[入門] PythonでuvとPEP 723を使うと開発体験が10倍向上する理由 #初心者 – Qiita

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.11"
# dependencies = [
#     "pandas>=2.0",
#     "matplotlib>=3.8",
#     "seaborn>=0.13",
# ]
# ///

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta

def generate_sales_data():
    """架空の売上データを生成"""
    dates = pd.date_range(
        start=datetime.now() - timedelta(days=30),
        end=datetime.now(),
        freq='D'
    )
    
    data = {
        '日付': dates,
        '売上': [50000 + i * 1000 + (i % 7) * 5000 for i in range(len(dates))],
        '来客数': [100 + i * 2 + (i % 7) * 10 for i in range(len(dates))],
    }
    return pd.DataFrame(data)

def main():
    df = generate_sales_data()
    df['客単価'] = df['売上'] / df['来客数']
    
    print("=== 売上統計（直近30日） ===")
    print(f"合計売上: {df['売上'].sum():,}円")
    print(f"平均日次売上: {df['売上'].mean():,.0f}円")
    print(f"最高売上日: {df.loc[df['売上'].idxmax(), '日付'].strftime('%Y-%m-%d')}")
    print(f"平均客単価: {df['客単価'].mean():,.0f}円")
    
    fig, axes = plt.subplots(2, 1, figsize=(10, 8))
    
    axes[0].plot(df['日付'], df['売上'], marker='o')
    axes[0].set_title('日次売上推移', fontsize=14)
    axes[0].set_ylabel('売上（円）')
    axes[0].grid(True, alpha=0.3)
    
    axes[1].scatter(df['来客数'], df['客単価'])
    axes[1].set_title('来客数と客単価の関係', fontsize=14)
    axes[1].set_xlabel('来客数')
    axes[1].set_ylabel('客単価（円）')
    axes[1].grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.savefig('sales_analysis.png', dpi=150, bbox_inches='tight')
    print("\nグラフを 'sales_analysis.png' に保存しました")

if __name__ == "__main__":
    main()