semvec/pypi-packages huggingface dataset:
prompt-genetics 0.0.1
Genetic algorithms for LLMs
Requires Python: >=3.7
Keywords: llm, prompt engineering, genetic algorithms, ai

# prompt-genetics

Genetic algorithms for optimizing LLM prompts.

## Installation

```bash
pip install prompt-genetics
```

## Usage

```python
from prompt_genetics import PromptOptimizer

# Example usage will be provided here
```

## Features

- Genetic algorithm-based optimization for LLM prompts
- Support for various fitness functions
- Configurable mutation and crossover operations
- Easy integration with popular LLM APIs

## Development

### Setup

```bash
git clone https://github.com/OlegSokolov/prompt-genetics.git
cd prompt-genetics
pip install -e ".[dev]"
```

### Running Tests

```bash
pytest
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
psdec 0.1.1
A sample Python project
Requires Python: >=3.9
Keywords: psdec, setuptools, development

# psdec

一个用于设备验证的 Python 工具包，支持多平台和多 Python 版本。

## 功能特点

- 支持 Python 3.9 及以上版本
- 使用 Cython 编译，提供更好的性能和代码保护
- 支持 Linux 平台（x86_64 架构）
- 提供设备验证功能

## 安装

```bash
pip install psdec
```

## 系统要求

- Python >= 3.9
- 支持的平台：Linux (x86_64)

## 依赖项

- getmac：用于获取设备 MAC 地址
- py-cpuinfo：用于获取 CPU 信息
- loguru：提供日志功能

## 使用方法

### 验证设备

```bash
psverifier
```

## 开发

如果您想参与开发，需要安装额外的开发依赖：

```bash
pip install psdec[dev]
```

## 测试

安装测试依赖并运行测试：

```bash
pip install psdec[test]
pytest
```

## 许可证

本项目采用 MIT 许可证。详见 [LICENSE.txt](LICENSE.txt) 文件。

## 作者

- 作者：iepanda (iepanda@outlook.com)
- 维护者：PulSpread (iepanda@outlook.com)

## 版本历史

当前版本：0.0.10

## 构建说明

本项目使用 Cython 进行编译，通过 cibuildwheel 构建多版本 wheel 包。构建过程已优化：

- 使用多核心并行编译
- 禁用不必要的运行时检查以提高性能
- 支持 manylinux 标准
dataengx 0.1.0
Data engineering utilities for converting and querying data files
Requires Python: >=3.7

# DataEngX

Data engineering utilities for converting between CSV, Parquet, JSONL, and more.

## Features

- ✅ Convert Parquet to CSV
- ✅ Convert CSV to JSON (flat + nested)
- ✅ Split Parquet to multiple CSV chunks
- ✅ CSV to SQLite database
- ✅ Parquet to JSONL
- ✅ JSONL to CSV
- ✅ SQL-like querying on CSV using DuckDB
- ✅ Export DataFrame to multiple formats (CSV, Parquet, JSON)

## Installation

```bash
pip install .
```

## CLI Usage

### Convert Parquet to CSV
```bash
dataengx parquet-to-csv path/to/input.parquet output/ --filename optional_filename
```

### Convert CSV to JSON
```bash
dataengx csv-to-json path/to/input.csv path/to/output.json
```

More CLI commands coming soon...

## Python Usage

```python
from dataengx import (
    convert_parquet_to_csv,
    csv_to_json,
    csv_to_nested_json,
    split_parquet_to_csv,
    csv_to_sqlite,
    parquet_to_jsonl,
    jsonl_to_csv,
    query_csv_with_sql,
    export_dataframe
)

# Convert Parquet to CSV
convert_parquet_to_csv("data/data.parquet", "output/")

# Convert CSV to flat JSON
csv_to_json("data/data.csv", "output/data.json")

# Convert CSV to nested JSON
csv_to_nested_json("data/nested.csv", "output/nested.json")

# Split Parquet to multiple CSV files
split_parquet_to_csv("data/big.parquet", "output/splits", row_limit=5000)

# Convert CSV to SQLite database
csv_to_sqlite("data/input.csv", "output/output.sqlite")

# Convert Parquet to JSONL
parquet_to_jsonl("data/input.parquet", "output/data.jsonl")

# Convert JSONL to CSV
jsonl_to_csv("data/input.jsonl", "output/data.csv")

# Query CSV like SQL
query_csv_with_sql("SELECT * FROM 'data.csv' WHERE age > 25", csv_path="data/data.csv")

# Export a DataFrame to CSV
import pandas as pd
df = pd.DataFrame({"name": ["Alice", "Bob"], "age": [30, 25]})
export_dataframe(df, "output", "people", fmt="csv")
```