semvec/pypi-packages huggingface dataset:
pocketkit 0.1.2
A small utility toolbox with optional modules
License: MIT
Requires Python: >=3.13

# pocketkit

`pocketkit` 是一个轻量级的 Python 工具包，用于沉淀和复用项目中常用的工具函数、装饰器以及可选的基础设施封装。  
设计目标是 **模块化、可选依赖、长期可维护**。

---

## 特性

- 📦 **工具集合**：将常用代码集中管理，避免项目间重复复制
- ⏱ **计时装饰器**：支持同步 / 异步函数执行时间统计
- 🔌 **可选模块设计**：如 `nats`，按需安装，不影响核心功能
- 🧩 **标准 logging**：基于 Python 标准库，避免日志库冲突

---

## 安装

### 基础安装（不包含可选模块）

```bash
uv add pocketkit
```
## 安装 NATS 模块（可选）

```
uv add pocketkit[nats]
```
nats 模块依赖官方客户端 nats-py，通过 extras 自动安装。

## 计时装饰器

### 同步函数
```python
from pocketkit.decorators.timing import get_time_sync

@get_time_sync
def compute(a: int, b: int) -> int:
    return a + b

compute(1, 2)

```

### 异步函数
```python
import asyncio
from pocketkit.decorators.timing import get_time_async

@get_time_async
async def async_compute(a: int, b: int) -> int:
    await asyncio.sleep(1)
    return a + b

asyncio.run(async_compute(1, 2))
```
## Nats
```python
import asyncio
from pocketkit.nats import NatsConnection, EventPublisher

async def main():
    conn = await NatsConnection.create("nats://127.0.0.1:4222")

    publisher = EventPublisher(conn)
    await publisher.publish("demo.subject", b"hello world")

    await conn.close()

asyncio.run(main())

```
## 项目结构
```
pocketkit/
├── src/
│   └── pocketkit/
│       ├── __init__.py
│       ├── logger.py
│       ├── decorators/
│       │   ├── __init__.py
│       │   └── timing.py
│       └── nats/
│           ├── __init__.py
│           └── client.py
```
utsunomiya-mcp-server 0.1.0
MCP Server for Utsunomiya Open Data
License: MIT
Requires Python: >=3.10

# Utsunomiya Open Data MCP Server

This is an MCP (Model Context Protocol) server that provides access to Utsunomiya city's open data through natural language queries.

## Features

- List available datasets from Utsunomiya city
- Get schema information for specific datasets
- Query dataset contents with filtering
- Statistical analysis of dataset values
- Caching for improved performance

## Setup

1. Install dependencies: `pip install -e .`
2. Run the server: `python -m utsunomiya_mcp`

## Usage

The server provides several tools for interacting with Utsunomiya's open data:

- `list_datasets`: Get available datasets
- `get_dataset_schema`: Get schema information for a dataset
- `query_dataset`: Query dataset contents
- `analyze_dataset`: Get statistical analysis of a dataset
kapanadze-mathtools 0.1.0
მარტივი მათემატიკური ხელსაწყოები Python-ისთვის
License: MIT
Requires Python: >=3.7

# Kapanadze MathTools

მარტივი მათემატიკური ხელსაწყოების ბიბლიოთეკა Python-ისთვის.

## დაყენება

```bash
pip install kapanadze-mathtools
```

## გამოყენება

```python
from kapanadze_mathtools import statistics, geometry

# სტატისტიკური გამოთვლები
numbers = [1, 2, 3, 4, 5]
print(statistics.mean(numbers))  # საშუალო
print(statistics.median(numbers))  # მედიანა

# გეომეტრიული გამოთვლები
print(geometry.circle_area(5))  # წრის ფართობი
print(geometry.rectangle_area(4, 6))  # მართკუთხედის ფართობი
```

## მოდულები

- `statistics` - სტატისტიკური გამოთვლები
- `geometry` - გეომეტრიული გამოთვლები
tabulate-html 0.1.1
A robust HTML table parser for Python with full rowspan/colspan and multi-header support
License: MIT
Requires Python: >=3.8
Keywords: colspan, crawler, data-extraction, html, lxml, parser, rowspan, table, web-scraping

# tabulate-html

A robust, professional-grade HTML table parser for Python, designed specifically for complex real-world web tables. It fully supports **rowspan**, **colspan**, multi-row hierarchical headers, merged cells, and concatenation of fragmented HTML snippets.

While many lightweight table parsers fail on merged cells, **tabulate-html** uses a precise 2D matrix mapping to handle them perfectly — making it an ideal choice for web scraping, data extraction, and structured crawling.

[![PyPI version](https://badge.fury.io/py/tabulate-html.svg)](https://badge.fury.io/py/tabulate-html)
[![Python versions](https://img.shields.io/pypi/pyversions/tabulate-html.svg)](https://pypi.org/project/tabulate-html/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Tests](https://img.shields.io/badge/tests-11_passed-brightgreen)](https://github.com/yourusername/tabulate-html)
[![Coverage](https://img.shields.io/badge/coverage-100%25-brightgreen)](https://github.com/yourusername/tabulate-html)

[中文文档](./README.zh-CN.md)

## Features

- Accurate handling of arbitrary `rowspan` and `colspan` (outperforms pandas.read_html and most other libraries on complex tables)
- Support for multi-row hierarchical headers (automatically merged as `Level1_Level2_Level3`)
- Concatenation of multiple table fragments (perfect for paginated or dynamically loaded tables)
- Configurable extraction strategies:
  - `TEXT_AND_LINKS`: plain text + appended links/images
  - `RAW_HTML`: preserve original cell HTML
  - `STRIP_TAGS`: pure text only
- Comprehensive logging and production-ready error handling
- Full type hints and clean, maintainable code

## Installation

```bash
pip install tabulate-html

## Quick Start

```python
from tabulate_html import parse_html_tables

html = """
<table>
  <tr><th>Name</th><th colspan="2">Contact</th></tr>
  <tr><th>Age</th><th>Phone</th><th>Email</th></tr>
  <tr><td rowspan="2">John Doe</td><td>30</td><td>138...</td><td>john@example.com</td></tr>
  <tr><td>31</td><td>139...</td><td>john.new@example.com</td></tr>
</table>
"""

data = parse_html_tables(html, header_rows=[0, 1])
print(data)
```

##### Output:

```json
[
  {
    "Name_Age": "John Doe",
    "Contact_Phone": "138...",
    "Contact_Email": "john@example.com"
  },
  {
    "Name_Age": "John Doe",
    "Contact_Phone": "139...",
    "Contact_Email": "john.new@example.com"
  }
]
```

## Advanced Usage

```python
from tabulate_html import TableParser, ParseStrategy

parser = TableParser(
    strategy=ParseStrategy.TEXT_AND_LINKS,  # Extract text and append links/images
    key_separator="-",                      # Use hyphen for header keys
    tag_separator=" "                       # Join multi-text segments with space
)

result = parser.parse(html_snippets, header_rows=[0, 1, 2])  # Support 3-level headers
json_output = parser.to_json(result)
```