{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic usage of pytidycensus\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/01_basic_usage.ipynb)\n",
    "\n",
    "This notebook demonstrates the basic functionality of **pytidycensus**, a Python library for accessing US Census Bureau data with pandas and GeoPandas support.\n",
    "\n",
    "## Setup\n",
    "\n",
    "First, let's install and import the necessary packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to install if running in Colab\n",
    "# !pip install pytidycensus matplotlib\n",
    "\n",
    "import pytidycensus as tc\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set styling\n",
    "plt.style.use('default')\n",
    "sns.set_palette(\"husl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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",
    "Set your API key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace with your actual API key\n",
    "tc.set_census_api_key(\"Your API Key Here\")\n",
    "\n",
    "# # Alternatively, set as environment variable:\n",
    "# import os\n",
    "# os.environ['CENSUS_API_KEY'] = 'Your API Key Here'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore this cell. I am just loading my credentials from a yaml file in the parent directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "import os\n",
    "# Try to get API key from environment\n",
    "api_key = os.environ.get(\"CENSUS_API_KEY\")\n",
    "\n",
    "# For documentation builds without a key, we'll mock the responses\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",
    "    # This won't make real API calls during documentation builds\n",
    "    tc.set_census_api_key(\"EXAMPLE_API_KEY_FOR_DOCS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Core Functions\n",
    "\n",
    "pytidycensus provides two main functions:\n",
    "\n",
    "- **`get_decennial()`**: Access to 2000, 2010, and 2020 decennial US Census APIs\n",
    "- **`get_acs()`**: Access to 1-year and 5-year American Community Survey APIs\n",
    "\n",
    "### Example: Median Age by State (2020 Census)\n",
    "\n",
    "Let's look at median age by state from the 2020 Census:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2020 decennial Census\n",
      "Using the Demographic and Housing Characteristics File\n",
      "Data shape: (52, 5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/decennial.py:429: UserWarning: Note: 2020 decennial Census data use differential privacy, a technique that introduces errors into data to preserve respondent confidentiality. Small counts should be interpreted with caution. See https://www.census.gov/library/fact-sheets/2021/protecting-the-confidentiality-of-the-2020-census-redistricting-data.html for additional guidance.\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>state</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>09</td>\n",
       "      <td>09</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>P13_001N</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>P13_001N</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>DC</td>\n",
       "      <td>P13_001N</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>Florida</td>\n",
       "      <td>P13_001N</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>P13_001N</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state GEOID         NAME  variable  estimate\n",
       "0    09    09  Connecticut  P13_001N      41.1\n",
       "1    10    10     Delaware  P13_001N      41.1\n",
       "2    11    11           DC  P13_001N      33.9\n",
       "3    12    12      Florida  P13_001N      43.0\n",
       "4    13    13      Georgia  P13_001N      37.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get median age by state from 2020 Census\n",
    "age_2020 = tc.get_decennial(\n",
    "    geography=\"state\",\n",
    "    variables=\"P13_001N\",  # Median age variable\n",
    "    year=2020,\n",
    "    sumfile=\"dhc\"  # Demographic and Housing Characteristics file\n",
    ")\n",
    "\n",
    "print(f\"Data shape: {age_2020.shape}\")\n",
    "age_2020.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function returns a pandas DataFrame with four columns:\n",
    "\n",
    "- **`GEOID`**: Identifier for the geographical unit\n",
    "- **`NAME`**: Descriptive name of the geographical unit  \n",
    "- **`variable`**: Census variable represented in the row\n",
    "- **`value`**: Value of the variable for that unit\n",
    "\n",
    "### Visualizing the Data\n",
    "\n",
    "Since we have a tidy DataFrame, we can easily visualize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a horizontal bar plot\n",
    "plt.figure(figsize=(10, 12))\n",
    "\n",
    "# Sort by median age and plot\n",
    "age_sorted = age_2020.sort_values('estimate', ascending=True).reset_index(drop=True)\n",
    "plt.barh(range(len(age_sorted)), age_sorted['estimate'])\n",
    "plt.yticks(range(len(age_sorted)), age_sorted['state'])\n",
    "plt.xlabel('Median Age (years)')\n",
    "plt.title('Median Age by State (2020 Census)', fontsize=14, fontweight='bold')\n",
    "plt.grid(axis='x', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geography in pytidycensus\n",
    "\n",
    "pytidycensus supports many geographic levels. Here are the most commonly used:\n",
    "\n",
    "| Geography | Definition | Example Usage |\n",
    "|-----------|------------|---------------|\n",
    "| `\"us\"` | United States | National data |\n",
    "| `\"state\"` | State or equivalent | State-level data |\n",
    "| `\"county\"` | County or equivalent | County-level data |\n",
    "| `\"tract\"` | Census tract | Neighborhood-level data |\n",
    "| `\"block group\"` | Census block group | Sub-neighborhood data |\n",
    "| `\"place\"` | Census-designated place | City/town data |\n",
    "| `\"zcta\"` | ZIP Code Tabulation Area | ZIP code areas |\n",
    "\n",
    "### Example: County Data\n",
    "\n",
    "Let's get population data for counties in Texas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2020 decennial Census\n",
      "Using the PL 94-171 Redistricting Data Summary File\n",
      "Number of Texas counties: 254\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmann1123/Documents/github/pytidycensus/pytidycensus/decennial.py:429: UserWarning: Note: 2020 decennial Census data use differential privacy, a technique that introduces errors into data to preserve respondent confidentiality. Small counts should be interpreted with caution. See https://www.census.gov/library/fact-sheets/2021/protecting-the-confidentiality-of-the-2020-census-redistricting-data.html for additional guidance.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
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       "    }\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</th>\n",
       "      <th>county</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>48</td>\n",
       "      <td>001</td>\n",
       "      <td>48001</td>\n",
       "      <td>Anderson County, Texas</td>\n",
       "      <td>P1_001N</td>\n",
       "      <td>57922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>003</td>\n",
       "      <td>48003</td>\n",
       "      <td>Andrews County, Texas</td>\n",
       "      <td>P1_001N</td>\n",
       "      <td>18610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48</td>\n",
       "      <td>005</td>\n",
       "      <td>48005</td>\n",
       "      <td>Angelina County, Texas</td>\n",
       "      <td>P1_001N</td>\n",
       "      <td>86395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>007</td>\n",
       "      <td>48007</td>\n",
       "      <td>Aransas County, Texas</td>\n",
       "      <td>P1_001N</td>\n",
       "      <td>23830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>009</td>\n",
       "      <td>48009</td>\n",
       "      <td>Archer County, Texas</td>\n",
       "      <td>P1_001N</td>\n",
       "      <td>8560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state county  GEOID                    NAME variable  estimate\n",
       "0    48    001  48001  Anderson County, Texas  P1_001N     57922\n",
       "1    48    003  48003   Andrews County, Texas  P1_001N     18610\n",
       "2    48    005  48005  Angelina County, Texas  P1_001N     86395\n",
       "3    48    007  48007   Aransas County, Texas  P1_001N     23830\n",
       "4    48    009  48009    Archer County, Texas  P1_001N      8560"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total population for Texas counties\n",
    "tx_pop = tc.get_decennial(\n",
    "    geography=\"county\",\n",
    "    variables=\"P1_001N\",  # Total population\n",
    "    state=\"TX\",  # Filter to Texas\n",
    "    year=2020\n",
    ")\n",
    "\n",
    "print(f\"Number of Texas counties: {len(tx_pop)}\")\n",
    "tx_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Searching for Variables\n",
    "\n",
    "The Census has thousands of variables. Use `load_variables()` to search for what you need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded cached variables for 2022 acs acs5\n",
      "Total variables available: 28193\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>name</th>\n",
       "      <th>label</th>\n",
       "      <th>concept</th>\n",
       "      <th>predicateType</th>\n",
       "      <th>group</th>\n",
       "      <th>limit</th>\n",
       "      <th>table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AIANHH</td>\n",
       "      <td>Geography</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N/A</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AIHHTL</td>\n",
       "      <td>Geography</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N/A</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AIRES</td>\n",
       "      <td>Geography</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N/A</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANRC</td>\n",
       "      <td>Geography</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>N/A</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B01001A_001E</td>\n",
       "      <td>Estimate!!Total:</td>\n",
       "      <td>Sex by Age (White Alone)</td>\n",
       "      <td>int</td>\n",
       "      <td>B01001A</td>\n",
       "      <td>0</td>\n",
       "      <td>B01001A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name             label                   concept predicateType  \\\n",
       "0        AIANHH         Geography                                           \n",
       "1        AIHHTL         Geography                                           \n",
       "2         AIRES         Geography                                           \n",
       "3          ANRC         Geography                                           \n",
       "4  B01001A_001E  Estimate!!Total:  Sex by Age (White Alone)           int   \n",
       "\n",
       "     group  limit    table  \n",
       "0      N/A      0      NaN  \n",
       "1      N/A      0      NaN  \n",
       "2      N/A      0      NaN  \n",
       "3      N/A      0      NaN  \n",
       "4  B01001A      0  B01001A  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load variables for 2022 5-year ACS\n",
    "variables_2022 = tc.load_variables(2022, \"acs\", \"acs5\", cache=True)\n",
    "\n",
    "print(f\"Total variables available: {len(variables_2022)}\")\n",
    "variables_2022.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded cached variables for 2022 acs acs5\n",
      "Found 25 income-related variables\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>name</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B19013A_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B19013B_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B19013C_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B19013D_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B19013E_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B19013F_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B19013G_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B19013H_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B19013I_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B19013_001E</td>\n",
       "      <td>Estimate!!Median household income in the past ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                                              label\n",
       "0  B19013A_001E  Estimate!!Median household income in the past ...\n",
       "1  B19013B_001E  Estimate!!Median household income in the past ...\n",
       "2  B19013C_001E  Estimate!!Median household income in the past ...\n",
       "3  B19013D_001E  Estimate!!Median household income in the past ...\n",
       "4  B19013E_001E  Estimate!!Median household income in the past ...\n",
       "5  B19013F_001E  Estimate!!Median household income in the past ...\n",
       "6  B19013G_001E  Estimate!!Median household income in the past ...\n",
       "7  B19013H_001E  Estimate!!Median household income in the past ...\n",
       "8  B19013I_001E  Estimate!!Median household income in the past ...\n",
       "9   B19013_001E  Estimate!!Median household income in the past ..."
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search for income-related variables\n",
    "income_vars = tc.search_variables(\"median household income\", 2022, \"acs\", \"acs5\")\n",
    "print(f\"Found {len(income_vars)} income-related variables\")\n",
    "income_vars[['name', 'label']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with ACS Data\n",
    "\n",
    "American Community Survey (ACS) data includes estimates with margins of error, since it's based on a sample rather than a complete count.\n",
    "\n",
    "### Example: Median Household Income\n",
    "\n",
    "Let's get median household income for Vermont counties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2018-2022 5-year ACS\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>medincome</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>tract</th>\n",
       "      <th>NAME</th>\n",
       "      <th>medincome_moe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50001960100</td>\n",
       "      <td>96154</td>\n",
       "      <td>50</td>\n",
       "      <td>001</td>\n",
       "      <td>960100</td>\n",
       "      <td>Addison County, Vermont</td>\n",
       "      <td>18071.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50001960200</td>\n",
       "      <td>106328</td>\n",
       "      <td>50</td>\n",
       "      <td>001</td>\n",
       "      <td>960200</td>\n",
       "      <td>Addison County, Vermont</td>\n",
       "      <td>22525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50001960300</td>\n",
       "      <td>72171</td>\n",
       "      <td>50</td>\n",
       "      <td>001</td>\n",
       "      <td>960300</td>\n",
       "      <td>Addison County, Vermont</td>\n",
       "      <td>19890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50001960400</td>\n",
       "      <td>99056</td>\n",
       "      <td>50</td>\n",
       "      <td>001</td>\n",
       "      <td>960400</td>\n",
       "      <td>Addison County, Vermont</td>\n",
       "      <td>5760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50001960500</td>\n",
       "      <td>75875</td>\n",
       "      <td>50</td>\n",
       "      <td>001</td>\n",
       "      <td>960500</td>\n",
       "      <td>Addison County, Vermont</td>\n",
       "      <td>15001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>50027966501</td>\n",
       "      <td>64756</td>\n",
       "      <td>50</td>\n",
       "      <td>027</td>\n",
       "      <td>966501</td>\n",
       "      <td>Windsor County, Vermont</td>\n",
       "      <td>8833.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>50027966502</td>\n",
       "      <td>103750</td>\n",
       "      <td>50</td>\n",
       "      <td>027</td>\n",
       "      <td>966502</td>\n",
       "      <td>Windsor County, Vermont</td>\n",
       "      <td>12857.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>50027966600</td>\n",
       "      <td>64364</td>\n",
       "      <td>50</td>\n",
       "      <td>027</td>\n",
       "      <td>966600</td>\n",
       "      <td>Windsor County, Vermont</td>\n",
       "      <td>5805.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>50027966700</td>\n",
       "      <td>56875</td>\n",
       "      <td>50</td>\n",
       "      <td>027</td>\n",
       "      <td>966700</td>\n",
       "      <td>Windsor County, Vermont</td>\n",
       "      <td>12544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>50027966800</td>\n",
       "      <td>68542</td>\n",
       "      <td>50</td>\n",
       "      <td>027</td>\n",
       "      <td>966800</td>\n",
       "      <td>Windsor County, Vermont</td>\n",
       "      <td>3365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           GEOID medincome state county   tract                     NAME  \\\n",
       "0    50001960100     96154    50    001  960100  Addison County, Vermont   \n",
       "1    50001960200    106328    50    001  960200  Addison County, Vermont   \n",
       "2    50001960300     72171    50    001  960300  Addison County, Vermont   \n",
       "3    50001960400     99056    50    001  960400  Addison County, Vermont   \n",
       "4    50001960500     75875    50    001  960500  Addison County, Vermont   \n",
       "..           ...       ...   ...    ...     ...                      ...   \n",
       "188  50027966501     64756    50    027  966501  Windsor County, Vermont   \n",
       "189  50027966502    103750    50    027  966502  Windsor County, Vermont   \n",
       "190  50027966600     64364    50    027  966600  Windsor County, Vermont   \n",
       "191  50027966700     56875    50    027  966700  Windsor County, Vermont   \n",
       "192  50027966800     68542    50    027  966800  Windsor County, Vermont   \n",
       "\n",
       "    medincome_moe  \n",
       "0         18071.0  \n",
       "1         22525.0  \n",
       "2         19890.0  \n",
       "3          5760.0  \n",
       "4         15001.0  \n",
       "..            ...  \n",
       "188        8833.0  \n",
       "189       12857.0  \n",
       "190        5805.0  \n",
       "191       12544.0  \n",
       "192        3365.0  \n",
       "\n",
       "[193 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get median household income for Vermont tracts\n",
    "vt_income = tc.get_acs(\n",
    "    geography=\"tract\",\n",
    "    variables={\"medincome\": \"B19013_001\"},  # Can use dictionary for variable names\n",
    "    state=\"VT\",\n",
    "    year=2022,\n",
    "    output=\"wide\",  # Get data in wide format\n",
    ")\n",
    "\n",
    "vt_income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that ACS data returns `estimate` and `moe` (margin of error) columns instead of a single `value` column.\n",
    "\n",
    "### Visualizing Uncertainty\n",
    "\n",
    "We can visualize the uncertainty around estimates using error bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clean county names and create visualization\n",
    "vt_clean = vt_income.copy()\n",
    "vt_clean = vt_clean.sort_values(\"medincome\")\n",
    "vt_clean.dropna(subset=[\"medincome\"], inplace=True)\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create error bar plot\n",
    "plt.errorbar(\n",
    "    vt_clean[\"medincome\"],\n",
    "    range(len(vt_clean)),\n",
    "    xerr=vt_clean[\"medincome_moe\"],  # Using margin of error as error bars\n",
    "    fmt=\"o\",\n",
    "    color=\"red\",\n",
    "    markersize=8,\n",
    "    capsize=5,\n",
    "    capthick=2,\n",
    ")\n",
    "\n",
    "plt.xlabel('ACS Estimate (bars represent margin of error)')\n",
    "plt.title('Median Household Income by County in Vermont\\n2018-2022 American Community Survey')\n",
    "plt.grid(axis='x', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Output Formats\n",
    "\n",
    "By default, pytidycensus returns data in \"tidy\" format. You can also request \"wide\" format:\n",
    "\n",
    "### Tidy Format (Default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2018-2022 5-year ACS\n",
      "Tidy format shape: (174, 7)\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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>variable</th>\n",
       "      <th>estimate</th>\n",
       "      <th>moe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>06001</td>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>total_pop</td>\n",
       "      <td>1663823.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06</td>\n",
       "      <td>003</td>\n",
       "      <td>06003</td>\n",
       "      <td>Alpine County, California</td>\n",
       "      <td>total_pop</td>\n",
       "      <td>1515.0</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06</td>\n",
       "      <td>005</td>\n",
       "      <td>06005</td>\n",
       "      <td>Amador County, California</td>\n",
       "      <td>total_pop</td>\n",
       "      <td>40577.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06</td>\n",
       "      <td>007</td>\n",
       "      <td>06007</td>\n",
       "      <td>Butte County, California</td>\n",
       "      <td>total_pop</td>\n",
       "      <td>213605.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06</td>\n",
       "      <td>009</td>\n",
       "      <td>06009</td>\n",
       "      <td>Calaveras County, California</td>\n",
       "      <td>total_pop</td>\n",
       "      <td>45674.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>06</td>\n",
       "      <td>107</td>\n",
       "      <td>06107</td>\n",
       "      <td>Tulare County, California</td>\n",
       "      <td>median_age</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>06</td>\n",
       "      <td>109</td>\n",
       "      <td>06109</td>\n",
       "      <td>Tuolumne County, California</td>\n",
       "      <td>median_age</td>\n",
       "      <td>48.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>06</td>\n",
       "      <td>111</td>\n",
       "      <td>06111</td>\n",
       "      <td>Ventura County, California</td>\n",
       "      <td>median_age</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>06</td>\n",
       "      <td>113</td>\n",
       "      <td>06113</td>\n",
       "      <td>Yolo County, California</td>\n",
       "      <td>median_age</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>06</td>\n",
       "      <td>115</td>\n",
       "      <td>06115</td>\n",
       "      <td>Yuba County, California</td>\n",
       "      <td>median_age</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    state county  GEOID                          NAME    variable   estimate  \\\n",
       "0      06    001  06001    Alameda County, California   total_pop  1663823.0   \n",
       "1      06    003  06003     Alpine County, California   total_pop     1515.0   \n",
       "2      06    005  06005     Amador County, California   total_pop    40577.0   \n",
       "3      06    007  06007      Butte County, California   total_pop   213605.0   \n",
       "4      06    009  06009  Calaveras County, California   total_pop    45674.0   \n",
       "..    ...    ...    ...                           ...         ...        ...   \n",
       "169    06    107  06107     Tulare County, California  median_age       31.5   \n",
       "170    06    109  06109   Tuolumne County, California  median_age       48.7   \n",
       "171    06    111  06111    Ventura County, California  median_age       39.0   \n",
       "172    06    113  06113       Yolo County, California  median_age       32.0   \n",
       "173    06    115  06115       Yuba County, California  median_age       33.5   \n",
       "\n",
       "       moe  \n",
       "0      NaN  \n",
       "1    206.0  \n",
       "2      NaN  \n",
       "3      NaN  \n",
       "4      NaN  \n",
       "..     ...  \n",
       "169    0.2  \n",
       "170    0.4  \n",
       "171    0.2  \n",
       "172    0.2  \n",
       "173    0.3  \n",
       "\n",
       "[174 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiple variables in tidy format\n",
    "ca_demo_tidy = tc.get_acs(\n",
    "    geography=\"county\",\n",
    "    variables={\n",
    "        \"total_pop\": \"B01003_001\",\n",
    "        \"median_income\": \"B19013_001\",\n",
    "        \"median_age\": \"B01002_001\"\n",
    "    },\n",
    "    state=\"CA\",\n",
    "    year=2022,\n",
    "    output=\"tidy\"  # This is the default\n",
    ")\n",
    "\n",
    "print(f\"Tidy format shape: {ca_demo_tidy.shape}\")\n",
    "ca_demo_tidy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wide Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2018-2022 5-year ACS\n",
      "Wide format shape: (58, 10)\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>total_pop</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_age</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>NAME</th>\n",
       "      <th>total_pop_moe</th>\n",
       "      <th>median_income_moe</th>\n",
       "      <th>median_age_moe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06001</td>\n",
       "      <td>1663823</td>\n",
       "      <td>122488</td>\n",
       "      <td>38.4</td>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1231.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06003</td>\n",
       "      <td>1515</td>\n",
       "      <td>101125</td>\n",
       "      <td>43.0</td>\n",
       "      <td>06</td>\n",
       "      <td>003</td>\n",
       "      <td>Alpine County, California</td>\n",
       "      <td>206.0</td>\n",
       "      <td>17442.0</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06005</td>\n",
       "      <td>40577</td>\n",
       "      <td>74853</td>\n",
       "      <td>49.6</td>\n",
       "      <td>06</td>\n",
       "      <td>005</td>\n",
       "      <td>Amador County, California</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>6048.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06007</td>\n",
       "      <td>213605</td>\n",
       "      <td>66085</td>\n",
       "      <td>36.5</td>\n",
       "      <td>06</td>\n",
       "      <td>007</td>\n",
       "      <td>Butte County, California</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2261.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06009</td>\n",
       "      <td>45674</td>\n",
       "      <td>77526</td>\n",
       "      <td>52.1</td>\n",
       "      <td>06</td>\n",
       "      <td>009</td>\n",
       "      <td>Calaveras County, California</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3875.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GEOID  total_pop  median_income  median_age state county  \\\n",
       "0  06001    1663823         122488        38.4    06    001   \n",
       "1  06003       1515         101125        43.0    06    003   \n",
       "2  06005      40577          74853        49.6    06    005   \n",
       "3  06007     213605          66085        36.5    06    007   \n",
       "4  06009      45674          77526        52.1    06    009   \n",
       "\n",
       "                           NAME total_pop_moe  median_income_moe  \\\n",
       "0    Alameda County, California          <NA>             1231.0   \n",
       "1     Alpine County, California         206.0            17442.0   \n",
       "2     Amador County, California          <NA>             6048.0   \n",
       "3      Butte County, California          <NA>             2261.0   \n",
       "4  Calaveras County, California          <NA>             3875.0   \n",
       "\n",
       "   median_age_moe  \n",
       "0             0.2  \n",
       "1            10.5  \n",
       "2             0.4  \n",
       "3             0.3  \n",
       "4             0.6  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same data in wide format\n",
    "ca_demo_wide = tc.get_acs(\n",
    "    geography=\"county\",\n",
    "    variables={\n",
    "        \"total_pop\": \"B01003_001\",\n",
    "        \"median_income\": \"B19013_001\", \n",
    "        \"median_age\": \"B01002_001\"\n",
    "    },\n",
    "    state=\"CA\",\n",
    "    year=2022,\n",
    "    output=\"wide\"\n",
    ")\n",
    "\n",
    "print(f\"Wide format shape: {ca_demo_wide.shape}\")\n",
    "ca_demo_wide.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Variables Analysis\n",
    "\n",
    "Let's analyze the relationship between different demographic variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create scatter plot of median age vs median income\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.scatter(\n",
    "    ca_demo_wide['median_age'], \n",
    "    ca_demo_wide['median_income'],\n",
    "    s=ca_demo_wide['total_pop']/5000,  # Size by population\n",
    "    alpha=0.6\n",
    ")\n",
    "\n",
    "plt.xlabel('Median Age (years)')\n",
    "plt.ylabel('Median Household Income ($)')\n",
    "plt.title('Median Age vs Median Income by California County\\n(Bubble size = Population)')\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "# Add correlation coefficient\n",
    "correlation = ca_demo_wide['median_age'].corr(ca_demo_wide['median_income'])\n",
    "plt.text(0.05, 0.95, f'Correlation: {correlation:.3f}', \n",
    "         transform=plt.gca().transAxes, fontsize=12,\n",
    "         bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Different Survey Types\n",
    "\n",
    "The ACS has different survey periods:\n",
    "\n",
    "- **5-year ACS** (`survey=\"acs5\"`): More reliable for small areas, 5-year average\n",
    "- **1-year ACS** (`survey=\"acs1\"`): More current, only for areas with 65,000+ population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data from the 2018-2022 5-year ACS\n",
      "Getting data from the 2022 1-year ACS\n",
      "The 1-year ACS provides data for geographies with populations of 65,000 and greater.\n",
      "5-year ACS places: 1611\n",
      "1-year ACS places: 142\n",
      "\n",
      "1-year ACS is only available for larger places:\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>NAME</th>\n",
       "      <th>state</th>\n",
       "      <th>place</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>variable</th>\n",
       "      <th>estimate</th>\n",
       "      <th>moe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda city, California</td>\n",
       "      <td>06</td>\n",
       "      <td>00562</td>\n",
       "      <td>0600562</td>\n",
       "      <td>B19013_001</td>\n",
       "      <td>131116</td>\n",
       "      <td>11963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alhambra city, California</td>\n",
       "      <td>06</td>\n",
       "      <td>00884</td>\n",
       "      <td>0600884</td>\n",
       "      <td>B19013_001</td>\n",
       "      <td>72406</td>\n",
       "      <td>11887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anaheim city, California</td>\n",
       "      <td>06</td>\n",
       "      <td>02000</td>\n",
       "      <td>0602000</td>\n",
       "      <td>B19013_001</td>\n",
       "      <td>85133</td>\n",
       "      <td>4032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Antioch city, California</td>\n",
       "      <td>06</td>\n",
       "      <td>02252</td>\n",
       "      <td>0602252</td>\n",
       "      <td>B19013_001</td>\n",
       "      <td>100178</td>\n",
       "      <td>13978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple Valley town, California</td>\n",
       "      <td>06</td>\n",
       "      <td>02364</td>\n",
       "      <td>0602364</td>\n",
       "      <td>B19013_001</td>\n",
       "      <td>56187</td>\n",
       "      <td>7004.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            NAME state  place    GEOID    variable  estimate  \\\n",
       "0       Alameda city, California    06  00562  0600562  B19013_001    131116   \n",
       "1      Alhambra city, California    06  00884  0600884  B19013_001     72406   \n",
       "2       Anaheim city, California    06  02000  0602000  B19013_001     85133   \n",
       "3       Antioch city, California    06  02252  0602252  B19013_001    100178   \n",
       "4  Apple Valley town, California    06  02364  0602364  B19013_001     56187   \n",
       "\n",
       "       moe  \n",
       "0  11963.0  \n",
       "1  11887.0  \n",
       "2   4032.0  \n",
       "3  13978.0  \n",
       "4   7004.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare 1-year vs 5-year estimates for large cities\n",
    "cities_5yr = tc.get_acs(\n",
    "    geography=\"place\",\n",
    "    variables=\"B19013_001\",\n",
    "    state=\"CA\", \n",
    "    year=2022,\n",
    "    survey=\"acs5\"\n",
    ")\n",
    "\n",
    "cities_1yr = tc.get_acs(\n",
    "    geography=\"place\",\n",
    "    variables=\"B19013_001\",\n",
    "    state=\"CA\",\n",
    "    year=2022, \n",
    "    survey=\"acs1\"\n",
    ")\n",
    "\n",
    "print(f\"5-year ACS places: {len(cities_5yr)}\")\n",
    "print(f\"1-year ACS places: {len(cities_1yr)}\")\n",
    "print(\"\\n1-year ACS is only available for larger places:\")\n",
    "cities_1yr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this notebook, we learned:\n",
    "\n",
    "1. **Setup**: How to install pytidycensus and set your API key\n",
    "2. **Core functions**: `get_decennial()` and `get_acs()` for different data sources\n",
    "3. **Geographic levels**: From national to neighborhood-level data\n",
    "4. **Variable search**: Using `load_variables()` and `search_variables()`\n",
    "5. **Data formats**: Tidy vs wide format output\n",
    "6. **Uncertainty**: Working with ACS margins of error\n",
    "7. **Visualization**: Creating plots with matplotlib\n",
    "\n",
    "## Next Steps\n",
    "\n",
    "- **Spatial Analysis**: See `02_spatial_data.ipynb` for mapping examples\n",
    "- **Advanced ACS**: Explore `03_margins_of_error.ipynb` for statistical techniques\n",
    "- **Other Datasets**: Check `04_other_datasets.ipynb` for population estimates\n",
    "- **Microdata**: Learn about PUMS data in `05_pums_data.ipynb`\n",
    "\n",
    "## Resources\n",
    "\n",
    "- [pytidycensus Documentation](https://mmann1123.github.io/pytidycensus)\n",
    "- [Census Variable Search](https://api.census.gov/data.html)\n",
    "- [Census Bureau Data](https://www.census.gov/data.html)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "test2",
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