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| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| def repartition_par_pourcentiles(values, pct_step=5): | |
| """ | |
| Calcule la répartition par tranche de population et renvoie bornes, effectifs et proportions. | |
| """ | |
| series = pd.Series(values).dropna().sort_values().reset_index(drop=True) | |
| total = len(series) | |
| n_steps = int(100 / pct_step) | |
| edges = [i * pct_step / 100 for i in range(n_steps + 1)] | |
| groups = pd.qcut(series, q=edges, duplicates='drop') | |
| cats = groups.cat.categories | |
| counts = groups.value_counts().sort_index() | |
| low_bounds = [interval.left for interval in cats] | |
| high_bounds = [interval.right for interval in cats] | |
| proportions = (counts / total * 100).values | |
| return pd.DataFrame({ | |
| 'count': counts.values, | |
| 'proportion': proportions, | |
| 'threshold_low': low_bounds, | |
| 'threshold_high': high_bounds | |
| }) | |
| if __name__ == '__main__': | |
| df = pd.DataFrame({ | |
| 'transactions': [5, 12, 7, 20, 15, 3, 30, 8, 14, 10] | |
| }) | |
| pct_step = 5 | |
| stats = repartition_par_pourcentiles(df['transactions'], pct_step=pct_step) | |
| print(stats) | |
| # Graphe en pourcentages avec labels proportionnels | |
| fig, ax = plt.subplots() | |
| bars = ax.bar(range(len(stats)), stats['proportion']) | |
| ax.set_xlabel('') | |
| ax.set_ylabel('Proportion (%)') | |
| ax.set_title(f'Répartition par tranches de {pct_step}%') | |
| # Définir ticks et labels en fonction de la colonne 'proportion' | |
| percent_labels = [f"{p:.1f}%" for p in stats['proportion']] | |
| ax.set_xticks(range(len(stats))) | |
| ax.set_xticklabels(percent_labels, rotation=45) | |
| # Annotation : intervalle de valeurs pour chaque barre | |
| for i, row in stats.iterrows(): | |
| label = f"{int(row['threshold_low']):,}-{int(row['threshold_high']):,}" | |
| ax.text(i, row['proportion'] + 0.5, label, | |
| ha='center', va='bottom', fontsize=8, rotation=90) | |
| plt.tight_layout() | |
| plt.show() |
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