README.md

---
license: cc-by-nc-4.0
---

A curation of datasets for educations purposes.

### California Housing

Since SciKit Learn's [California Housing dataset](https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset) often fails to download this is the Data Frame of the data in CSV format.  
By default [SciKit Learn use some pre processing](https://github.com/scikit-learn/scikit-learn/blob/d3898d9d57aeb1e960d266613a2e31b07bca39d7/sklearn/datasets/_california_housing.py#L208-L220) of the [original data](https://web.archive.org/web/20250912205745/https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html).  
This CSV is the data after the processing of SciKit Learn.

### CIFAR 10

Since using SciKit Learn's `fetch_openml()` fails to download the `CIFAR_10` dataset this is an alternative.  
It was generated by:

```python
dsTrain = torchvision.datasets.CIFAR10(root = dataFolderPath, train = True,  download = True)
dsVal   = torchvision.datasets.CIFAR10(root = dataFolderPath, train = False, download = True)

numSamples = len(dsTrain)
tXTrain = np.zeros((numSamples, 32, 32, 3), dtype = np.uint8)
vYTrain = np.zeros((numSamples,), dtype = np.uint8)

for ii in range(numSamples):
    tXi, valY = dsTrain[ii]
    tXTrain[ii] = tXi
    vYTrain[ii] = valY

numSamples = len(dsVal)
tXVal = np.zeros((numSamples, 32, 32, 3), dtype = np.uint8)
vYVal = np.zeros((numSamples,), dtype = np.uint8)

for ii in range(numSamples):
    tXi, valY = dsVal[ii]
    tXVal[ii] = tXi
    vYVal[ii] = valY

tX = np.concatenate((tXTrain, tXVal), axis = 0)
vY = np.concatenate((vYTrain, vYVal), axis = 0)

mX = np.reshape(tX, (tX.shape[0], -1))

dfData = pd.DataFrame(np.concatenate((mX, vY[:, np.newaxis]), axis = 1), columns = [f'Pixel_{ii:04d}' for ii in range(mX.shape[1])] + ['Label'])
dfData.to_parquet('CIFAR10.parquet', index = False)
```

### MNIST

A dataframe where the first 60,000 rows are the train set and the last 10,000 are the test set.  
The last column is the label.  
Images are row major, hence a `np.reshape(dfX.iloc[0, :-1], (28, 28))` will generate the image. 

Generated by:

```python
import numpy as np
from sklearn.datasets import fetch_openml

dfX, dsY = fetch_openml('mnist_784', version = 1, return_X_y = True, as_frame = True)

dfX.columns = [f'{ii:04d}' for ii in range(dfX.shape[1])]
dfX['Label'] = dsY.astype(np.uint8)
dfX = dfX.astype(np.uint8)

dfX.to_parquet('MNIST.parquet', index = False)
```

### Israel Settlements

Generated by:

```python
# Scientific Python
import numpy as np
import scipy as sp
import pandas as pd

# Geo
from geopy.distance import geodesic

# Miscellaneous
from platform import python_version
import random
import requests

numSettlements = 1000

lOverpassEndpoints = [
    "https://overpass-api.de/api/interpreter",
    "https://overpass.kumi.systems/api/interpreter",
    "https://overpass.private.coffee/api/interpreter",
]

overpassQuery = """
[out:json][timeout:120];
(
  node["place"~"^(city|town|village)$"](29.4,34.2,33.4,35.9);
  way["place"~"^(city|town|village)$"](29.4,34.2,33.4,35.9);
);
out center;
"""

dHeaders = {
    "User-Agent": "OverpassQueryClient/1.0",
    "Accept": "application/json, text/plain;q=0.9, */*;q=0.8",
    "Content-Type": "text/plain; charset=utf-8",
}

dData     = None
lastError = None

for overpassUrl in lOverpassEndpoints:
    try:
        # Send raw Overpass QL as the request body.
        postResponse = requests.post(overpassUrl, data=overpassQuery, headers=dHeaders, timeout=180)
        postResponse.raise_for_status()
        dData = postResponse.json()
        print(f"Overpass query succeeded via: {overpassUrl}")
        break
    except requests.RequestException as ex:
        statusCode = getattr(ex.response, "status_code", None)
        responseText = ""
        if getattr(ex, "response", None) is not None and ex.response is not None:
            responseText = ex.response.text[:400]
        print(f"Overpass request failed via {overpassUrl} (status={statusCode}): {responseText}")
        lastError = ex

if dData is None:
    raise RuntimeError("All Overpass endpoints failed") from lastError

lSettlements = []

# Parse the geo-data
for element in dData.get('elements', []):
    tags = element.get('tags', {})
    
    # Extract name (prefer English, fallback to default name)
    settlementName = tags.get('name:en', tags.get('name'))
    
    # Extract population safely (convert to int)
    populationStr = tags.get('population', '0').replace(',', '').split(';')[0]
    try:
        population = int(populationStr)
    except ValueError:
        population = 0
        
    # Extract coordinates (ways have a center, nodes have direct lat/lon)
    coordLat = element.get('lat', element.get('center', {}).get('lat'))
    coordLon = element.get('lon', element.get('center', {}).get('lon'))
    
    if settlementName and coordLat and coordLon:
        lSettlements.append({
            'name': settlementName,
            'population': population,
            'lat': coordLat,
            'lon': coordLon
        })

# Normalize names to have only ASCII characters (remove accents, etc.)
for settlement in lSettlements:
    settlement['name'] = settlement['name'].encode('ascii', 'ignore').decode('ascii')

# Convert to DataFrame, drop any duplicates, and sort by largest population
dfSettlement = pd.DataFrame(lSettlements).drop_duplicates(subset = ['name'])
dfSettlement = dfSettlement.sort_values(by = 'population', ascending = False).head(numSettlements)
dfSettlement = dfSettlement.reset_index(drop = True)

lNames  = dfSettlement['name'].tolist()
lCoords = list(zip(dfSettlement['lat'], dfSettlement['lon']))

mD = np.zeros((numSettlements, numSettlements))

for ii, coordA in enumerate(lCoords):
    for jj, coordB in enumerate(lCoords):
        if ii == jj:
            continue  # Skip distance to self
        elif jj < ii:
            mD[ii, jj] = mD[jj, ii]  # Symmetric
        else:
            # Matches Google Maps' "Measure Distance" formula
            mD[ii, jj] = geodesic(coordA, coordB).kilometers

dfDistance = pd.DataFrame(mD, columns = lNames, index = lNames)

dfSettlement.to_csv(f'Israel{numSettlements}Settlements.csv')
dfDistance.to_csv(f'Israel{numSettlements}SettlementsGeodesicDistances.csv')

dfSettlement.to_parquet(f'Israel{numSettlements}Settlements.parquet')
dfDistance.to_parquet(f'Israel{numSettlements}SettlementsGeodesicDistances.parquet')
```