results = {} for cat, heights in categories.items(): if heights: results[cat] = { "count": len(heights), "mean": round(statistics.mean(heights), 1), "range": f"{min(heights)}-{max(heights)}" }
def height_outliers(self, multiplier: float = 1.5) -> List[Dict]: """Detect height outliers using IQR method""" if len(self.heights) < 4: return []
for model in self.models: fits = []
def __init__(self, analyzer: MaleModelHeightAnalyzer): self.analyzer = analyzer self.heights = analyzer.heights
outliers = [] for model in self.models: if model.height_cm < lower_bound or model.height_cm > upper_bound: outliers.append({ "id": model.id, "name": model.name, "height_cm": model.height_cm, "height_ft_in": model.height_ft_in, "deviation": "below" if model.height_cm < lower_bound else "above" })
results = {} for cat, heights in categories.items(): if heights: results[cat] = { "count": len(heights), "mean": round(statistics.mean(heights), 1), "range": f"{min(heights)}-{max(heights)}" }
def height_outliers(self, multiplier: float = 1.5) -> List[Dict]: """Detect height outliers using IQR method""" if len(self.heights) < 4: return [] height of male models
for model in self.models: fits = []
def __init__(self, analyzer: MaleModelHeightAnalyzer): self.analyzer = analyzer self.heights = analyzer.heights results = {} for cat, heights in categories
outliers = [] for model in self.models: if model.height_cm < lower_bound or model.height_cm > upper_bound: outliers.append({ "id": model.id, "name": model.name, "height_cm": model.height_cm, "height_ft_in": model.height_ft_in, "deviation": "below" if model.height_cm < lower_bound else "above" }) results = {} for cat
Encrypted Gallery App for couples - Nudex
Want to meet couples with the similar interests? Join Opencouple
© 2026 Spicer Limited - Malta
