Datasets:
Add a tested pandas quickstart notebook
Browse files- README.md +6 -0
- notebooks/quickstart.ipynb +75 -0
README.md
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@@ -77,6 +77,12 @@ This table is published under CC BY 4.0: credit "Ziplore by CyberMax, with data
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- [Fipsbook: US Census Geographies with FIPS Codes, Centroids & Land Area (2025)](https://huggingface.co/datasets/CyberMax-tools/fipsbook-us-census-geographies)
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<!-- cybermax-xlinks:end -->
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<!-- cybermax-web:start -->
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## Browse it on the web
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- [Fipsbook: US Census Geographies with FIPS Codes, Centroids & Land Area (2025)](https://huggingface.co/datasets/CyberMax-tools/fipsbook-us-census-geographies)
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<!-- cybermax-xlinks:end -->
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<!-- cybermax-notebook:start -->
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## Quickstart notebook
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Load this dataset with pandas and see real outputs in the [quickstart notebook](https://huggingface.co/datasets/CyberMax-tools/us-zip-codes-demographics/blob/main/notebooks/quickstart.ipynb).
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<!-- cybermax-notebook:end -->
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<!-- cybermax-web:start -->
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## Browse it on the web
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notebooks/quickstart.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"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."
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "## Load the data"
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "41,682 rows x 25 columns\n"
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},
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{
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"output_type": "execute_result",
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"execution_count": 1,
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"metadata": {},
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"data": {
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"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]",
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"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>"
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}
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}
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],
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"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)"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "Summary statistics for the numeric columns:"
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"execution_count": 2,
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"metadata": {},
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"data": {
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"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",
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"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>"
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}
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}
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],
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"source": "df[['stateFips', 'countyFips', 'latitude', 'longitude', 'landSqMi', 'waterSqMi']].describe().round(3)"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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