commit 2137d41d0b1158c74b462b77465d7f219e310e6c Author: HCha Date: Tue Jun 25 16:01:57 2024 +0800 init diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c14bb49 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +data/ +__pycache__/ +.vscode/ +config.json \ No newline at end of file diff --git a/LICENCE b/LICENCE new file mode 100644 index 0000000..be3f7b2 --- /dev/null +++ b/LICENCE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..8c54f5e --- /dev/null +++ b/README.md @@ -0,0 +1,78 @@ +# 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) diff --git a/analyzer.py b/analyzer.py new file mode 100644 index 0000000..60433a5 --- /dev/null +++ b/analyzer.py @@ -0,0 +1,65 @@ +""" +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']}。") diff --git a/config_manager.py b/config_manager.py new file mode 100644 index 0000000..d895c3b --- /dev/null +++ b/config_manager.py @@ -0,0 +1,76 @@ +""" +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) diff --git a/config_template.json b/config_template.json new file mode 100644 index 0000000..a31dff4 --- /dev/null +++ b/config_template.json @@ -0,0 +1,5 @@ +{ + "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" +} \ No newline at end of file diff --git a/data_processor.py b/data_processor.py new file mode 100644 index 0000000..e49b562 --- /dev/null +++ b/data_processor.py @@ -0,0 +1,142 @@ +""" +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'] diff --git a/main.py b/main.py new file mode 100644 index 0000000..8b4df6c --- /dev/null +++ b/main.py @@ -0,0 +1,86 @@ +""" +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() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3b9a24f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +pandas +matplotlib +seaborn \ No newline at end of file diff --git a/visualizer.py b/visualizer.py new file mode 100644 index 0000000..29b81df --- /dev/null +++ b/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)