init

visualizer.py

@@ -0,0 +1,103 @@
+"""
+Visualizer Module
+
+This module provides functionality for creating data visualizations for the Starbucks Global Store Data Analysis Program.
+
+The Visualizer class contains static methods for generating various plots using matplotlib and seaborn,
+including bar plots for top countries, cities, and Chinese cities with the most Starbucks stores.
+
+Classes:
+    Visualizer: A class containing static methods for data visualization.
+
+Dependencies:
+    matplotlib.pyplot: For creating plots.
+    seaborn: For enhanced plot styling.
+    typing: For type hinting.
+    pandas: For data manipulation and analysis.
+"""
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from typing import List, Tuple
+import pandas as pd
+
+plt.rcParams['font.sans-serif'] = ['SimHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+class Visualizer:
+    """
+    A class for creating data visualizations.
+
+    This class provides static methods to generate various plots for analyzing Starbucks store data,
+    including bar plots for top countries, cities, and Chinese cities with the most stores.
+
+    Methods:
+        plot_top_n(data: pd.Series, n: int, title: str, xlabel: str, ylabel: str, horizontal: bool = False) -> None:
+            Create a bar plot of the top n items in a series.
+        plot_top_countries(df: pd.DataFrame, n: int = 10) -> None:
+            Create a bar plot of the top n countries with the most Starbucks stores.
+        plot_top_cities(df: pd.DataFrame, n: int = 10) -> None:
+            Create a bar plot of the top n cities with the most Starbucks stores.
+        plot_top_cn_cities(df: pd.DataFrame, n: int = 10) -> None:
+            Create a horizontal bar plot of the top n Chinese cities with the most Starbucks stores.
+    """
+
+    @staticmethod
+    def plot_top_n(data: pd.Series, n: int, title: str, xlabel: str, ylabel: str, horizontal: bool = False) -> None:
+        """
+        Create a bar plot of the top n items in a series.
+
+        Args:
+            data (pd.Series): The data series to plot.
+            n (int): The number of top items to include in the plot.
+            title (str): The title of the plot.
+            xlabel (str): The label for the x-axis.
+            ylabel (str): The label for the y-axis.
+            horizontal (bool, optional): Whether to create a horizontal bar plot. Defaults to False.
+        """
+        plt.figure(figsize=(12, 6))
+        if horizontal:
+            sns.barplot(y=data.index[:n], x=data.values[:n])
+        else:
+            sns.barplot(x=data.index[:n], y=data.values[:n])
+        plt.title(title)
+        plt.xlabel(xlabel)
+        plt.ylabel(ylabel)
+        plt.xticks(rotation=45 if not horizontal else 0)
+        plt.show()
+
+    @classmethod
+    def plot_top_countries(cls, df: pd.DataFrame, n: int = 10) -> None:
+        """
+        Create a bar plot of the top n countries with the most Starbucks stores.
+
+        Args:
+            df (pd.DataFrame): The DataFrame containing the Starbucks store data.
+            n (int, optional): The number of top countries to include. Defaults to 10.
+        """
+        top_countries = df['Country'].value_counts().nlargest(n)
+        cls.plot_top_n(top_countries, n, f'店铺数量排名前{n}的国家', '国家', '店铺数量')
+
+    @classmethod
+    def plot_top_cities(cls, df: pd.DataFrame, n: int = 10) -> None:
+        """
+        Create a bar plot of the top n cities with the most Starbucks stores.
+
+        Args:
+            df (pd.DataFrame): The DataFrame containing the Starbucks store data.
+            n (int, optional): The number of top cities to include. Defaults to 10.
+        """
+        top_cities = df['City'].value_counts().nlargest(n)
+        cls.plot_top_n(top_cities, n, f'店铺数量排名前{n}的城市', '城市', '店铺数量')
+
+    @classmethod
+    def plot_top_cn_cities(cls, df: pd.DataFrame, n: int = 10) -> None:
+        """
+        Create a horizontal bar plot of the top n Chinese cities with the most Starbucks stores.
+
+        Args:
+            df (pd.DataFrame): The DataFrame containing the Starbucks store data for China.
+            n (int, optional): The number of top cities to include. Defaults to 10.
+        """
+        top_cn_cities = df['City'].value_counts().nlargest(n)
+        cls.plot_top_n(top_cn_cities, n, f'中国星巴克店铺数量前{n}的城市', '店铺数量', '城市', horizontal=True)