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# 星巴克店铺数据分析
> 本项目是杭州电子科技大学`Python程序设计`的课程期末作业。
本项目旨在对星巴克全球店铺数据进行分析，包括数据导入、清洗、探索和可视化。
## 文件结构
```shell
.
├── LICENCE
├── README.md
├── analyzer.py
├── config_manager.py
├── config_template.json
├── data_processor.py
├── main.py
├── requirements.txt
└── visualizer.py
```
## 安装
您需要一个能工作的`Python 3`执行环境。
1. 克隆仓库：
```shell
git clone https://github.com/HChaZZY/starbucks-analysis.git
cd starbucks-analysis
```
2. 安装依赖：
```shell
pip install -r requirements.txt
```
## 使用方法
1. 复制 `config_template.json` 并重命名为 `config.json`。
2. 在 `config.json` 中填写必要的配置信息。
3. 运行主程序：
```shell
python main.py
```
## 配置
在 `config.json` 中，您可以设置以下参数：
- 输入文件(csv)路径
- 输出文件(csv)路径，包括清洗输出和中国店铺输出
具体配置选项请参考 `config_template.json`。
## 许可证
本项目基于 AGPL（GNU Affero General Public License）协议开源。详情请参阅 [LICENCE](LICENCE) 文件。
## 贡献
欢迎于 `Github Issues` 提交问题和拉取请求。
## 致谢
本项目使用了以下开源库，在此对这些优秀工具的开发者们表示衷心的感谢：
- pandas
- matplotlib
- seaborn
我们深深感谢开源社区的贡献，是这些贡献使得这样的项目成为可能。
## 联系方式
如有任何问题或建议，请联系[电子邮件](mailto:i@hcha.top)

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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']}。")

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"""
Configuration Manager Module
This module provides a ConfigManager class for managing configuration settings for the Starbucks Global Store Data Analysis Program.
The ConfigManager class handles reading from and writing to a JSON configuration file, allowing easy access and modification of program settings.
Classes:
ConfigManager: Manages configuration settings stored in a JSON file.
Dependencies:
json: For reading and writing JSON files.
typing: For type hinting.
"""
import json
from typing import Dict, Any
class ConfigManager:
"""
A class for managing configuration settings.
This class provides methods to read from and write to a JSON configuration file,
as well as to get and set individual configuration values.
Attributes:
config (Dict[str, Any]): A dictionary containing the configuration settings.
Methods:
__init__(self, config_file: str = 'config.json'): Initialize the ConfigManager with a specified config file.
get(self, key: str) -> Any: Retrieve a configuration value by its key.
set(self, key: str, value: Any) -> None: Set a configuration value for a given key.
save(self, config_file: str = 'config.json') -> None: Save the current configuration to a JSON file.
"""
def __init__(self, config_file: str = 'config.json'):
"""
Initialize the ConfigManager with a specified config file.
Args:
config_file (str): The path to the JSON configuration file. Defaults to 'config.json'.
"""
with open(config_file, 'r') as f:
self.config: Dict[str, Any] = json.load(f)
def get(self, key: str) -> Any:
"""
Retrieve a configuration value by its key.
Args:
key (str): The key of the configuration setting to retrieve.
Returns:
Any: The value associated with the given key, or None if the key doesn't exist.
"""
return self.config.get(key)
def set(self, key: str, value: Any) -> None:
"""
Set a configuration value for a given key.
Args:
key (str): The key of the configuration setting to set.
value (Any): The value to associate with the given key.
"""
self.config[key] = value
def save(self, config_file: str = 'config.json') -> None:
"""
Save the current configuration to a JSON file.
Args:
config_file (str): The path where the configuration file should be saved. Defaults to 'config.json'.
"""
with open(config_file, 'w') as f:
json.dump(self.config, f, indent=4)

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{
"input_file": "输入文件(csv)位置",
"output_file": "清洗数据输出(csv)位置",
"cn_output_file": "我国星巴克店铺分布输出(csv)位置"
}

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"""
Data Processor Module
This module provides functionality for processing and manipulating data for the Starbucks Global Store Data Analysis Program.
The DataProcessor class handles various data processing tasks such as reading CSV files, applying functions to columns,
replacing values, filtering data, and saving processed data.
Classes:
DataProcessor: Manages data processing operations on a pandas DataFrame.
Functions:
fill_city: A helper function to fill missing city values with state/province values.
Dependencies:
pandas: For data manipulation and analysis.
typing: For type hinting.
functools: For partial function application.
config_manager: For managing configuration settings.
"""
import pandas as pd
from typing import Callable, Any
from functools import partial
from config_manager import ConfigManager
class DataProcessor:
"""
A class for processing and manipulating data stored in a pandas DataFrame.
This class provides methods to read data from CSV files, apply functions to columns,
replace values, filter data, and save processed data.
Attributes:
config (ConfigManager): An instance of ConfigManager for handling configuration settings.
df (pd.DataFrame): The pandas DataFrame containing the data to be processed.
Methods:
__init__(self, file_path: str): Initialize the DataProcessor with data from a CSV file.
apply_function(self, func: Callable[[pd.Series], Any], column: str) -> None: Apply a function to a specific column.
replace_values(self, column: str, value_map: dict) -> None: Replace values in a column based on a mapping.
filter_by_country(self, country_code: str) -> pd.DataFrame: Filter the DataFrame by country code.
save_to_csv(self, file_path: str) -> None: Save the processed DataFrame to a CSV file.
head(self) -> pd.DataFrame: Get the first few rows of the DataFrame.
missing_data(self) -> pd.Series: Get information about missing data in the DataFrame.
city_null(self) -> pd.DataFrame: Get rows where the 'City' column is null.
"""
def __init__(self, file_path: str):
"""
Initialize the DataProcessor with data from a CSV file.
Args:
file_path (str): The path to the CSV file to be processed.
"""
self.config = ConfigManager()
self.df = pd.read_csv(file_path)
def apply_function(self, func: Callable[[pd.Series], Any], column: str) -> None:
"""
Apply a function to a specific column in the DataFrame.
Args:
func (Callable[[pd.Series], Any]): The function to apply to the column.
column (str): The name of the column to apply the function to.
"""
self.df[column] = self.df.apply(func, axis=1)
def replace_values(self, column: str, value_map: dict) -> None:
"""
Replace values in a column based on a mapping.
Args:
column (str): The name of the column to replace values in.
value_map (dict): A dictionary mapping old values to new values.
"""
self.df[column] = self.df[column].replace(value_map)
def filter_by_country(self, country_code: str) -> pd.DataFrame:
"""
Filter the DataFrame by country code.
Args:
country_code (str): The country code to filter by.
Returns:
pd.DataFrame: A new DataFrame containing only rows for the specified country.
"""
return self.df[self.df['Country'] == country_code]
def save_to_csv(self, file_path: str) -> None:
"""
Save the processed DataFrame to a CSV file.
Args:
file_path (str): The path where the CSV file should be saved.
"""
self.df.to_csv(file_path, index=False)
self.config.set('output_file', file_path)
self.config.save()
@property
def head(self) -> pd.DataFrame:
"""
Get the first few rows of the DataFrame.
Returns:
pd.DataFrame: The first few rows of the DataFrame.
"""
return self.df.head()
@property
def missing_data(self) -> pd.Series:
"""
Get information about missing data in the DataFrame.
Returns:
pd.Series: A series containing the count of missing values for each column with missing data.
"""
return self.df.isnull().sum()[lambda x: x > 0]
@property
def city_null(self) -> pd.DataFrame:
"""
Get rows where the 'City' column is null.
Returns:
pd.DataFrame: A DataFrame containing rows where the 'City' column is null.
"""
return self.df[self.df['City'].isnull()]
def fill_city(row: pd.Series) -> str:
"""
Fill missing city values with state/province values.
Args:
row (pd.Series): A row from the DataFrame.
Returns:
str: The city value if not null, otherwise the state/province value.
"""
return row['State/Province'] if pd.isnull(row['City']) else row['City']

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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()

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pandas
matplotlib
seaborn

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"""
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)