semvec/pypi-packages huggingface dataset:
chengxuyuan-mianshi-jindian-diliuban-quanjie-doocs-kaiyuan-shequ-fix1 2025.3.25.0
程序员面试金典第六版全解（Doocs 开源社区）
Requires Python: >=3.6

# 程序员面试金典第六版全解（Doocs 开源社区）

## 下载

### Docker

```
docker pull apachecn0/chengxuyuan-mianshi-jindian-diliuban-quanjie-doocs-kaiyuan-shequ
docker run -tid -p <port>:80 apachecn0/chengxuyuan-mianshi-jindian-diliuban-quanjie-doocs-kaiyuan-shequ
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install chengxuyuan-mianshi-jindian-diliuban-quanjie-doocs-kaiyuan-shequ
chengxuyuan-mianshi-jindian-diliuban-quanjie-doocs-kaiyuan-shequ <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g chengxuyuan-mianshi-jindian-diliuban-quanjie-doocs-kaiyuan-shequ
chengxuyuan-mianshi-jindian-diliuban-quanjie-doocs-kaiyuan-shequ <port>
# 访问 http://localhost:{port} 查看文档
```
jupyter-server-favicon 0.0.2
A Jupyter Server extension that adds a favicon to your Jupyter Server instance, making it easier to identify in browser tabs
Requires Python: >=3.9

# Jupyter Server Favicon Extension

[![PyPI version](https://badge.fury.io/py/jupyter-server-favicon.svg)](https://badge.fury.io/py/jupyter-server-favicon)
[![Python Versions](https://img.shields.io/pypi/pyversions/jupyter-server-favicon.svg)](https://pypi.org/project/jupyter-server-favicon/)
[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

> **Sponsored by [Enverge.ai](https://enverge.ai)** - Simpler, greener, cheaper AI training platform. Enverge harnesses excess green energy for powerful, cost-effective computing on GPUs, enabling environmentally friendly AI model development, training, and fine-tuning. Currently in private alpha with limited spots available.

A Jupyter Server extension that adds favicon support to your Jupyter Server instance, making it easier to identify your Jupyter tabs in the browser.

![demo](https://raw.githubusercontent.com/Enverge-Labs/jupyter-server-favicon/refs/heads/main/enverge-favicon.gif)

## Features

- Automatically serves a favicon for your Jupyter Server instance
- Easy to install and configure
- Compatible with all modern browsers
- No additional configuration required

## Installation

You can install the extension using pip:

```bash
pip install jupyter-server-favicon
```

Or if you want to install from source:

```bash
# Clone the repository
git clone https://github.com/Enverge-Labs/jupyter-server-favicon.git
cd jupyter-server-favicon

# Create a conda environment (optional but recommended)
conda create -y -n jupyter-server-favicon python=3.9
conda activate jupyter-server-favicon

# Install in development mode
pip install -e .
```

## Usage

The extension is automatically enabled after installation. Simply start Jupyter Server:

```bash
jupyter server
```

The favicon will be automatically served at `/static/jupyter_server_favicon/favicon.ico`.

## Development

To set up the development environment:

```bash
# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .
isort .

# Type checking
mypy .
```

## Contributing

We welcome contributions! Please feel free to submit a Pull Request.

## License

This project is licensed under the BSD 3-Clause License - see the [LICENSE](LICENSE) file for details.

## Support

If you encounter any issues or have questions, please file an issue on the [GitHub repository](https://github.com/Enverge-Labs/jupyter-server-favicon/issues).
ceh-v8-module-0-3-fix1 2025.3.25.0
CEH v8 Module 0~3
Requires Python: >=3.6

# CEH v8 Module 0~3

## 下载

### Docker

```
docker pull apachecn0/ceh-v8-module-0-3
docker run -tid -p <port>:80 apachecn0/ceh-v8-module-0-3
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ceh-v8-module-0-3
ceh-v8-module-0-3 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ceh-v8-module-0-3
ceh-v8-module-0-3 <port>
# 访问 http://localhost:{port} 查看文档
```
btex 0.4
A latex's superset
Requires Python: <4.0,>=3.9

# btex

A latex superset.

## How to install

You can simply install btex with the pip package manager
```python
pip install btex
```

## Get started

The language is very similar to latex.\
If you want to learn btex, take a look at the examples, I've also made a visual studio code extension for syntax highligthing and snippets.

## Macros

Macros are predefined variables in the btex interpreter which will be ignored by the program and instantly converted into the latex alternative.\
Macros are surrounded by double $, you can find a list of all the supported macros in the main python file of the interpreter.
\
Some examples of macros:
```
$$infinity$$ -> the infinity symbol
$$textwidth$$ -> the \textwidth unit
$$dot$$ -> the dot product symbol for math expressions
$$percent$$ -> since the % symbol is used in latex for comments, you can escape it using this macro
```

**NOTE**: if the document doesn't format as you want, you can force it to place some characters:
```
$$fs$$ -> forces a space
$$fn$$ -> forces a newline (putting \\)
```

## Env

Some latex libraries can have some variables to set for customization.
Since btex automatically imports used libraries without needing to include them manually, you can set some variables with the env.
\
You can set them in the file
```
@env {
    pdftitle = Document\
    var = value\
}
```
Or using a file
```
@env .env\
```
These are the variables you can change

| Var name    | Description |
| ----------- | ----------- |
| pdftitle    | Used in the hyperref package `\hypersetup{pdftitle={VAR}` |


## Using components not yet supported

Since this project is open source, if you want to use a component that is not available on btex, you can clone this repository and add it by yourself, or just open an issue asking me to do it. \
However, if you do really need a component, you can use the @latex attribute
```
@latex {
    your latex code
}
```
Everything you write in this block, will be ignored by the btex interpreter and will be put exactly how it is in the .tex output file.
ceh-v11-module-8-12-fix1 2025.3.25.0
CEH v11 Module 8~12
Requires Python: >=3.6

# CEH v11 Module 8~12

## 下载

### Docker

```
docker pull apachecn0/ceh-v11-module-8-12
docker run -tid -p <port>:80 apachecn0/ceh-v11-module-8-12
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ceh-v11-module-8-12
ceh-v11-module-8-12 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ceh-vyiyi-module-8-yier
ceh-vyiyi-module-8-yier <port>
# 访问 http://localhost:{port} 查看文档
```
ceh-v11-module-5-7-fix1 2025.3.25.0
CEH v11 Module 5~7
Requires Python: >=3.6

# CEH v11 Module 5~7

## 下载

### Docker

```
docker pull apachecn0/ceh-v11-module-5-7
docker run -tid -p <port>:80 apachecn0/ceh-v11-module-5-7
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ceh-v11-module-5-7
ceh-v11-module-5-7 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ceh-vyiyi-module-5-7
ceh-vyiyi-module-5-7 <port>
# 访问 http://localhost:{port} 查看文档
```
xiaoming-datahelper 0.1.3
一个简单的数据处理工具包
Requires Python: >=3.9

# Xiaoming DataHelper

一个简单的数据处理工具包，帮助你轻松处理 CSV 文件数据。

## 功能特点

- 支持相对路径和绝对路径的 CSV 文件读取
- 提供基本的数据统计功能
- 简单易用的 API 设计
- 完整的类型提示支持

## 安装

```bash
pip install xiaoming-datahelper
```

## 快速开始

```python
from datahelper import CSVReader, DataProcessor

# 读取 CSV 文件
reader = CSVReader("data.csv")
data = reader.read()

# 处理数据
processor = DataProcessor(data)
stats = processor.calculate_stats("age")
print(stats)
```

## 使用示例

### 使用相对路径

```python
from datahelper import CSVReader

# 使用相对路径（基于当前工作目录）
reader = CSVReader("data/sales.csv")
data = reader.read()
```

### 使用绝对路径

```python
from datahelper import CSVReader

# 使用绝对路径
reader = CSVReader("/absolute/path/to/data.csv")
data = reader.read()
```

### 指定基准目录

```python
from datahelper import CSVReader

# 指定基准目录
reader = CSVReader("data/sales.csv", base_dir="/path/to/project")
data = reader.read()
```

### 数据统计

```python
from datahelper import CSVReader, DataProcessor

# 读取数据
reader = CSVReader("data.csv")
data = reader.read()

# 计算统计信息
processor = DataProcessor(data)
age_stats = processor.calculate_stats("age")
salary_stats = processor.calculate_stats("salary")

print("年龄统计:", age_stats)
print("薪资统计:", salary_stats)
```

## 开发指南

### 环境设置

1. 克隆仓库：

```bash
git clone https://github.com/yourusername/xiaoming-datahelper.git
cd xiaoming-datahelper
```

2. 创建虚拟环境：

```bash
python -m venv venv
source venv/bin/activate  # Windows 使用: venv\Scripts\activate
```

3. 安装开发依赖：

```bash
pip install -e ".[dev]"
```

### 开发工具

项目提供了多个便捷的脚本：

1. 清理项目：

```bash
./scripts/clean.sh
```

2. 构建包：

```bash
./scripts/build.sh
```

3. 上传到 PyPI：

```bash
./scripts/upload.sh
```

### 运行测试

```bash
pytest tests/
```

## 项目结构

```
datahelper/
├── src/
│   └── datahelper/
│       ├── __init__.py
│       ├── core/
│       │   ├── __init__.py
│       │   ├── reader.py
│       │   └── processor.py
│       └── utils/
│           ├── __init__.py
│           └── helpers.py
├── tests/
│   ├── __init__.py
│   ├── test_reader.py
│   └── test_processor.py
├── examples/
│   └── basic_usage.py
└── scripts/
    ├── clean.py
    ├── build.py
    └── upload.py
```

## 贡献指南

1. Fork 项目
2. 创建特性分支 (`git checkout -b feature/AmazingFeature`)
3. 提交更改 (`git commit -m 'Add some AmazingFeature'`)
4. 推送到分支 (`git push origin feature/AmazingFeature`)
5. 创建 Pull Request

## 许可证

本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件

## 作者

- 小明 - [@xiaoming](https://github.com/xiaoming)

## 致谢

- 感谢所有贡献者的付出
- 特别感谢 pandas 和 numpy 团队