Height Of Male Models -

if not agency_groups: print("No agency data available") return

def __post_init__(self): if not self.height_ft_in and self.height_cm: self.height_ft_in = self.cm_to_ft_in(self.height_cm) height of male models

def plot_height_distribution(self, save_path: str = None): """Create histogram with KDE of height distribution""" fig, axes = plt.subplots(2, 2, figsize=(15, 10)) axes = plt.subplots(2

def height_outliers(self, multiplier: float = 1.5) -> List[Dict]: """Detect height outliers using IQR method""" if len(self.heights) < 4: return [] 10)) def height_outliers(self

# Category comparison cat_data = self.analyzer.distribution_by_category() categories = list(cat_data.keys()) means = [cat_data[cat]['mean'] for cat in categories] axes[1, 1].bar(categories, means, color=['blue', 'green', 'orange']) axes[1, 1].set_ylabel('Mean Height (cm)') axes[1, 1].set_title('Mean Height by Category') axes[1, 1].grid(True, alpha=0.3, axis='y')

: This is the most inclusive category. While being at least 5'9" is helpful, there are often no strict height requirements for commercial print, lifestyle, or fitness modeling.

# Color boxes if agency_colors: for i, box in enumerate(bp['boxes']): agency = list(agency_groups.keys())[i] box.set_facecolor(agency_colors.get(agency, 'lightblue'))