semvec/pypi-packages huggingface dataset:
bcp-exorcist 0.2.1
Cleans up SQL Server's bcp Export
Requires Python: >=3.9
Keywords: bcp, csv

# 🧹 bcp-exorcist

**Fix malformed CSVs exported with broken delimiters and newlines from legacy systems.**

`bcp-exorcist` is a Python extension written in Rust that "exorcises" broken CSV files using uncommon delimiters and newline characters. It processes files in memory-efficient batches and transforms them into proper, escaped CSV files ready for analysis or ingestion.

## ✨ Features

- Efficient batch processing of large CSV files.
- Handles broken CSVs with custom delimiters (e.g. `\x1E`) and newlines (e.g. `\x1D`).
- Recovers valid CSV structure with correct escaping.
- Rust-powered speed with Python simplicity.

## 📦 Installation

```bash
pip install bcp-exorcist
```

## ☦️ Example

```python
from bcp_exorcist import exorcize_csv

try:
    exorcize_csv("path/to/broken.csv", delim=b'\x1E', newline=b'\x1D', chunk_size=1024 * 1024)
    print("Exorcism completed successfully!")

except TypeError:
    print("`delim` and `newline` must be single-byte values.")
except FileNotFoundError:
    print("Check that the filepath is correct.")
except RuntimeError as e:
    print(f"Exorcism failed: {e}")
```
dqframework 0.6.1
A dq framework
Requires Python: >=3.8

# dqframework is a Python package that provides a framework for data quality assessment and monitoring.

This package is designed with polars in mind, but in the future it might get expanded.

## Another framework?

This framework is designed to be used in a similar way to many others, but it has been designed with some goals in mind:

- **Observability**: The framework should provide a way to monitor the data quality of a dataset in a way that is easy
  to understand and to act upon.
- **Extensibility**: The framework should be easy to extend with new checks and new ways to monitor the data quality.
- **Performance**: The framework should be able to handle large datasets and provide a way to monitor the data quality
  of these datasets in a performant way.
- **Ease of use**: The framework should be easy to use and to understand, so that it can be used by data engineers, data
  scientists, and other data professionals.
- **Monitoring and Reporting**: The framework should provide a way to monitor the data quality of a dataset over time
  and to report on the data quality of the dataset.

## How to use it?

This framework is centered around three main concepts:

- Pipeline: A pipeline is an object that comprises multiple Checks, and is responsible for running these checks on a
  dataframe.
- Check: A check is an object that comprises multiple Validators, and is responsible for checking a certain set of
  properties of a dataframe. It has severity levels, and can be used to monitor the data quality of a dataset.
- Validator: A validator is a function that is responsible for validating a certain property of a dataframe

## Installation

To install the package, you can use pip:

```bash
pip install dqframework
```
pritty-logger 0.3.4
A logger with rich formatting
Requires Python: >=3.6

[![Tests](https://github.com/leon-gorissen/pritty_logger/actions/workflows/test.yml/badge.svg)](https://github.com/leon-gorissen/pritty_logger/actions/workflows/test.yml)
[![Build](https://github.com/leon-gorissen/pritty_logger/actions/workflows/build.yml/badge.svg)](https://github.com/leon-gorissen/pritty_logger/actions/workflows/build.yml)
[![Python package](https://github.com/leon-gorissen/pritty_logger/actions/workflows/publish.yml/badge.svg)](https://github.com/leon-gorissen/pritty_logger/actions/workflows/publish.yml)

# Pritty logger

A simple logger that logs to console using the rich library and to file in /var/log/ or to ~/.log/. Created to simplify setting up logging in containerized development and deployment.

## Installation 

```bash
pip install pritty_logger
``` 

## Usage

```python
from pritty_logger import RichLogger

logger = RichLogger("example")
logger.log("This is an info message")
logger.log({"key": "value"}, level="debug")
```

Supports levels "debug", "info", "warning", "error", "critical". Defaults to "info".
image-validator 0.1.0
A package to detect fake or low-quality images using OpenCV and Tesseract
Requires Python: >=3.6

# image-validator

A Python library to validate images for face presence, image quality, and detect signs of tampering like overlaid text (e.g., branding, timestamps). Useful for applications like loan verification, identity checks, and fraud detection.

---

## 🚀 Features

- ✅ Detect if image contains a face
- ✅ Check image sharpness and quality
- ✅ Detect overlaid text (e.g., screenshot labels, mobile branding)
- ✅ Combine all checks into one simple function

---

## 📦 Installation

You can install the package using pip:

```bash
pip install image-validator
guardionsdk 0.1.0
The Guardion AI SDK
Requires Python: >=3.11

# Guardion.AI SDK

## How to use?

### OpenAI Agents SDK

You need to install our SDK using our openai-agents extras with the following command:

```bash
pip install guardionsdk[openai_agents]
```

And use it as shown in the file `examples/openai_agents.sdk`.

### LangChain

We support Chat and simple LLM models from LangChain, in order to use it, you need to install our langchain extra.

```bash
pip install guardionsdk[langchain]
```

And in order to use it, just checkout our `examples/langhchain.py` file.
AgentCP 0.0.2
连接Au互联网络的库，让你的应用可以连接到Au网络
Requires Python: >=3.6
Keywords: Agent Communication Protocol

我将为你生成一份完整的 `README.md` 文件，基于你提供的库功能和最佳实践：

```markdown:d:\client\agentid\README.md
# AgentID 网络连接库

一个用于连接 Au 互联网络的 Python 库，让你的应用可以连接到 AgentID 网络并进行安全通信。

## 功能特性

- ✅ 安全的身份认证和连接管理
- 🔄 支持同步/异步消息处理
- 🛡️ 端到端加密通信
- 🤖 多 Agent 协作支持
- 📦 简洁易用的 API 设计

## 安装

```bash
pip install agentid
```

## 快速入门

### 初始化客户端

```python
import agentid import

# 初始化客户端
client = agentid.AgentId()
```

### 创建和管理身份

```python
# 创建新身份
new_aid = "yourname"
agent = client.create_aid(new_aid):

# 加载现有身份
agent = client.load_aid(new_aid)
```

### 连接到网络

```python
# 初始化
agent.initialize()

# 设置消息处理器
async def message_handler(msg):
    print(f"收到消息: {msg}")
agent.add_message_handler(message_handler)

# 上线
agent.online()
```

## 核心 API

### AgentIdCilent 类

| 方法 | 描述 |
|------|------|
| `create_aid(aid)` | 创建新身份 |
| `load_aid(aid)` | 加载现有身份 |
| `get_agentid_list()` | 获取身份列表 |

### AgentId 类

#### 连接管理
| 方法 | 描述 |
|------|------|
| `initialize()` | 初始化 |
| `add_message_handler(handler)` | 设置消息处理器 |
| `online()` | 上线 |

#### 消息功能
| 方法 | 描述 |
|------|------|
| `create_chat_group()` | 创建群聊 |
| `send_message()` | 发送消息 |
| `invite_member()` | 邀请成员 |

## 高级用法

### 消息处理

```python
# 消息处理器追加方式
async def async_handler(msg):
    print(f"异步处理消息: {msg}")
agent.add_message_handler(async_handler)

# 装饰器方式
@agentid.message_handler()
async def message_handler(message_data):
    print('received a message:{message_data}")
```

### 群组通信

```python
# 创建群组
group_id = agent.create_chat_group(
    name="开发组",
    subject="项目讨论",
    to_aid_list=["member1@id.au", "member2@id.au"]
)

# 发送群消息
agent.send_message(
    to_aid_list=["member1@id.au"],
    session_id=group_id,
    message={"type": "text", "content": "你好！"}
)
```

### 进程阻塞和信号注册
```python

# 注册信号(exit_hook)
def exit_hook(signum, frame):
    pass
agent.register_signal_handler(exit_hook_func=exit_hook):

# 阻塞运行
agent.serve_forever()
```

## 开发指南

### 运行测试

```bash
python -m pytest tests/
```

### 构建发布

```bash
python setup.py sdist bdist_wheel
twine upload dist/*
```

## 许可证

MIT © 2023

---

📮 问题反馈: your.email@example.com
```

这个版本包含了：
1. 更清晰的功能特性描述
2. 完整的API文档表格
3. 实际使用示例
4. 开发指南
5. 美观的排版和emoji

需要我调整或补充任何部分吗?
TLScouting 0.4.0
Scouting Database for TandemLaunch's Active Recruitment
Requires Python: >=3.12

# Server + Client Setup Instructions

## Server Machine Setup

1. Ensure Python 3.12+ and `uv` are installed.
2. Navigate to your project folder.
3. Ensure your database is configured at `~/scouting_data/app.db`.
4. Start the server with:

```bash
uv run uvicorn server.main:app --host 0.0.0.0 --port 8000 --reload
```

5. Make sure port 8000 is open on the server firewall.
6. Server now listens on the network (e.g., 192.168.1.123:8000).

## Client Machine Setup

1. Install Python 3.12+ and uv.
2. Copy the project folder (only need the client part).
3. Edit settings.toml:
```bash
[server]
host = "192.168.1.123"
port = 8000
```
(Change host to match server IP.)
4. Start the client:
```bash
PYTHONPATH=. uv run -m client.main
```
5. Client GUI will show connection status.
protowhat 2.3.1
Prototype package for submission correctness testing

# protowhat

[![PyPI version](https://badge.fury.io/py/protowhat.svg)](https://badge.fury.io/py/protowhat)
[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fdatacamp%2Fprotowhat.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2Fdatacamp%2Fprotowhat?ref=badge_shield)

`protowhat` is a utility package required by `shellwhat`, `sqlwhat` and `pythonwhat` packages, used for writing Submission Correctness Tests SCTs for interactive Shell, SQL and Python exercises on DataCamp. It contains shared functionality related to SCT syntax, selectors and state manipulation.

- If you are new to teaching on DataCamp, check out https://instructor-support.datacamp.com.
- If you want to learn what SCTs are and how they work, visit [this article](https://instructor-support.datacamp.com/courses/course-development/submission-correctness-tests) specifically.
- For a deep dive in `protowhat`, consult https://protowhat.readthedocs.io.

## Installation

```
pyenv local 3.12.7
pip3.12 install protowhat   # install from pypi
make install            # install from source
```

## Testing

```
pip3.12 install -r requirements-test.txt
pip3.12 install -e .
pytest
```

## License

[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fdatacamp%2Fprotowhat.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fdatacamp%2Fprotowhat?ref=badge_large)
hcw-py-sdk 0.5.0
SDK Package for communication with HCW@Home API
Requires Python: >=3.8

# HCW Py SDK

Healthcare worker @Home SDK, the Open Source Telehealth. This package provide SDK for more easy integration with HCW, especially for form builder.

* Website: [https://hcw-at-home.com](https://hcw-at-home.com)
* SDK documentation: [https://hcw-home.github.io/hcw-docs/python/](https://hcw-home.github.io/hcw-docs/python/)

# Build and publish this package

```
python -m build
twine upload -r pypi dist/*
```

# This add test on HCW as well

simply configure your .env and run with

```
pytest test.py
```