init

main.py

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+"""
+Starbucks Analysis Program
+
+HangZhou Dianzi University 2023-2024 Semester Final Project
+
+This module serves as the main entry point for the program, coordinating the work of various components
+including data processing, visualization, and analysis.
+
+Main functionalities:
+1. Read and process Starbucks global store data
+2. Perform data cleaning and transformation
+3. Generate data visualization charts
+4. Conduct data analysis and output results
+
+Dependencies:
+- data_processor: For data processing tasks
+- visualizer: For data visualization
+- analyzer: For data analysis
+- config_manager: For managing configuration information
+"""
+
+from data_processor import DataProcessor, fill_city
+from visualizer import Visualizer
+from analyzer import Analyzer
+from config_manager import ConfigManager
+
+def main():
+    """
+    Main function that coordinates the execution flow of the entire program.
+
+    Key steps:
+    1. Initialize the configuration manager
+    2. Read and process data
+    3. Display data statistics
+    4. Perform data cleaning and transformation
+    5. Save the processed data
+    6. Generate visualization charts
+    7. Process Starbucks data for China
+    8. Conduct data analysis and output results
+
+    No parameters, no return value
+    """
+
+    config = ConfigManager()
+
+    processor = DataProcessor(config.get('input_file'))
+    print("前五行数据：")
+    print(processor.head)
+
+    print("\n缺失值情况：")
+    print(processor.missing_data)
+
+    print("\nCity字段缺失的数据：")
+    print(processor.city_null)
+
+    processor.apply_function(fill_city, 'City')
+
+    print("\n填充后的埃及(EG)数据：")
+    print(processor.filter_by_country('EG'))
+
+    unique_brands = processor.df['Brand'].unique()
+    print("\n唯一的Brand值：", unique_brands)
+
+    processor.replace_values('Brand', dict.fromkeys(unique_brands, 'Starbucks'))
+
+    processor.save_to_csv(config.get('output_file'))
+    print(f"\n数据已保存到{config.get('output_file')}")
+
+    Visualizer.plot_top_countries(processor.df)
+    Visualizer.plot_top_cities(processor.df)
+
+    cn_starbucks = processor.filter_by_country('CN')
+    print("\n中国星巴克分布（前五行）：")
+    print(cn_starbucks.head())
+
+    cn_starbucks.to_csv(config.get('cn_output_file'), index=False)
+    print(f"\n中国星巴克数据已保存到{config.get('cn_output_file')}")
+
+    Visualizer.plot_top_cn_cities(cn_starbucks)
+
+    analyzer = Analyzer()
+    analysis = analyzer.analyze_data(processor.df)
+    print(analyzer.format_analysis(analysis))
+
+if __name__ == "__main__":
+    main()