init

analyzer.py

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+"""
+Analyzer Module
+
+This module provides functionality for analyzing data in the Starbucks Global Store Data Analysis Program.
+
+The Analyzer class contains methods for performing various analyses on the Starbucks store data,
+including calculating total stores, unique countries, and identifying top countries and cities.
+
+Classes:
+    Analyzer: A class containing methods for data analysis and result formatting.
+
+Dependencies:
+    pandas: For data manipulation and analysis.
+    typing: For type hinting.
+"""
+
+import pandas as pd
+from typing import Dict, Any
+
+class Analyzer:
+    """
+    A class for analyzing Starbucks store data.
+
+    This class provides methods to perform various analyses on the Starbucks store data
+    and format the results for presentation.
+
+    Methods:
+        analyze_data(df: pd.DataFrame) -> Dict[str, Any]:
+            Perform analysis on the given DataFrame and return the results.
+        format_analysis(analysis: Dict[str, Any]) -> str:
+            Format the analysis results into a human-readable string.
+    """
+
+    @staticmethod
+    def analyze_data(df: pd.DataFrame) -> Dict[str, Any]:
+        """
+        Perform analysis on the given DataFrame.
+
+        This method calculates various statistics from the Starbucks store data,
+        including total number of stores, number of countries, top country, and top city.
+
+        Args:
+            df (pd.DataFrame): The DataFrame containing the Starbucks store data.
+
+        Returns:
+            Dict[str, Any]: A dictionary containing the analysis results.
+                Keys include:
+                - 'total_stores': Total number of unique stores
+                - 'total_countries': Total number of unique countries
+                - 'top_country': Country with the most stores
+                - 'top_city': City with the most stores
+        """
+        return {
+            'total_stores': df['Store Number'].nunique(),
+            'total_countries': df['Country'].nunique(),
+            'top_country': df['Country'].value_counts().index[0],
+            'top_city': df['City'].value_counts().index[0]
+        }
+
+    @staticmethod
+    def format_analysis(analysis: Dict[str, Any]) -> str:
+        return (f"星巴克在全球共有 {analysis['total_stores']} 家店铺，"
+                f"分布在 {analysis['total_countries']} 个国家和地区，"
+                f"店铺数量最多的国家是 {analysis['top_country']}，"
+                f"城市是 {analysis['top_city']}。")