{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Quickstart: Ziplore US ZIP Codes with Census Demographics (41,682 ZIPs)\n\nA free, current replacement for the frozen ZIP data inside uszipcode (last release 2022), pyzipcode (2021) and the zipcodes npm package (2018).\n\nDataset: [huggingface.co/datasets/CyberMax-tools/us-zip-codes-demographics](https://huggingface.co/datasets/CyberMax-tools/us-zip-codes-demographics) · file: `zips.csv` · by CyberMax.\n\nRuns anywhere with pandas (Colab, Kaggle, Jupyter, VS Code). The outputs below are from a real run of this notebook."
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Load the data"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "41,682 rows x 25 columns\n"
    },
    {
     "output_type": "execute_result",
     "execution_count": 1,
     "metadata": {},
     "data": {
      "text/plain": "   zip  ...                                         counties\n0  501  ...                                              NaN\n1  544  ...                                              NaN\n2  601  ...                          72001:0.988;72141:0.012\n3  602  ...                          72003:1.000;72011:0.000\n4  603  ...                          72005:0.998;72099:0.002\n5  604  ...                                              NaN\n6  605  ...                                              NaN\n7  606  ...              72093:0.823;72153:0.117;72121:0.061\n8  610  ...  72011:0.968;72099:0.028;72083:0.004;72003:0.001\n9  611  ...                                      72141:1.000\n\n[10 rows x 25 columns]",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>zip</th>\n      <th>city</th>\n      <th>state</th>\n      <th>stateName</th>\n      <th>stateFips</th>\n      <th>county</th>\n      <th>...</th>\n      <th>medianGrossRent</th>\n      <th>housingUnits</th>\n      <th>occupiedUnits</th>\n      <th>ownerOccupiedUnits</th>\n      <th>ownerOccupiedPct</th>\n      <th>counties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>501</td>\n      <td>Holtsville</td>\n      <td>NY</td>\n      <td>New York</td>\n      <td>36.0</td>\n      <td>Suffolk County</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>544</td>\n      <td>Holtsville</td>\n      <td>NY</td>\n      <td>New York</td>\n      <td>36.0</td>\n      <td>Suffolk County</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>601</td>\n      <td>Adjuntas</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Adjuntas Municipio</td>\n      <td>...</td>\n      <td>427.0</td>\n      <td>7570.0</td>\n      <td>5768.0</td>\n      <td>3891.0</td>\n      <td>67.5</td>\n      <td>72001:0.988;72141:0.012</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>602</td>\n      <td>Aguada</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Aguada Municipio</td>\n      <td>...</td>\n      <td>475.0</td>\n      <td>17610.0</td>\n      <td>12954.0</td>\n      <td>9529.0</td>\n      <td>73.6</td>\n      <td>72003:1.000;72011:0.000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>603</td>\n      <td>Aguadilla</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Aguadilla Municipio</td>\n      <td>...</td>\n      <td>475.0</td>\n      <td>26239.0</td>\n      <td>20131.0</td>\n      <td>11531.0</td>\n      <td>57.3</td>\n      <td>72005:0.998;72099:0.002</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>604</td>\n      <td>Aguadilla</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Aguadilla Municipio</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>605</td>\n      <td>Aguadilla</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Aguadilla Municipio</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>606</td>\n      <td>Maricao</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Maricao Municipio</td>\n      <td>...</td>\n      <td>413.0</td>\n      <td>2681.0</td>\n      <td>1860.0</td>\n      <td>1479.0</td>\n      <td>79.5</td>\n      <td>72093:0.823;72153:0.117;72121:0.061</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>610</td>\n      <td>Anasco</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Añasco Municipio</td>\n      <td>...</td>\n      <td>579.0</td>\n      <td>12636.0</td>\n      <td>9604.0</td>\n      <td>6879.0</td>\n      <td>71.6</td>\n      <td>72011:0.968;72099:0.028;72083:0.004;72003:0.001</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>611</td>\n      <td>Angeles</td>\n      <td>PR</td>\n      <td>Puerto Rico</td>\n      <td>72.0</td>\n      <td>Utuado Municipio</td>\n      <td>...</td>\n      <td>470.0</td>\n      <td>776.0</td>\n      <td>679.0</td>\n      <td>429.0</td>\n      <td>63.2</td>\n      <td>72141:1.000</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "source": "import pandas as pd\n\nURL = \"https://huggingface.co/datasets/CyberMax-tools/us-zip-codes-demographics/resolve/main/zips.csv\"\ndf = pd.read_csv(URL)\nprint(f\"{len(df):,} rows x {len(df.columns)} columns\")\ndf.head(10)"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "Summary statistics for the numeric columns:"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 2,
     "metadata": {},
     "data": {
      "text/plain": "       stateFips  countyFips   latitude  longitude   landSqMi  waterSqMi\ncount  41173.000   41173.000  41682.000  41682.000  33791.000  33791.000\nmean      29.484   29568.102     38.458    -89.461     83.582      1.953\nstd       15.761   15774.006      5.829     22.055    234.252     13.823\nmin        1.000    1001.000    -89.998   -177.389      0.001      0.000\n25%       17.000   17131.000     34.954    -97.190      8.116      0.017\n50%       29.000   29183.000     39.118    -87.730     34.892      0.205\n75%       42.000   42057.000     41.905    -79.867     87.036      1.029\nmax       78.000   78030.000     76.531    178.878  13680.117   1426.087",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>stateFips</th>\n      <th>countyFips</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>landSqMi</th>\n      <th>waterSqMi</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>41173.000</td>\n      <td>41173.000</td>\n      <td>41682.000</td>\n      <td>41682.000</td>\n      <td>33791.000</td>\n      <td>33791.000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>29.484</td>\n      <td>29568.102</td>\n      <td>38.458</td>\n      <td>-89.461</td>\n      <td>83.582</td>\n      <td>1.953</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>15.761</td>\n      <td>15774.006</td>\n      <td>5.829</td>\n      <td>22.055</td>\n      <td>234.252</td>\n      <td>13.823</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000</td>\n      <td>1001.000</td>\n      <td>-89.998</td>\n      <td>-177.389</td>\n      <td>0.001</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>17.000</td>\n      <td>17131.000</td>\n      <td>34.954</td>\n      <td>-97.190</td>\n      <td>8.116</td>\n      <td>0.017</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>29.000</td>\n      <td>29183.000</td>\n      <td>39.118</td>\n      <td>-87.730</td>\n      <td>34.892</td>\n      <td>0.205</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>42.000</td>\n      <td>42057.000</td>\n      <td>41.905</td>\n      <td>-79.867</td>\n      <td>87.036</td>\n      <td>1.029</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>78.000</td>\n      <td>78030.000</td>\n      <td>76.531</td>\n      <td>178.878</td>\n      <td>13680.117</td>\n      <td>1426.087</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "source": "df[['stateFips', 'countyFips', 'latitude', 'longitude', 'landSqMi', 'waterSqMi']].describe().round(3)"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Go further\n- **All CyberMax tools, datasets and free apps:** [https://cybermax-tools-cybermax.static.hf.space/](https://cybermax-tools-cybermax.static.hf.space/)"
  }
 ],
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