{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d7dfea67",
   "metadata": {},
   "source": [
    "# Other Census Bureau datasets\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mmann1123/pytidycensus/blob/main/examples/04_other_datasets.ipynb)\n",
    "\n",
    "## Population Estimates Program (PEP)\n",
    "\n",
    "  - Purpose: Provides annual population estimates between decennial censuses\n",
    "  - Frequency: Updated annually\n",
    "  - Geographic Coverage: US, regions, divisions, states, counties, metro areas, places\n",
    "  - Time Range: 2010-present (function supports 2015+)\n",
    "\n",
    "\n",
    "## Specific Datasets Within PEP\n",
    "\n",
    "  1. Population Totals (product=`\"population\"`)\n",
    "    - Basic population counts by geography\n",
    "    - Annual estimates for intercensal years\n",
    "  2. Components of Change (product=`\"components\"`)\n",
    "    - Births, deaths, migration flows\n",
    "    - Natural change calculations\n",
    "    - Domestic and international migration\n",
    "  3. Population Characteristics (product=`\"characteristics\"`)\n",
    "    - Demographics by Age, Sex, Race, Hispanic Origin (ASRH)\n",
    "    - Population breakdowns by key demographic categories\n",
    "\n",
    "\n",
    "## Key Parameters\n",
    "- **`product`**: \"population\", \"components\", \"characteristics\"\n",
    "- **`variables`**: \"POP\", \"BIRTHS\", \"DEATHS\", migration variables, rates\n",
    "- **`breakdown`**: [\"SEX\", \"RACE\", \"ORIGIN\", \"AGEGROUP\"] for demographics\n",
    "- **`vintage`**: Dataset version (e.g., 2024 for most recent)\n",
    "- **`year`**: Specific data year (defaults to vintage)\n",
    "- **`time_series`**: Get multi-year data\n",
    "- **`geometry`**: Add geographic boundaries\n",
    "- **`output`**: \"tidy\" (default) or \"wide\" format\n",
    "\n",
    "## Data Sources  \n",
    "- **2020+**: CSV files from Census Bureau FTP servers\n",
    "- **2015-2019**: Census API (requires API key)\n",
    "- **Automatic handling**: No user intervention needed\n",
    "\n",
    "## Geographic Support\n",
    "All major Census geographies: US, regions, divisions, states, counties, CBSAs, CSAs, places\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e458fe",
   "metadata": {},
   "source": [
    "\n",
    "## Mapping Migration Estimates\n",
    "\n",
    "Let's create a map of net migration rates:\n",
    "\n",
    "## Census API Key\n",
    "\n",
    "To use pytidycensus, you need a free API key from the US Census Bureau. Get one at: https://api.census.gov/data/key_signup.html\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "802905a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytidycensus as tc\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tc.set_census_api_key(\"YOUR API KEY GOES HERE\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5029d130",
   "metadata": {},
   "source": [
    "Ignore the next cell. It is just to show how users can load their credentials using the new utility function. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "733fa451",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Census API key has been set for this session.\n",
      "Using Census API key from environment\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import os\n",
    "\n",
    "# Try to get API key from environment\n",
    "api_key = os.environ.get(\"CENSUS_API_KEY\")\n",
    "\n",
    "try:\n",
    "    tc.set_census_api_key(api_key)\n",
    "    print(\"Using Census API key from environment\")\n",
    "except Exception:\n",
    "    print(\"Using example API key for documentation\")\n",
    "    tc.set_census_api_key(\"EXAMPLE_API_KEY_FOR_DOCS\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d5351c",
   "metadata": {},
   "source": [
    "\n",
    "## Product Types and Variables\n",
    "\n",
    "The `get_estimates()` function supports three main products:\n",
    "\n",
    "1. **population** (default) - Basic population totals\n",
    "2. **components** - Components of population change \n",
    "3. **characteristics** - Population by demographics\n",
    "\n",
    "### Available Variables\n",
    "\n",
    "Common variables include:\n",
    "- **POP**: Total population\n",
    "- **BIRTHS**: Births \n",
    "- **DEATHS**: Deaths\n",
    "- **DOMESTICMIG**: Domestic migration\n",
    "- **INTERNATIONALMIG**: International migration\n",
    "- **NETMIG**: Net migration\n",
    "- **NATURALCHG**: Natural change (births - deaths)\n",
    "- **RNETMIG**: Net migration rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cba2e7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2024 Population Estimates Program (vintage 2024)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/estimates.py:728: UserWarning: Using vintage 2024 data for year 2022. Consider setting vintage=2022 if available.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Multiple states specified, will filter after download\n",
      "Loading county boundaries...\n",
      "County data with geometry: (90, 8)\n",
      "Data types: <class 'geopandas.geodataframe.GeoDataFrame'>\n"
     ]
    },
    {
     "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>GEOID</th>\n",
       "      <th>NAME_x</th>\n",
       "      <th>geometry</th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>NAME_y</th>\n",
       "      <th>RNETMIG2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06091</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>POLYGON ((-120.55587 39.50874, -120.55614 39.5...</td>\n",
       "      <td>06</td>\n",
       "      <td>091</td>\n",
       "      <td>Sierra County</td>\n",
       "      <td>Sierra County, California</td>\n",
       "      <td>-19.960080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32001</td>\n",
       "      <td>Churchill</td>\n",
       "      <td>POLYGON ((-119.22613 39.90038, -119.2261 39.90...</td>\n",
       "      <td>32</td>\n",
       "      <td>001</td>\n",
       "      <td>Churchill County</td>\n",
       "      <td>Churchill County, Nevada</td>\n",
       "      <td>9.048192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32013</td>\n",
       "      <td>Humboldt</td>\n",
       "      <td>POLYGON ((-119.33142 41.0391, -119.3314 41.041...</td>\n",
       "      <td>32</td>\n",
       "      <td>013</td>\n",
       "      <td>Humboldt County</td>\n",
       "      <td>Humboldt County, Nevada</td>\n",
       "      <td>-20.804677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GEOID     NAME_x                                           geometry  \\\n",
       "0  06091     Sierra  POLYGON ((-120.55587 39.50874, -120.55614 39.5...   \n",
       "1  32001  Churchill  POLYGON ((-119.22613 39.90038, -119.2261 39.90...   \n",
       "2  32013   Humboldt  POLYGON ((-119.33142 41.0391, -119.3314 41.041...   \n",
       "\n",
       "  STATEFP COUNTYFP          NAMELSAD                     NAME_y  RNETMIG2022  \n",
       "0      06      091     Sierra County  Sierra County, California   -19.960080  \n",
       "1      32      001  Churchill County   Churchill County, Nevada     9.048192  \n",
       "2      32      013   Humboldt County    Humboldt County, Nevada   -20.804677  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get county data with geometry for mapping\n",
    "counties_geo = tc.get_estimates(\n",
    "    geography=\"county\",\n",
    "    variables=\"RNETMIG\",  # Net migration rate\n",
    "    year=2022,\n",
    "    geometry=True,  # Include geometry for mapping\n",
    "    state=[\"CA\", \"NV\", \"AZ\"],  # Western states\n",
    ")\n",
    "\n",
    "print(\"County data with geometry:\", counties_geo.shape)\n",
    "print(\"Data types:\", type(counties_geo))\n",
    "counties_geo.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5159fcfd",
   "metadata": {},
   "source": [
    "\n",
    "### Mapping with Geometry\n",
    "\n",
    "Because we set `geometry=True`, the returned GeoDataFrame includes geometry data for mapping.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f98f4b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Create a map of net migration rates\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "# Create migration categories for better visualization\n",
    "counties_geo[\"migration_category\"] = pd.cut(\n",
    "    counties_geo[\"RNETMIG2022\"],\n",
    "    bins=[-float(\"inf\"), -10, -5, 5, 10, float(\"inf\")],\n",
    "    labels=[\n",
    "        \"High Out-migration\",\n",
    "        \"Moderate Out-migration\",\n",
    "        \"Stable\",\n",
    "        \"Moderate In-migration\",\n",
    "        \"High In-migration\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "# Plot the map\n",
    "counties_geo.plot(\n",
    "    column=\"migration_category\",\n",
    "    legend=True,\n",
    "    ax=ax,\n",
    "    cmap=\"RdYlBu\",\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.1,\n",
    ")\n",
    "\n",
    "ax.set_title(\"Net Migration Rates by County: Western States (2022)\", fontsize=16)\n",
    "ax.set_axis_off()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e94038",
   "metadata": {},
   "source": [
    "\n",
    "### Geographic Levels\n",
    "\n",
    "Population estimates support multiple geographic levels beyond states:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e96c6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/estimates.py:728: UserWarning: Using vintage 2024 data for year 2022. Consider setting vintage=2022 if available.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2024 Population Estimates Program (vintage 2024)\n",
      "Metro areas: 925 CBSAs\n",
      "Largest metropolitan areas:\n",
      "                                              NAME  POPESTIMATE2022\n",
      "1030            New York-Newark-Jersey City, NY-NJ         19619844\n",
      "860             Los Angeles-Long Beach-Anaheim, CA         12906936\n",
      "293                Chicago-Naperville-Elgin, IL-IN          9310017\n",
      "394                Dallas-Fort Worth-Arlington, TX          7973603\n",
      "640             Houston-Pasadena-The Woodlands, TX          7403068\n",
      "1521  Washington-Arlington-Alexandria, DC-VA-MD-WV          6289810\n",
      "65               Atlanta-Sandy Springs-Roswell, GA          6253070\n",
      "1126   Philadelphia-Camden-Wilmington, PA-NJ-DE-MD          6252024\n",
      "929      Miami-Fort Lauderdale-West Palm Beach, FL          6211194\n",
      "1142                     Phoenix-Mesa-Chandler, AZ          5029933\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Metropolitan areas (CBSAs) \n",
    "metros = tc.get_estimates(\n",
    "    geography=\"cbsa\",\n",
    "    variables=\"POP\", \n",
    "    year=2022\n",
    ")\n",
    "\n",
    "print(f\"Metro areas: {metros.shape[0]} CBSAs\")\n",
    "print(\"Largest metropolitan areas:\")\n",
    "metros_largest = metros.nlargest(10, 'POPESTIMATE2022')\n",
    "print(metros_largest[['NAME', 'POPESTIMATE2022']])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "85c07044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2024 Population Estimates Program (vintage 2024)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/estimates.py:728: UserWarning: Using vintage 2024 data for year 2022. Consider setting vintage=2022 if available.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texas counties: 254 counties\n",
      "Largest Texas counties by population:\n",
      "                         NAME  POPESTIMATE2022\n",
      "2669     Harris County, Texas          4806747\n",
      "2625     Dallas County, Texas          2613712\n",
      "2788    Tarrant County, Texas          2161670\n",
      "2583      Bexar County, Texas          2063840\n",
      "2795     Travis County, Texas          1332544\n",
      "2611     Collin County, Texas          1163724\n",
      "2629     Denton County, Texas           980355\n",
      "2647  Fort Bend County, Texas           893319\n",
      "2676    Hidalgo County, Texas           889799\n",
      "2639    El Paso County, Texas           868890\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# County-level data for Texas\n",
    "tx_counties = tc.get_estimates(\n",
    "    geography=\"county\",\n",
    "    variables=\"POP\",\n",
    "    state=\"TX\",\n",
    "    year=2022\n",
    ")\n",
    "\n",
    "print(f\"Texas counties: {tx_counties.shape[0]} counties\")\n",
    "print(\"Largest Texas counties by population:\")\n",
    "tx_largest = tx_counties.nlargest(10, 'POPESTIMATE2022')\n",
    "print(tx_largest[['NAME', 'POPESTIMATE2022']])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d80e9b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2023 Population Estimates Program (vintage 2023)\n",
      "Time series data shape: (16, 5)\n",
      "Years available: [2020, 2021, 2022, 2023]\n"
     ]
    },
    {
     "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>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>variable</th>\n",
       "      <th>year</th>\n",
       "      <th>estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06</td>\n",
       "      <td>California</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2020</td>\n",
       "      <td>39503200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>Florida</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2020</td>\n",
       "      <td>21591299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>New York</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2020</td>\n",
       "      <td>20104710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>Texas</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2020</td>\n",
       "      <td>29234361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06</td>\n",
       "      <td>California</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2021</td>\n",
       "      <td>39145060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>Florida</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2021</td>\n",
       "      <td>21830708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36</td>\n",
       "      <td>New York</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2021</td>\n",
       "      <td>19854526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48</td>\n",
       "      <td>Texas</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2021</td>\n",
       "      <td>29561286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>06</td>\n",
       "      <td>California</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2022</td>\n",
       "      <td>39040616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>Florida</td>\n",
       "      <td>POPESTIMATE</td>\n",
       "      <td>2022</td>\n",
       "      <td>22245521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GEOID        NAME     variable  year  estimate\n",
       "0    06  California  POPESTIMATE  2020  39503200\n",
       "1    12     Florida  POPESTIMATE  2020  21591299\n",
       "2    36    New York  POPESTIMATE  2020  20104710\n",
       "3    48       Texas  POPESTIMATE  2020  29234361\n",
       "4    06  California  POPESTIMATE  2021  39145060\n",
       "5    12     Florida  POPESTIMATE  2021  21830708\n",
       "6    36    New York  POPESTIMATE  2021  19854526\n",
       "7    48       Texas  POPESTIMATE  2021  29561286\n",
       "8    06  California  POPESTIMATE  2022  39040616\n",
       "9    12     Florida  POPESTIMATE  2022  22245521"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Get time series data for select states\n",
    "time_series_states = tc.get_estimates(\n",
    "    geography=\"state\",\n",
    "    variables=\"POP\",\n",
    "    time_series=True,\n",
    "    vintage=2023,  # Use 2023 vintage for data through 2023\n",
    "    state=[\"CA\", \"TX\", \"FL\", \"NY\"],  #Focus on large states\n",
    ")\n",
    "\n",
    "print(\"Time series data shape:\", time_series_states.shape)\n",
    "print(\"Years available:\", sorted(time_series_states[\"year\"].unique()))\n",
    "time_series_states.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41c42c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot population trends\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "for state in time_series_states['NAME'].unique():\n",
    "    state_data = time_series_states[time_series_states['NAME'] == state]\n",
    "    plt.plot(state_data['year'], state_data['estimate'], \n",
    "             marker='o', linewidth=2, label=state)\n",
    "\n",
    "plt.title('Population Trends: Large States (2020-2023)', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=12)\n",
    "plt.ylabel('Population', fontsize=12)\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019867f4",
   "metadata": {},
   "source": [
    "\n",
    "### Basic Population Estimates\n",
    "\n",
    "Let's start with basic population estimates by state:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "640e0341",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2024 Population Estimates Program (vintage 2024)\n",
      "Shape: (52, 3)\n",
      "Data format: ['GEOID', 'NAME', 'POPESTIMATE2022']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/estimates.py:728: UserWarning: Using vintage 2024 data for year 2022. Consider setting vintage=2022 if available.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>POPESTIMATE2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>5076181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>734442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>7377566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>3047704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>06</td>\n",
       "      <td>California</td>\n",
       "      <td>39142414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GEOID        NAME  POPESTIMATE2022\n",
       "14    01     Alabama          5076181\n",
       "15    02      Alaska           734442\n",
       "16    04     Arizona          7377566\n",
       "17    05    Arkansas          3047704\n",
       "18    06  California         39142414"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Get population estimates for US states\n",
    "us_pop_estimates = tc.get_estimates(\n",
    "    geography=\"state\",\n",
    "    variables=\"POP\",\n",
    "    year=2022\n",
    ")\n",
    "\n",
    "print(f\"Shape: {us_pop_estimates.shape}\")\n",
    "print(\"Data format:\", us_pop_estimates.columns.tolist())\n",
    "us_pop_estimates.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2972f5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/estimates.py:728: UserWarning: Using vintage 2024 data for year 2022. Consider setting vintage=2022 if available.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2024 Population Estimates Program (vintage 2024)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Components data shape: (208, 4)\n",
      "Variables available: ['BIRTHS2022' 'DEATHS2022' 'DOMESTICMIG2022' 'INTERNATIONALMIG2022']\n"
     ]
    },
    {
     "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>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>variable</th>\n",
       "      <th>estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>58103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>9359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>79173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>36115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06</td>\n",
       "      <td>California</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>424071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>08</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>62659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>09</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>35637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>10849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>8502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>Florida</td>\n",
       "      <td>BIRTHS2022</td>\n",
       "      <td>221941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GEOID                  NAME    variable  estimate\n",
       "0    01               Alabama  BIRTHS2022     58103\n",
       "1    02                Alaska  BIRTHS2022      9359\n",
       "2    04               Arizona  BIRTHS2022     79173\n",
       "3    05              Arkansas  BIRTHS2022     36115\n",
       "4    06            California  BIRTHS2022    424071\n",
       "5    08              Colorado  BIRTHS2022     62659\n",
       "6    09           Connecticut  BIRTHS2022     35637\n",
       "7    10              Delaware  BIRTHS2022     10849\n",
       "8    11  District of Columbia  BIRTHS2022      8502\n",
       "9    12               Florida  BIRTHS2022    221941"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Get components of population change\n",
    "us_components = tc.get_estimates(\n",
    "    geography=\"state\",\n",
    "    product=\"components\",  # Specify components product\n",
    "    variables=[\"BIRTHS\", \"DEATHS\", \"DOMESTICMIG\", \"INTERNATIONALMIG\"],\n",
    "    year=2022,\n",
    "    output=\"tidy\"  # Tidy format for easier analysis\n",
    ")\n",
    "\n",
    "print(\"Components data shape:\", us_components.shape)\n",
    "print(\"Variables available:\", us_components['variable'].unique())\n",
    "us_components.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3810fdf",
   "metadata": {},
   "source": [
    "\n",
    "### Demographic Breakdowns\n",
    "\n",
    "One of the most powerful features is demographic breakdowns using the `breakdown` parameter. This accesses the Age, Sex, Race, Hispanic Origin (ASRH) datasets:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd63259e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2024 Population Estimates Program (vintage 2024)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/estimates.py:728: UserWarning: Using vintage 2024 data for year 2022. Consider setting vintage=2022 if available.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing characteristics data with breakdown: ['SEX']\n",
      "Population by sex data:\n",
      "   STATE        NAME  SEX  ORIGIN  RACE  AGE GEOID  estimate  year variable  \\\n",
      "0      1     Alabama    1       0     0    0    01     29531  2022      POP   \n",
      "1      1     Alabama    2       0     0    0    01     28195  2022      POP   \n",
      "2      2      Alaska    1       0     0    0    02      4808  2022      POP   \n",
      "3      2      Alaska    2       0     0    0    02      4504  2022      POP   \n",
      "4      4     Arizona    1       0     0    0    04     40228  2022      POP   \n",
      "5      4     Arizona    2       0     0    0    04     38496  2022      POP   \n",
      "6      5    Arkansas    1       0     0    0    05     18258  2022      POP   \n",
      "7      5    Arkansas    2       0     0    0    05     17590  2022      POP   \n",
      "8      6  California    1       0     0    0    06    217463  2022      POP   \n",
      "9      6  California    2       0     0    0    06    208334  2022      POP   \n",
      "\n",
      "  SEX_label  \n",
      "0      Male  \n",
      "1    Female  \n",
      "2      Male  \n",
      "3    Female  \n",
      "4      Male  \n",
      "5    Female  \n",
      "6      Male  \n",
      "7    Female  \n",
      "8      Male  \n",
      "9    Female  \n",
      "\n",
      "Sex categories: ['Male' 'Female']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Get population by sex for all states\n",
    "pop_by_sex = tc.get_estimates(\n",
    "    geography=\"state\",\n",
    "    variables=\"POP\",\n",
    "    breakdown=[\"SEX\"],\n",
    "    breakdown_labels=True, # Include human-readable labels\n",
    "    year=2022\n",
    ")\n",
    "\n",
    "print(\"Population by sex data:\")\n",
    "print(pop_by_sex.head(10))\n",
    "\n",
    "# Check the breakdown categories\n",
    "if 'SEX_label' in pop_by_sex.columns:\n",
    "    print(\"\\nSex categories:\", pop_by_sex['SEX_label'].unique())\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "669223e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test2",
   "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}