semvec/pypi-packages huggingface dataset:
warpdata 3.0.27
Remote-first dataset library for ML training
Requires Python: >=3.10

# WarpDatasets v3

Remote-first dataset library for ML training.

## Installation

```bash
pip install warpdata
```

## Quick Start

```python
import warpdata as wd

# Load a dataset
ds = wd.dataset("workspace/name")

# Get a table
table = ds.table("main")

# Preview data
print(table.head(5))

# Get schema
print(table.schema())
```

## CLI

```bash
# Show dataset info
warpdata info workspace/name

# Show schema
warpdata schema workspace/name

# Preview rows
warpdata head workspace/name -n 10
```
nz-house-prices 2.0.1
Scrape house price estimates from NZ real estate websites
Requires Python: >=3.9
Keywords: house prices, real estate, new zealand, scraping, property

# NZ House Prices

A Python package to scrape house price estimates from New Zealand real estate websites.

## Installation

```bash
pip install nz-house-prices
```

## Supported Sites

- homes.co.nz
- qv.co.nz
- propertyvalue.co.nz
- realestate.co.nz
- oneroof.co.nz

## Quick Start

### Command Line

```bash
# Search by address
nz-house-prices "123 Example Street, Ponsonby, Auckland"

# Search specific sites only
nz-house-prices "123 Main St, Auckland" --sites homes.co.nz,qv.co.nz

# Output as JSON
nz-house-prices "123 Example Street, Ponsonby" --json

# Use sequential mode (slower, but uses less resources)
nz-house-prices "123 Example Street" --sequential

# Skip cache for fresh results
nz-house-prices "123 Example Street" --no-cache

# List supported sites
nz-house-prices --list-sites
```

### Python API

```python
from nz_house_prices import get_prices

# Get prices from all sites (parallel by default, ~20-25s)
prices = get_prices("123 Example Street, Ponsonby, Auckland")

for site, estimate in prices.items():
    if estimate.midpoint:
        print(f"{site}: ${estimate.midpoint:,.0f}")

# Use sequential mode if needed
prices = get_prices("123 Example Street, Auckland", parallel=False)
```

### Context Manager (for multiple lookups)

```python
from nz_house_prices import HousePriceScraper

with HousePriceScraper() as scraper:
    prices1 = scraper.scrape_address("123 Example Street, Ponsonby")
    prices2 = scraper.scrape_address("123 Main St, Auckland")

    for site, estimate in prices1.items():
        print(f"{site}: {estimate.midpoint}")
```

## Features

- **Parallel scraping** - Queries all 5 sites concurrently (~20-25 seconds)
- **Geocoding-based address matching** - Accurately finds properties even when suburb names differ between sites
- **Automatic address-to-URL resolution** for all supported sites
- **URL caching** for faster repeated lookups
- **Rate limiting** to avoid overwhelming sites
- **Retry logic** with exponential backoff
- Both **CLI and Python API** interfaces

## Requirements

- Python 3.9+
- Playwright (installed automatically with the package)

After installing, run once to set up browsers:
```bash
playwright install chromium
```

## License

MIT License