Nitzan Pomerantz
c87750f57f
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
"""
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Statistical calculation functions for deal data.
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This module provides pure mathematical functions for analyzing real estate deal data.
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"""
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from collections import Counter
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from typing import Any, Dict, List
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def calculate_deal_statistics(deals: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Calculate statistical aggregations on deal data.
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Args:
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deals: List of deal dictionaries
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Returns:
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Dictionary with statistical metrics
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Raises:
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ValueError: If deals is not a valid list
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"""
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if not isinstance(deals, list):
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raise ValueError("deals must be a list")
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if not deals:
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return {
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"count": 0,
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"price_stats": {},
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"area_stats": {},
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"price_per_sqm_stats": {},
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"room_distribution": {},
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}
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# Extract numeric values
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prices = []
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areas = []
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price_per_sqm_values = []
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rooms = []
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for deal in deals:
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price = deal.get("dealAmount")
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if isinstance(price, (int, float)) and price > 0:
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prices.append(price)
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area = deal.get("assetArea")
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if isinstance(area, (int, float)) and area > 0:
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areas.append(area)
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pps = deal.get("price_per_sqm")
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if pps is None and price and area and area > 0:
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pps = price / area
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if isinstance(pps, (int, float)) and pps > 0:
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price_per_sqm_values.append(pps)
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room_count = deal.get("assetRoomNum")
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if isinstance(room_count, (int, float)):
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rooms.append(room_count)
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# Calculate statistics
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stats: Dict[str, Any] = {"count": len(deals)}
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# Price statistics
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if prices:
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sorted_prices = sorted(prices)
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stats["price_stats"] = {
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"mean": round(sum(prices) / len(prices), 2),
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"median": (sorted_prices[len(sorted_prices) // 2] + sorted_prices[(len(sorted_prices) - 1) // 2]) / 2,
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"min": min(prices),
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"max": max(prices),
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"p25": sorted_prices[len(sorted_prices) // 4],
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"p75": sorted_prices[(3 * len(sorted_prices)) // 4],
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"std_dev": round(calculate_std_dev(prices), 2) if len(prices) > 1 else 0,
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"total": sum(prices),
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}
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# Area statistics
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if areas:
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sorted_areas = sorted(areas)
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stats["area_stats"] = {
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"mean": round(sum(areas) / len(areas), 2),
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"median": sorted_areas[len(sorted_areas) // 2],
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"min": min(areas),
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"max": max(areas),
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"p25": sorted_areas[len(sorted_areas) // 4],
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"p75": sorted_areas[(3 * len(sorted_areas)) // 4],
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}
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# Price per sqm statistics
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if price_per_sqm_values:
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sorted_pps = sorted(price_per_sqm_values)
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stats["price_per_sqm_stats"] = {
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"mean": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 2),
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"median": round(sorted_pps[len(sorted_pps) // 2], 2),
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"min": round(min(price_per_sqm_values), 2),
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"max": round(max(price_per_sqm_values), 2),
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"p25": round(sorted_pps[len(sorted_pps) // 4], 2),
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"p75": round(sorted_pps[(3 * len(sorted_pps)) // 4], 2),
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}
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# Room distribution
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if rooms:
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room_counts = Counter(rooms)
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stats["room_distribution"] = dict(sorted(room_counts.items()))
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return stats
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def calculate_std_dev(values: List[float]) -> float:
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"""
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Calculate standard deviation of a list of values.
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Args:
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values: List of numeric values
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Returns:
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Standard deviation
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"""
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if len(values) < 2:
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return 0.0
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mean = sum(values) / len(values)
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variance = sum((x - mean) ** 2 for x in values) / (len(values) - 1)
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return variance**0.5
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