semvec/pypi-packages huggingface dataset:
ML-Automation-Tool 1.0.0
Automated Machine Learning tool with GUI
Requires Python: >=3.8

# ML Automation Tool
Automatically train and evaluate multiple ML models from a CSV dataset.

## How to use GUI
1. Run `python gui_main.py`
2. Select your CSV file and enter the target column
3. Click "Run"
4. Results appear in the output box, and the best model is saved automatically
nix-manipulator 0.1.3
A Python library for parsing, manipulating, and reconstructing Nix source code with high-level abstractions while preserving formatting and comments in RFC-formatted code.
Requires Python: >=3.11
Keywords: ast, code-manipulation, nix, parsing

# Nix-manipulator (Nima)

A Python library and tools for parsing, manipulating, and reconstructing Nix source code.

## Overview

Started during SaltSprint 2025, Nix-manipulator aims to fill the absence of tools for easily
updating and editing Nix code. 
Popular tools such as [nix-update](https://github.com/Mic92/nix-update) rely on 
[simple string replacement](https://github.com/Mic92/nix-update/blob/fbb35af0ed032ab634c7ef9018320d2370ecfeb1/nix_update/update.py#L26)
or regular expressions for updating Nix code.

## Features and Goals

- **Ease of use** - Simple CLI and API for common operations.
- **High-level abstractions** make manipulating expressions easy.
- **Preserving formatting and comments** in code that respects RFC-166.

## Non-goals

- Preserving eccentric formatting that does not respect RFC-166 and would add unnecessary complexity.

## Targeted applications

- Updating values in Nix code by hand, scripts, pipelines, and frameworks.
- Writing refactoring tools.
- Interactive modifications from a REPL.

## Foundations

Nix-manipulator leverages [tree-sitter](https://tree-sitter.github.io/tree-sitter/)
, a multilingual concrete-syntax AST, and its Nix grammar [tree-sitter-nix](https://github.com/nix-community/tree-sitter-nix).

## Project Status

The project is still in early-stage:

- Not all Nix syntax is supported yet
- Test-driven approach prevents regressions
- CLI and API are still evolving and subject to change
- 28991 / 39573 (73.26%) Nix files from nixpkgs could be parsed and reproduced

## Target Audience

Intermediate Nix users and developers working with Nix code manipulation.

## CLI Usage

Nix-manipulator provides a command-line interface for common operations:

Set a value in a Nix file
```shell
nima set -f package.nix version '"1.2.3"'
```

Set a boolean value
```shell
nima set -f package.nix doCheck true
```

Remove an attribute
```shell
nima rm -f package.nix doCheck
```

Test/validate that a Nix file can be parsed
```shell
nima test -f package.nix
```
yhd-depchecker 1.0.0
用于检测当前代码环境是否拥有开发者所要求的库，并安装。可用于直接分发代码快捷安装
Requires Python: >=3.8
Keywords: dependency, checker, installer, package management, environment

# DepChecker

一个轻量级的Python库，用于快速检测和安装项目所需的依赖包。

## 功能特性

- ✅ **依赖检测** - 检查包是否安装及版本是否符合要求
- 🔧 **自动安装** - 一键安装所有缺失或需要更新的包
- 📊 **详细报告** - 清晰的检查结果和状态显示
- 🚀 **简单易用** - 提供类接口和便捷函数两种使用方式
- 🔄 **版本控制** - 支持最低版本要求检查

## 安装

```bash
pip install depchecker
```

## 快速开始

### 方式一：使用便捷函数

```python
from depchecker import quick_check

# 定义项目依赖
REQUIREMENTS = {
    "requests": "2.31.0",
    "numpy": "1.21.0", 
    "pandas": "1.3.0",
    "matplotlib": "3.4.0"
}

# 快速检查并自动安装
ready = quick_check(REQUIREMENTS, auto_install=True, verbose=True)

if ready:
    print("✅ 环境准备就绪！")
else:
    print("❌ 环境准备失败")
```

### 方式二：使用类接口

```python
from depchecker import DependencyChecker

# 创建检测器实例
checker = DependencyChecker({
    "flask": "2.0.0",
    "sqlalchemy": "1.4.0",
    "redis": "4.0.0"
})

# 检查所有依赖
results = checker.check_all()
print("检查结果:", results)

# 安装缺失的包
if not checker.is_ready():
    checker.install_all(verbose=True)
```

## API 参考

### `DependencyChecker` 类

#### `__init__(requirements: Dict[str, str])`
初始化依赖检测器。

- `requirements`: 依赖包字典，格式为 `{包名: 最低版本}`

#### `check_package(package_name: str, min_version: Optional[str] = None) -> Tuple[bool, str, str]`
检查单个包的安装状态。

- 返回: `(是否安装, 当前版本, 状态信息)`

#### `check_all() -> Dict[str, Tuple[bool, str, str]]`
检查所有要求的依赖包。

- 返回: 字典格式的检查结果 `{包名: (是否安装, 版本, 状态)}`

#### `install_all(verbose: bool = False) -> Dict[str, bool]`
安装所有缺失和需要更新的包。

- 返回: 安装结果字典 `{包名: 是否成功}`

#### `is_ready() -> bool`
检查所有依赖是否都已满足。

### `quick_check(requirements: Dict[str, str], auto_install: bool = False, verbose: bool = False) -> bool`
快速检查依赖的便捷函数。

- `auto_install`: 是否自动安装缺失的包
- `verbose`: 是否显示详细输出
- 返回: 所有依赖是否就绪

## 示例输出

```
📦 依赖库检查:
--------------------------------------------------
requests      2.28.0       ✓ (需要: 2.31.0+)
numpy         1.20.0       ✗ 需要版本 >= 1.21.0 (需要: 1.21.0+)
pandas        未安装       ✗ (需要: 1.3.0+)
--------------------------------------------------
🔧 自动安装缺失的库...
安装 numpy>=1.21.0...
✓ numpy 安装成功
安装 pandas>=1.3.0...
✓ pandas 安装成功
🎉 所有依赖库安装完成！
✅ 环境准备就绪，可以开始开发！
```

## 贡献

欢迎提交 Issue 和 Pull Request！

## 许可证

MIT License

## 支持版本

- Python 3.7+
- 支持 Windows、macOS、Linux