README

项目概述

🚀 基础数据处理框架 是一个基于 PyQt 的多功能数据处理框架，专为数据导入、预处理和可视化设计。此框架为 钻井漏失数据处理软件 v1.0.1 提供了基础结构document，展示了高效的多线程处理、状态栏实时更新等高级技术功能。

功能特点

• 状态栏：实时显示软件运行状态、提示信息及辅助信息。🛠️
• 多线程支持：提高数据处理效率，支持同时处理多个文件。⚡
• 数据导入：支持Excel和PDF文件的导入。📥
• 数据预处理：自动清洗、筛选和转换数据。🧹
• 数据可视化：提供多种图表类型，如折线图和散点图。📊
• 工作区域管理：支持多任务并行处理，允许创建和管理多个独立的工作区域。📂

技术细节

• PyQt：用于创建用户界面，使得界面直观友好。
• 多线程处理：通过Python的threading模块，实现数据处理的并行化，显著提高了处理速度。
• 数据可视化：使用matplotlib库生成多种类型的图表，帮助用户直观地分析数据。
• 数据处理：利用pandas库进行数据清洗、转换和合并，使得数据处理更加高效便捷。
• 状态栏集成：实时显示当前操作状态和进度，提供良好的用户体验。

安装与运行

1. 克隆仓库

   git clone https://github.com/huihuihenqiang/PYQTdataproject.git

2. 安装依赖

   pip install -r requirements.txt

3. 运行项目

   python main.py

贡献

欢迎贡献代码！请提交Pull Request，我们会尽快审核。

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README

Project Overview

🚀 Basic Data Processing Framework is a versatile data processing framework based on PyQt, designed for data import, preprocessing, and visualization. This framework serves as the foundational structure for the Drilling Loss Data Processing Software v1.0.1 document, showcasing advanced technical features such as efficient multithreading processing and real-time status bar updates.

Features

• Status Bar: Displays real-time software running status, prompt messages, and auxiliary information. 🛠️
• Multithreading Support: Improves data processing efficiency, supporting simultaneous processing of multiple files. ⚡
• Data Import: Supports importing Excel and PDF files. 📥
• Data Preprocessing: Automatically cleans, filters, and transforms data. 🧹
• Data Visualization: Provides various chart types, such as line charts and scatter plots. 📊
• Workspace Management: Supports multitasking parallel processing, allowing the creation and management of multiple independent workspaces. 📂

Technical Details

• PyQt: Used for creating the user interface, making it intuitive and user-friendly.
• Multithreading: Implemented using Python's threading module to parallelize data processing, significantly improving processing speed.
• Data Visualization: Utilized the matplotlib library to generate various types of charts, aiding users in intuitive data analysis.
• Data Processing: Employed the pandas library for data cleaning, transformation, and merging, making data handling more efficient and convenient.
• Status Bar Integration: Displays current operation status and progress in real-time, providing an excellent user experience.

Installation and Running

1. Clone the repository

   git clone https://github.com/huihuihenqiang/PYQTdataproject.git

2. Install dependencies

   pip install -r requirements.txt

3. Run the project

   python main.py

Contributing

We welcome contributions! Please submit a Pull Request, and we will review it as soon as possible.