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This feature provides:
BASIC STATISTICS: - Total Models: {stats.get('count', 0)} - Mean Height: {stats.get('mean', 'N/A')} cm - Median Height: {stats.get('median', 'N/A')} cm - Height Range: {stats.get('min', 'N/A')} - {stats.get('max', 'N/A')} cm - Standard Deviation: {stats.get('std_dev', 'N/A')} cm height of male models
def category_fit(self) -> Dict[str, Dict]: """Categorize models based on industry standards""" results = {} This feature provides: BASIC STATISTICS: - Total Models:
return results
def plot_height_by_agency(self, agency_colors: Dict[str, str] = None): """Plot height distribution grouped by agency""" agency_groups = {} for model in self.analyzer.models: if model.agency: if model.agency not in agency_groups: agency_groups[model.agency] = [] agency_groups[model.agency].append(model.height_cm) 0)} - Mean Height: {stats.get('mean'
return { "count": len(self.heights), "mean": round(statistics.mean(self.heights), 1), "median": round(statistics.median(self.heights), 1), "mode": round(statistics.mode(self.heights), 1) if self.heights else None, "min": min(self.heights), "max": max(self.heights), "range": round(max(self.heights) - min(self.heights), 1), "std_dev": round(statistics.stdev(self.heights), 1) if len(self.heights) > 1 else 0, "variance": round(statistics.variance(self.heights), 1) if len(self.heights) > 1 else 0 }
def basic_statistics(self) -> Dict: """Calculate basic height statistics""" if not self.heights: return {}