802,
          2.2214669158276523,
          2.111244322295036,
          1.8258826227689484,
          1.8212513533579555,
          1.6402412821696188,
          1.4072386956428762,
          1.3795742310876546,
          1.2830764006120043,
          1.2575108838518867,
          1.204723696339416,
          1.1517919632237472,
          1.0323380204380062,
          1.00737390427188,
          0.9039271014975814,
          0.8891889829127475,
          0.8178333380534308,
          0.7790645930649154,
          -0.02210971601106504,
          -0.306569125340328,
          -0.8401318180352724,
          -0.9499797215087202,
          -0.9958762974889512,
          -1.289773164553576,
          -1.873688775588822,
          -3.5768866580515923
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "height": 600,
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "State Population Percentage Increases: 2017 to 2021"
        },
        "width": 1200,
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -0.5,
          50.5
         ],
         "title": {
          "text": ""
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -4.3681468776467804,
          11.45705751425698
         ],
         "ticksuffix": "%",
         "title": {
          "text": "Percent Change"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"245e9853-15d9-4d98-8a64-2cb5e2b427c5\" class=\"plotly-graph-div\" style=\"height:600px; width:1200px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"245e9853-15d9-4d98-8a64-2cb5e2b427c5\")) {                    Plotly.newPlot(                        \"245e9853-15d9-4d98-8a64-2cb5e2b427c5\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"=%{x}<br>Percent Change=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"texttemplate\":\"%{y:.3s}\",\"x\":[\"ID\",\"UT\",\"NV\",\"MT\",\"DE\",\"TX\",\"NJ\",\"WA\",\"TN\",\"FL\",\"RI\",\"GA\",\"CO\",\"VT\",\"AL\",\"SC\",\"AZ\",\"NH\",\"ME\",\"NC\",\"ND\",\"SD\",\"MN\",\"NE\",\"OR\",\"MD\",\"IN\",\"VA\",\"WI\",\"MA\",\"IA\",\"OK\",\"PA\",\"KY\",\"NY\",\"HI\",\"NM\",\"OH\",\"MO\",\"CT\",\"KS\",\"AR\",\"MI\",\"WY\",\"CA\",\"IL\",\"AK\",\"LA\",\"MS\",\"WV\",\"DC\"],\"xaxis\":\"x\",\"y\":[10.665797294661793,7.640431829043259,5.861668975943668,4.920654224965371,4.866208274675676,4.356444979343377,4.29468151853718,4.247805778567715,3.9711876894806375,3.899685612398973,3.7830843452470564,3.7389400720246213,3.567143660285277,3.399728354881293,3.392993640765396,3.374386708695898,3.2979519614401376,2.9808264759372705,2.8199206960524803,2.755381573441107,2.6500049010387494,2.57862586324622,2.536007315543909,2.4919791622628473,2.4744034510844948,2.3450215044552802,2.2214669158276523,2.111244322295036,1.8258826227689484,1.8212513533579555,1.6402412821696188,1.4072386956428762,1.3795742310876546,1.2830764006120043,1.2575108838518867,1.204723696339416,1.1517919632237472,1.0323380204380062,1.00737390427188,0.9039271014975814,0.8891889829127475,0.8178333380534308,0.7790645930649154,-0.02210971601106504,-0.306569125340328,-0.8401318180352724,-0.9499797215087202,-0.9958762974889512,-1.289773164553576,-1.873688775588822,-3.5768866580515923],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Percent Change\"},\"ticksuffix\":\"%\"},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"State Population Percentage Increases: 2017 to 2021\"},\"barmode\":\"relative\",\"height\":600,\"width\":1200},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('245e9853-15d9-4d98-8a64-2cb5e2b427c5');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar chart\n",
    "\n",
    "fig = px.bar(pct_inc_st,\n",
    "             x='state_alpha',\n",
    "             y='pct_change',\n",
    "             text_auto='.3s',\n",
    "             labels={\"pct_change\": \"Percent Change\",\"state_alpha\": \"\"},\n",
    "             title=\"State Population Percentage Increases: 2017 to 2021\", \n",
    "             height=600, \n",
    "             width=1200)\n",
    "\n",
    "fig.update_yaxes(ticksuffix=\"%\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "451bd5fa-329f-494b-ab54-0abd980e1994",
   "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>metro_name</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>2017</td>\n",
       "      <td>170429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>2018</td>\n",
       "      <td>171150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>2019</td>\n",
       "      <td>172060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>2020</td>\n",
       "      <td>176809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abilene, TX</td>\n",
       "      <td>2021</td>\n",
       "      <td>177314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Acadia Parish, LA</td>\n",
       "      <td>2017</td>\n",
       "      <td>62559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Acadia Parish, LA</td>\n",
       "      <td>2018</td>\n",
       "      <td>62214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Acadia Parish, LA</td>\n",
       "      <td>2019</td>\n",
       "      <td>62045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Acadia Parish, LA</td>\n",
       "      <td>2020</td>\n",
       "      <td>57455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Acadia Parish, LA</td>\n",
       "      <td>2021</td>\n",
       "      <td>57288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          metro_name  year  population\n",
       "0        Abilene, TX  2017      170429\n",
       "1        Abilene, TX  2018      171150\n",
       "2        Abilene, TX  2019      172060\n",
       "3        Abilene, TX  2020      176809\n",
       "4        Abilene, TX  2021      177314\n",
       "5  Acadia Parish, LA  2017       62559\n",
       "6  Acadia Parish, LA  2018       62214\n",
       "7  Acadia Parish, LA  2019       62045\n",
       "8  Acadia Parish, LA  2020       57455\n",
       "9  Acadia Parish, LA  2021       57288"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create copy of our metro dataframe and  for easier plotting\n",
    "\n",
    "metro_pop = metro_pop_data\\\n",
    "            .groupby(['metro_name','year'],\n",
    "            group_keys=True)\\\n",
    "            .agg('sum')\\\n",
    "            .copy()\\\n",
    "            .reset_index()\n",
    "        \n",
    "metro_pop.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "b4aad1e6-267d-4ebe-9e6f-bcc2e968ba18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#top 20 metros with highest 2017 to 2021 population increases\n",
    "\n",
    "# 1 - group by metro and year, sum population\n",
    "metro_inc_pop = metro_pop\\\n",
    "                .groupby(['metro_name','year'],group_keys=True)\\\n",
    "                .agg('sum')\\\n",
    "                .reset_index()\n",
    "\n",
    "# 2 - calculate change over four periods (2017 to 2021)\n",
    "metro_inc_pop['change'] = metro_inc_pop\\\n",
    "                          .groupby('metro_name')['population']\\\n",
    "                          .diff(periods=4)\n",
    "\n",
    "# 3 - drop unused columns and null values; sort values\n",
    "metro_inc_pop = metro_inc_pop[['metro_name','change']]\\\n",
    "                .dropna()\\\n",
    "                .sort_values(by='change',ascending=False)\n",
    "\n",
    "# 4 - show first 20 rows\n",
    "metro_inc_pop = metro_inc_pop.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "619219a9-71db-4044-8f0d-9a7a31768c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Metro=%{x}<br>Increase=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "texttemplate": "%{y:.4s}",
         "type": "bar",
         "x": [
          "Dallas, TX",
          "Houston-The Woodlands-Sugar Land, TX",
          "Atlanta-Sandy Springs-Roswell, GA",
          "Austin-Round Rock, TX",
          "Phoenix-Mesa-Scottsdale, AZ",
          "Orlando-Kissimmee-Sanford, FL",
          "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
          "Washington-Arlington-Alexandria, DC-VA-MD",
          "Jacksonville, FL",
          "San Antonio-New Braunfels, TX",
          "Nashville-Davidson-Murfreesboro-Franklin, TN",
          "Charlotte-Concord-Gastonia, NC-SC",
          "Raleigh, NC",
          "Fort Worth-Arlington, TX",
          "Minneapolis-St. Paul-Bloomington, MN-WI",
          "Tampa-St. Petersburg-Clearwater, FL",
          "Las Vegas-Henderson-Paradise, NV",
          "Indianapolis-Carmel, IN",
          "Newark, NJ",
          "Sacramento-Roseville-Arden-Arcade, CA"
         ],
         "xaxis": "x",
         "y": [
          303095,
          288257,
          266066,
          236951,
          187397,
          174148,
          150079,
          138546,
          132172,
          124194,
          123204,
          114366,
          114176,
          113557,
          113176,
          112592,
          110841,
          98240,
          97351,
          93347
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top 20 Metro Area Population Increases: 2017 to 2021"
        },
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -0.5,
          19.5
         ],
         "title": {
          "text": "Metro"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          0,
          319047.36842105264
         ],
         "title": {
          "text": "Increase"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"8746bf2e-11b0-4fd3-8129-d30564e4bc4f\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"8746bf2e-11b0-4fd3-8129-d30564e4bc4f\")) {                    Plotly.newPlot(                        \"8746bf2e-11b0-4fd3-8129-d30564e4bc4f\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Metro=%{x}<br>Increase=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"texttemplate\":\"%{y:.4s}\",\"x\":[\"Dallas, TX\",\"Houston-The Woodlands-Sugar Land, TX\",\"Atlanta-Sandy Springs-Roswell, GA\",\"Austin-Round Rock, TX\",\"Phoenix-Mesa-Scottsdale, AZ\",\"Orlando-Kissimmee-Sanford, FL\",\"Philadelphia-Camden-Wilmington, PA-NJ-DE-MD\",\"Washington-Arlington-Alexandria, DC-VA-MD\",\"Jacksonville, FL\",\"San Antonio-New Braunfels, TX\",\"Nashville-Davidson-Murfreesboro-Franklin, TN\",\"Charlotte-Concord-Gastonia, NC-SC\",\"Raleigh, NC\",\"Fort Worth-Arlington, TX\",\"Minneapolis-St. Paul-Bloomington, MN-WI\",\"Tampa-St. Petersburg-Clearwater, FL\",\"Las Vegas-Henderson-Paradise, NV\",\"Indianapolis-Carmel, IN\",\"Newark, NJ\",\"Sacramento-Roseville-Arden-Arcade, CA\"],\"xaxis\":\"x\",\"y\":[303095.0,288257.0,266066.0,236951.0,187397.0,174148.0,150079.0,138546.0,132172.0,124194.0,123204.0,114366.0,114176.0,113557.0,113176.0,112592.0,110841.0,98240.0,97351.0,93347.0],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Metro\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Increase\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Top 20 Metro Area Population Increases: 2017 to 2021\"},\"barmode\":\"relative\",\"height\":600},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('8746bf2e-11b0-4fd3-8129-d30564e4bc4f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(metro_inc_pop,\n",
    "             x='metro_name',\n",
    "             y='change',\n",
    "             text_auto='.4s',\n",
    "             labels={\"change\": \"Increase\",\"metro_name\": \"Metro\"},\n",
    "             title=\"Top 20 Metro Area Population Increases: 2017 to 2021\",\n",
    "             height=600)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "fb607c6b-200f-4b3b-a872-20b1f181f8a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#top 20 metros with highest 2017 to 2021 population percent increases\n",
    "\n",
    "# 1 - make copy of metro dataframe\n",
    "pct_inc_pop = metro_pop.copy()\n",
    "\n",
    "# 2 - calculate change over four periods (2017 to 2021)\n",
    "pct_inc_pop['pct_change'] = pct_inc_pop\\\n",
    "                            .groupby('metro_name')['population']\\\n",
    "                            .pct_change(periods=4)\\\n",
    "                            .mul(100)\n",
    "\n",
    "# 3 - drop unused columns and null values; sort values\n",
    "pct_inc_pop = pct_inc_pop[['metro_name','pct_change']]\\\n",
    "              .dropna()\\\n",
    "              .sort_values(by='pct_change',ascending=False)\n",
    "\n",
    "# 4 - show first 20 rows\n",
    "pct_inc_pop = pct_inc_pop.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "2f082cd9-f042-48e1-a521-1bb24f35c310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Metro=%{x}<br>Percent Change=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "texttemplate": "%{y:.3s}",
         "type": "bar",
         "x": [
          "Walton County, FL",
          "St. George, UT",
          "Union County, OH",
          "Gem County, ID",
          "Coeur d'Alene, ID",
          "Maury County, TN",
          "Tooele County, UT",
          "Provo-Orem, UT",
          "Daphne-Fairhope-Foley, AL",
          "Morgan County, GA",
          "Idaho Falls, ID",
          "Boise City, ID",
          "Austin-Round Rock, TX",
          "Greeley, CO",
          "San Benito County, CA",
          "Hood County, TX",
          "Box Elder County, UT",
          "Logan, UT-ID",
          "Sussex County, DE",
          "Huntsville, AL"
         ],
         "xaxis": "x",
         "y": [
          17.712177121771223,
          15.245677368030908,
          14.38959118278813,
          14.331927676044142,
          14.282354436816668,
          13.576981037978243,
          13.564294816702716,
          12.799598728227357,
          12.597813863100594,
          12.311544113645013,
          12.037613606946195,
          12.002957951733961,
          11.200841418593942,
          11.164653382807256,
          10.825410544511671,
          10.779155813913377,
          10.580432406395325,
          10.442764500410306,
          10.331716797118773,
          10.273485825590267
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top 20 Metro Area Population Percentage Increases: 2017 to 2021"
        },
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -0.5,
          19.5
         ],
         "title": {
          "text": "Metro"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          0,
          18.6443969702855
         ],
         "ticksuffix": "%",
         "title": {
          "text": "Percent Change"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"88dba506-f9ce-4227-a578-e9085d91aca7\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"88dba506-f9ce-4227-a578-e9085d91aca7\")) {                    Plotly.newPlot(                        \"88dba506-f9ce-4227-a578-e9085d91aca7\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Metro=%{x}<br>Percent Change=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"texttemplate\":\"%{y:.3s}\",\"x\":[\"Walton County, FL\",\"St. George, UT\",\"Union County, OH\",\"Gem County, ID\",\"Coeur d'Alene, ID\",\"Maury County, TN\",\"Tooele County, UT\",\"Provo-Orem, UT\",\"Daphne-Fairhope-Foley, AL\",\"Morgan County, GA\",\"Idaho Falls, ID\",\"Boise City, ID\",\"Austin-Round Rock, TX\",\"Greeley, CO\",\"San Benito County, CA\",\"Hood County, TX\",\"Box Elder County, UT\",\"Logan, UT-ID\",\"Sussex County, DE\",\"Huntsville, AL\"],\"xaxis\":\"x\",\"y\":[17.712177121771223,15.245677368030908,14.38959118278813,14.331927676044142,14.282354436816668,13.576981037978243,13.564294816702716,12.799598728227357,12.597813863100594,12.311544113645013,12.037613606946195,12.002957951733961,11.200841418593942,11.164653382807256,10.825410544511671,10.779155813913377,10.580432406395325,10.442764500410306,10.331716797118773,10.273485825590267],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Metro\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Percent Change\"},\"ticksuffix\":\"%\"},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Top 20 Metro Area Population Percentage Increases: 2017 to 2021\"},\"barmode\":\"relative\",\"height\":600},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('88dba506-f9ce-4227-a578-e9085d91aca7');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(pct_inc_pop,\n",
    "             x='metro_name',\n",
    "             y='pct_change',\n",
    "             text_auto='.3s',\n",
    "             labels={\"pct_change\": \"Percent Change\", \"metro_name\": \"Metro\"},\n",
    "             title=\"Top 20 Metro Area Population Percentage Increases: 2017 to 2021\",\n",
    "             height=600)\n",
    "\n",
    "fig.update_yaxes(ticksuffix=\"%\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b89234cb-9eb8-4004-be3e-b8428a483a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate top 20 metros with highest 2017 to 2021 population decreases\n",
    "\n",
    "# 1 - group by metro and year, sum population\n",
    "metro_dec_pop = metro_pop\\\n",
    "                .groupby(['metro_name','year'],group_keys=True)\\\n",
    "                .agg('sum')\\\n",
    "                .reset_index()\n",
    "\n",
    "# 2 - calculate change over four periods (2017 to 2021)\n",
    "metro_dec_pop['change'] = metro_dec_pop\\\n",
    "                          .groupby('metro_name')['population']\\\n",
    "                          .diff(periods=4)\n",
    "\n",
    "# 3 - drop unused columns and null values; sort values\n",
    "metro_dec_pop = metro_dec_pop[['metro_name','change']]\\\n",
    "                .dropna()\\\n",
    "                .sort_values(by='change')\n",
    "\n",
    "# 4 - show first 20 rows\n",
    "metro_dec_pop = metro_dec_pop.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "aef2cfcc-d3bd-4fea-ba9c-ae420de7a7ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Metro=%{x}<br>Increase=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "texttemplate": "%{y:.4s}",
         "type": "bar",
         "x": [
          "Los Angeles-Long Beach-Glendale, CA",
          "San Francisco, CA",
          "Miami-Miami Beach-Kendall, FL",
          "San Jose-Sunnyvale-Santa Clara, CA",
          "San Diego-Carlsbad, CA",
          "Chicago-Joliet-Naperville, IL",
          "Chico, CA",
          "Santa Rosa, CA",
          "Shreveport-Bossier City, LA",
          "Oxnard-Thousand Oaks-Ventura, CA",
          "Milwaukee-Waukesha-West Allis, WI",
          "Lansing-East Lansing, MI",
          "Jackson, MS",
          "Santa Cruz-Watsonville, CA",
          "New Orleans-Metairie, LA",
          "Peoria, IL",
          "Bloomington, IN",
          "Santa Ana-Anaheim-Irvine, CA",
          "Greenville, NC",
          "Charleston, WV"
         ],
         "xaxis": "x",
         "y": [
          -274167,
          -93395,
          -50518,
          -47002,
          -35168,
          -30895,
          -20387,
          -16582,
          -11006,
          -9260,
          -7957,
          -7576,
          -7362,
          -7064,
          -6962,
          -6782,
          -6763,
          -6480,
          -6438,
          -6234
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top 20 Metro Area Population Decreases: 2017 to 2021"
        },
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -0.5,
          19.5
         ],
         "title": {
          "text": "Metro"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -288596.84210526315,
          0
         ],
         "title": {
          "text": "Increase"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"49d968ba-f18a-4e06-8b16-c385bf42bee8\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"49d968ba-f18a-4e06-8b16-c385bf42bee8\")) {                    Plotly.newPlot(                        \"49d968ba-f18a-4e06-8b16-c385bf42bee8\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Metro=%{x}<br>Increase=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"texttemplate\":\"%{y:.4s}\",\"x\":[\"Los Angeles-Long Beach-Glendale, CA\",\"San Francisco, CA\",\"Miami-Miami Beach-Kendall, FL\",\"San Jose-Sunnyvale-Santa Clara, CA\",\"San Diego-Carlsbad, CA\",\"Chicago-Joliet-Naperville, IL\",\"Chico, CA\",\"Santa Rosa, CA\",\"Shreveport-Bossier City, LA\",\"Oxnard-Thousand Oaks-Ventura, CA\",\"Milwaukee-Waukesha-West Allis, WI\",\"Lansing-East Lansing, MI\",\"Jackson, MS\",\"Santa Cruz-Watsonville, CA\",\"New Orleans-Metairie, LA\",\"Peoria, IL\",\"Bloomington, IN\",\"Santa Ana-Anaheim-Irvine, CA\",\"Greenville, NC\",\"Charleston, WV\"],\"xaxis\":\"x\",\"y\":[-274167.0,-93395.0,-50518.0,-47002.0,-35168.0,-30895.0,-20387.0,-16582.0,-11006.0,-9260.0,-7957.0,-7576.0,-7362.0,-7064.0,-6962.0,-6782.0,-6763.0,-6480.0,-6438.0,-6234.0],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Metro\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Increase\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Top 20 Metro Area Population Decreases: 2017 to 2021\"},\"barmode\":\"relative\",\"height\":600},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('49d968ba-f18a-4e06-8b16-c385bf42bee8');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar chart\n",
    "\n",
    "fig = px.bar(metro_dec_pop,\n",
    "             x='metro_name',\n",
    "             y='change',\n",
    "             text_auto='.4s',\n",
    "             labels={\"change\": \"Increase\",\"metro_name\": \"Metro\"},\n",
    "             title=\"Top 20 Metro Area Population Decreases: 2017 to 2021\", \n",
    "             height=600)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1c85fb40-5c37-4c0a-95db-0649bb626931",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate top 20 metros with highest 2017 to 2021 population percent decreases\n",
    "\n",
    "# 1 - group by metro and year, sum population\n",
    "pct_dec_pop = metro_pop\\\n",
    "              .groupby(['metro_name','year'],group_keys=True)\\\n",
    "              .agg('sum')\\\n",
    "              .reset_index()\n",
    "\n",
    "# 2 - calculate percentage change over four periods (2017 to 2021)\n",
    "pct_dec_pop['pct_change'] = pct_dec_pop\\\n",
    "                            .groupby('metro_name')['population']\\\n",
    "                            .pct_change(periods=4)\\\n",
    "                            .mul(100)\n",
    "\n",
    "# 3 - drop unused columns and null values; sort values\n",
    "pct_dec_pop = pct_dec_pop[['metro_name','pct_change']]\\\n",
    "              .dropna()\\\n",
    "              .sort_values(by='pct_change')\n",
    "\n",
    "# 4 - show first 20 rows\n",
    "pct_dec_pop = pct_dec_pop.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "80c4c05b-d510-40ca-9410-941cfaf312de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Metro=%{x}<br>Decrease=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "texttemplate": "%{y:.3s}",
         "type": "bar",
         "x": [
          "Hudspeth County, TX",
          "Oldham County, TX",
          "Sioux County, ND",
          "Newton County, TX",
          "Pulaski County, GA",
          "Gulf County, FL",
          "Gates County, NC",
          "Iberville Parish, LA",
          "Jackson County, IL",
          "Chico, CA",
          "Long County, GA",
          "Fayette County, WV",
          "Acadia Parish, LA",
          "Yazoo County, MS",
          "Webster Parish, LA",
          "St. James Parish, LA",
          "Benton County, MS",
          "Pickens County, AL",
          "Cotton County, OK",
          "Darlington County, SC"
         ],
         "xaxis": "x",
         "y": [
          -28.605560382276284,
          -18.393536121673005,
          -15.525423728813559,
          -11.985907391429397,
          -11.304892227886597,
          -10.816516609748527,
          -9.970470731283655,
          -9.366073056585423,
          -9.220433821497654,
          -8.914454122503235,
          -8.668796592119277,
          -8.43901208521568,
          -8.425646189996638,
          -7.976551868522974,
          -7.84433577832111,
          -7.661365762394756,
          -7.377347062386431,
          -6.944169471391803,
          -6.53249189834556,
          -6.30925187739807
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top 20 Metro Area Population Percentage Decreases: 2017 to 2021"
        },
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -0.5,
          19.5
         ],
         "title": {
          "text": "Metro"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -30.111116191869773,
          0
         ],
         "ticksuffix": "%",
         "title": {
          "text": "Decrease"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"18adcf46-e58b-4696-a741-255c5d02d0d4\" class=\"plotly-graph-div\" style=\"height:600px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"18adcf46-e58b-4696-a741-255c5d02d0d4\")) {                    Plotly.newPlot(                        \"18adcf46-e58b-4696-a741-255c5d02d0d4\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Metro=%{x}<br>Decrease=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"texttemplate\":\"%{y:.3s}\",\"x\":[\"Hudspeth County, TX\",\"Oldham County, TX\",\"Sioux County, ND\",\"Newton County, TX\",\"Pulaski County, GA\",\"Gulf County, FL\",\"Gates County, NC\",\"Iberville Parish, LA\",\"Jackson County, IL\",\"Chico, CA\",\"Long County, GA\",\"Fayette County, WV\",\"Acadia Parish, LA\",\"Yazoo County, MS\",\"Webster Parish, LA\",\"St. James Parish, LA\",\"Benton County, MS\",\"Pickens County, AL\",\"Cotton County, OK\",\"Darlington County, SC\"],\"xaxis\":\"x\",\"y\":[-28.605560382276284,-18.393536121673005,-15.525423728813559,-11.985907391429397,-11.304892227886597,-10.816516609748527,-9.970470731283655,-9.366073056585423,-9.220433821497654,-8.914454122503235,-8.668796592119277,-8.43901208521568,-8.425646189996638,-7.976551868522974,-7.84433577832111,-7.661365762394756,-7.377347062386431,-6.944169471391803,-6.53249189834556,-6.30925187739807],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Metro\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Decrease\"},\"ticksuffix\":\"%\"},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Top 20 Metro Area Population Percentage Decreases: 2017 to 2021\"},\"barmode\":\"relative\",\"height\":600},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('18adcf46-e58b-4696-a741-255c5d02d0d4');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar chart\n",
    "\n",
    "fig = px.bar(pct_dec_pop,\n",
    "             x='metro_name',\n",
    "             y='pct_change',\n",
    "             text_auto='.3s',\n",
    "             labels={\"pct_change\": \"Decrease\",\"metro_name\": \"Metro\"},\n",
    "             title=\"Top 20 Metro Area Population Percentage Decreases: 2017 to 2021\", \n",
    "             height=600)\n",
    "\n",
    "fig.update_yaxes(ticksuffix=\"%\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59a8b98-31a0-48a5-9b9b-ea1ee22d03b0",
   "metadata": {},
   "source": [
    "### Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8571db8-41fa-4df1-922e-9d612adf027b",
   "metadata": {},
   "source": [
    "**1. Which states have the largest population increases and decreases?**\n",
    "\n",
    "Top ***increases*** by total number:\n",
    "\n",
    "1. Texas (+1.2M)\n",
    "\n",
    "2. Florida (+817.5K)\n",
    "\n",
    "3. Georgia (+389.2K)\n",
    "\n",
    "4. New Jersey (+381.6K)\n",
    "\n",
    "5. Washington (+315.3K)\n",
    "\n",
    "Top ***increases*** by percentage change (2017 to 2021):\n",
    "\n",
    "1. Idaho (+10.7%)\n",
    "\n",
    "2. Utah (+7.6%)\n",
    "\n",
    "3. Nevada (+5.9%)\n",
    "\n",
    "4. Montana (+4.9%)\n",
    "\n",
    "5. Delaware (+4.9%)\n",
    "\n",
    "\n",
    "Top ***decreases*** by total number:\n",
    "\n",
    "1. California (-120.7K)\n",
    "\n",
    "2. Illinois (-107.4K)\n",
    "\n",
    "3. Louisiana (-46.5K)\n",
    "\n",
    "4. Mississippi (-38.5K)\n",
    "\n",
    "5. West Virginia (-35.0K)\n",
    "\n",
    "Top ***decreases*** by percentage change (2017 to 2021):\n",
    "\n",
    "1. Washington DC (-3.6%)\n",
    "\n",
    "2. West Virginia (-1.9%)\n",
    "\n",
    "3. Mississippi (-1.3%)\n",
    "\n",
    "4. Louisiana (-1.0%)\n",
    "\n",
    "5. Arkansas (-0.9%)\n",
    "\n",
    "-----------------------------\n",
    "\n",
    "**2. Which metro areas have the largest population increases and decreases?**\n",
    "\n",
    "Top ***increases*** by total number:\n",
    "\n",
    "1. Dallas, TX (+303.1K)\n",
    "\n",
    "2. Houston-The Woodlands-Sugar Land, TX (+288.3K)\n",
    "\n",
    "3. Atlanta-Sandy Springs-Roswell, GA (+266.1K)\n",
    "\n",
    "4. Austin-Round Rock, TX (+237.0K)\n",
    "\n",
    "5. Phoenix-Mesa-Scottsdale, AZ (+187.4K)\n",
    "\n",
    "Top ***increases*** by percentage change (2017 to 2021):\n",
    "\n",
    "1. Walton County, FL (+17.7%)\n",
    "\n",
    "2. St. George, UT (+15.2%)\n",
    "\n",
    "3. Union County, OH (+14.4%)\n",
    "\n",
    "4. Gem County, ID (+14.3%)\n",
    "\n",
    "5. Coeur d'Alene, ID (+14.3%)\n",
    "\n",
    "Top ***decreases*** by total number:\n",
    "\n",
    "1. Los Angeles-Long Beach-Glendale (-274.2K)\n",
    "\n",
    "2. San Francisco, CA (-93.4K)\n",
    "\n",
    "3. Miami-Miami Beach-Kendall, FL (-50.5K)\n",
    "\n",
    "4. San Jose-Sunnyvale-Santa Clara, CA (-47.0K)\n",
    "\n",
    "5. San Diego-Carlsbad, CA (-35.1K)\n",
    "\n",
    "\n",
    "Top ***decreases*** by percentage change (2017 to 2021):\n",
    "\n",
    "1. Hudspeth County, TX (-28.6%)\n",
    "\n",
    "2. Oldham County, TX (-18.4%)\n",
    "\n",
    "3. Sioux County, ND (-15.5%)\n",
    "\n",
    "4. Newton County, TX (-12.0%)\n",
    "\n",
    "5. Pulaski County, GA (-11.3%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e970c95c-2eb8-46ce-a33e-1c02e22ab504",
   "metadata": {},
   "source": [
    "Based on the names of the metro areas with the largest population percentage increases and decreases, I believe that they may be smaller metropolitan areas. We can verify this by querying our metro_pop dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "30d2317b-5fd2-457a-9891-b8f1b8a92349",
   "metadata": {},
   "outputs": [],
   "source": [
    "metros_inc = ['Walton County, FL', 'St. Geroge, UT', 'Union County, OH', 'Gem County, ID', \"Coeur d'Alene, ID\"]\n",
    "metros_dec = ['Hudspeth County, TX', 'Oldham County, TX', 'Sioux County, ND', 'Newton County, TX', 'Pulaski County, GA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "3e87b34f-11d8-4e9d-aa3a-fad1f65baf01",
   "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>metro_name</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>Coeur d'Alene, ID</td>\n",
       "      <td>2017</td>\n",
       "      <td>157320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Coeur d'Alene, ID</td>\n",
       "      <td>2018</td>\n",
       "      <td>161209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>Coeur d'Alene, ID</td>\n",
       "      <td>2019</td>\n",
       "      <td>165697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>Coeur d'Alene, ID</td>\n",
       "      <td>2020</td>\n",
       "      <td>172646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Coeur d'Alene, ID</td>\n",
       "      <td>2021</td>\n",
       "      <td>179789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>Gem County, ID</td>\n",
       "      <td>2017</td>\n",
       "      <td>17311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>Gem County, ID</td>\n",
       "      <td>2018</td>\n",
       "      <td>17587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>Gem County, ID</td>\n",
       "      <td>2019</td>\n",
       "      <td>18112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>Gem County, ID</td>\n",
       "      <td>2020</td>\n",
       "      <td>19258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>Gem County, ID</td>\n",
       "      <td>2021</td>\n",
       "      <td>19792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>Union County, OH</td>\n",
       "      <td>2017</td>\n",
       "      <td>56798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>Union County, OH</td>\n",
       "      <td>2018</td>\n",
       "      <td>57782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>Union County, OH</td>\n",
       "      <td>2019</td>\n",
       "      <td>58988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>Union County, OH</td>\n",
       "      <td>2020</td>\n",
       "      <td>63077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>Union County, OH</td>\n",
       "      <td>2021</td>\n",
       "      <td>64971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2790</th>\n",
       "      <td>Walton County, FL</td>\n",
       "      <td>2017</td>\n",
       "      <td>68021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2791</th>\n",
       "      <td>Walton County, FL</td>\n",
       "      <td>2018</td>\n",
       "      <td>70732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2792</th>\n",
       "      <td>Walton County, FL</td>\n",
       "      <td>2019</td>\n",
       "      <td>74071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>Walton County, FL</td>\n",
       "      <td>2020</td>\n",
       "      <td>75855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>Walton County, FL</td>\n",
       "      <td>2021</td>\n",
       "      <td>80069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             metro_name  year  population\n",
       "515   Coeur d'Alene, ID  2017      157320\n",
       "516   Coeur d'Alene, ID  2018      161209\n",
       "517   Coeur d'Alene, ID  2019      165697\n",
       "518   Coeur d'Alene, ID  2020      172646\n",
       "519   Coeur d'Alene, ID  2021      179789\n",
       "925      Gem County, ID  2017       17311\n",
       "926      Gem County, ID  2018       17587\n",
       "927      Gem County, ID  2019       18112\n",
       "928      Gem County, ID  2020       19258\n",
       "929      Gem County, ID  2021       19792\n",
       "2710   Union County, OH  2017       56798\n",
       "2711   Union County, OH  2018       57782\n",
       "2712   Union County, OH  2019       58988\n",
       "2713   Union County, OH  2020       63077\n",
       "2714   Union County, OH  2021       64971\n",
       "2790  Walton County, FL  2017       68021\n",
       "2791  Walton County, FL  2018       70732\n",
       "2792  Walton County, FL  2019       74071\n",
       "2793  Walton County, FL  2020       75855\n",
       "2794  Walton County, FL  2021       80069"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_pop.query(\"metro_name == @metros_inc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a8a0b33b-85d7-4b5d-ada1-7f8d52ed0032",
   "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>metro_name</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>Hudspeth County, TX</td>\n",
       "      <td>2017</td>\n",
       "      <td>4604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>Hudspeth County, TX</td>\n",
       "      <td>2018</td>\n",
       "      <td>4788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>Hudspeth County, TX</td>\n",
       "      <td>2019</td>\n",
       "      <td>4886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>Hudspeth County, TX</td>\n",
       "      <td>2020</td>\n",
       "      <td>3196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>Hudspeth County, TX</td>\n",
       "      <td>2021</td>\n",
       "      <td>3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>Newton County, TX</td>\n",
       "      <td>2017</td>\n",
       "      <td>13908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>Newton County, TX</td>\n",
       "      <td>2018</td>\n",
       "      <td>13751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>Newton County, TX</td>\n",
       "      <td>2019</td>\n",
       "      <td>13595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>Newton County, TX</td>\n",
       "      <td>2020</td>\n",
       "      <td>12163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>Newton County, TX</td>\n",
       "      <td>2021</td>\n",
       "      <td>12241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>Oldham County, TX</td>\n",
       "      <td>2017</td>\n",
       "      <td>2104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>Oldham County, TX</td>\n",
       "      <td>2018</td>\n",
       "      <td>2118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>Oldham County, TX</td>\n",
       "      <td>2019</td>\n",
       "      <td>2112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>Oldham County, TX</td>\n",
       "      <td>2020</td>\n",
       "      <td>1761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>Oldham County, TX</td>\n",
       "      <td>2021</td>\n",
       "      <td>1717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2170</th>\n",
       "      <td>Pulaski County, GA</td>\n",
       "      <td>2017</td>\n",
       "      <td>11181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>Pulaski County, GA</td>\n",
       "      <td>2018</td>\n",
       "      <td>11080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>Pulaski County, GA</td>\n",
       "      <td>2019</td>\n",
       "      <td>11137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>Pulaski County, GA</td>\n",
       "      <td>2020</td>\n",
       "      <td>9863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>Pulaski County, GA</td>\n",
       "      <td>2021</td>\n",
       "      <td>9917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2475</th>\n",
       "      <td>Sioux County, ND</td>\n",
       "      <td>2017</td>\n",
       "      <td>4425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2476</th>\n",
       "      <td>Sioux County, ND</td>\n",
       "      <td>2018</td>\n",
       "      <td>4377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2477</th>\n",
       "      <td>Sioux County, ND</td>\n",
       "      <td>2019</td>\n",
       "      <td>4230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2478</th>\n",
       "      <td>Sioux County, ND</td>\n",
       "      <td>2020</td>\n",
       "      <td>3884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>Sioux County, ND</td>\n",
       "      <td>2021</td>\n",
       "      <td>3738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               metro_name  year  population\n",
       "1165  Hudspeth County, TX  2017        4604\n",
       "1166  Hudspeth County, TX  2018        4788\n",
       "1167  Hudspeth County, TX  2019        4886\n",
       "1168  Hudspeth County, TX  2020        3196\n",
       "1169  Hudspeth County, TX  2021        3287\n",
       "1905    Newton County, TX  2017       13908\n",
       "1906    Newton County, TX  2018       13751\n",
       "1907    Newton County, TX  2019       13595\n",
       "1908    Newton County, TX  2020       12163\n",
       "1909    Newton County, TX  2021       12241\n",
       "1960    Oldham County, TX  2017        2104\n",
       "1961    Oldham County, TX  2018        2118\n",
       "1962    Oldham County, TX  2019        2112\n",
       "1963    Oldham County, TX  2020        1761\n",
       "1964    Oldham County, TX  2021        1717\n",
       "2170   Pulaski County, GA  2017       11181\n",
       "2171   Pulaski County, GA  2018       11080\n",
       "2172   Pulaski County, GA  2019       11137\n",
       "2173   Pulaski County, GA  2020        9863\n",
       "2174   Pulaski County, GA  2021        9917\n",
       "2475     Sioux County, ND  2017        4425\n",
       "2476     Sioux County, ND  2018        4377\n",
       "2477     Sioux County, ND  2019        4230\n",
       "2478     Sioux County, ND  2020        3884\n",
       "2479     Sioux County, ND  2021        3738"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_pop.query(\"metro_name == @metros_dec\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb897cc-383c-4ac4-b3ad-796f1b50371a",
   "metadata": {},
   "source": [
    "We can see now that the metro areas with the largest population percentage decreases are very small, with some of them having less than 3,000 residents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d09e867-2b83-43b9-b731-a1f2089b4138",
   "metadata": {},
   "source": [
    "### Next Steps\n",
    "\n",
    "Next we will combine our FMR, Wage, CPI and Population data and perform comparative analysis."
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}