semvec/pypi-packages huggingface dataset:
adjisan-proxy-scraper 0.1.1
Scrape fast HTTP, HTTPS, SOCKS4, and SOCKS5 proxies.
Requires Python: >=3.6

# Adjidev proxy scraper

> ===================================
# Guide
Let's get started if you don't know about my project
## 1. Installation
### requirements
> 1. python 3.8 or later
> 2. python installed on you device
> 3. Code editor or notepad etc

### for windows
- install python if python is not installed [click here](https://www.python.org/ftp/python/3.13.1/python-3.13.1-amd64.exe)
- install my pypi packages
  ```powershell
  python -m pip install adjisan-proxy-scraper
  ```

### for debian based linux
- install python if not installed
  ```bash
  sudo apt install python
  ```
- install my pypi package
  ```bash
  pip install adjisan-proxy-scraper
  ```

### for arch based linux
- install python if not installed
  ```bash
  sudo pacman -S python
  ```
- install my pypi packages
  ```bash
  pip install adjisan-proxy-scraper
  ```

### for red hat based linux
- install python if not installed
  ```bash
  sudo dnf install python3
  ```
- install my pypi packages
  ```bash
  pip install adjisan-proxy-scraper
  ```

## EXAMPLE
  ```python
  from ProxyScraper import GetProxy

  if __name__ == "__main__":
    #Init
    proxy = GetProxy(type="http", timeout=5, max_workers=20)

    #get
    proxies = proxy.get()
    print(f"Fetched {len(proxies)} proxies.")

    #save
    proxy.save("http.txt")
  ```

> ===================================
secret-duck 0.0.4
MQTT listener to save data
Requires Python: >=3.9

# Secret Duck

Convert a Keepass database into [DuckDB secrets](https://duckdb.org/docs/configuration/secrets_manager.html) that may
be loaded into DuckDB using its secret manager feature.

# Installation

```bash
pip install secret-duck
```

# Usage

```bash
secret-duck --help
```

To create AWS S3 secrets and save the results to an SQL file.

```bash
secret-duck my_secrets.kdbx --type S3 --keys {\"region\": \"eu-west-2\", \"endpoint\": \"s3.amazonaws.com\"} > secrets.sql
```
PEM4Fe 1.0.2
A tool for data processing and peak detection for PEM
Requires Python: >=3.6

```
PEM4Fe/
├── src/                  # 核心代码
│   ├── config.py
│   ├── fitting.py
│   ├── peak_detection.py
│   └── output.py
├── tests/                # 测试文件
│   ├── test_config.py
│   ├── test_fitting.py
│   ├── test_peak_detection.py
│   └── test_output.py
├── data/                 # 数据目录（原始数据）
├── logs/                 # 日志文件目录
├── output/               # 程序输出目录
├── requirements.txt      # 依赖库文件
├── README.md             # 项目说明文件
└── PEM4Fe.py               # 主程序入口

```
doctor-trans 1.0.3
This package translate whole dataframe in any language without any limits.

# Doctor Trans

Doctor Trans is a powerful Python package for translating large datasets in a DataFrame format. It process multiple columns simultaneously, translating text data from one language to another while preserving data integrity. The package outputs the translated DataFrame, making it perfect for large-scale multilingual data processing in business and research.

## Features
- **Automatic Language Detection**: Automatically detects the source language if not specified.
- **Multiple Language Support**: Supports translation to and from any language available in Google Translate.
- **Error Handling and Logging**: Provides detailed logging to track translation status and handle errors effectively.

## Installation
To use Doctor Trans, ensure the following packages are installed:
```bash
pip install pandas requests openpyxl
```
If Microsoft Visual C++ 14.0 is not installed, you can download it here:
https://visualstudio.microsoft.com/visual-cpp-build-tools/

```python
import pandas as pd
from doctor_trans import trans

# Sample data
data = {
    'Name': ['Hola', 'Bonjour', 'Ciao'],
    'Description': ['Buenos días', 'Bonne journée', 'Buona giornata']
}
df = pd.DataFrame(data)

# Translate the DataFrame to English and return translated DataFrame
df = trans(df, input_lang='auto', output_lang='en')

df.to_excel("translated_file.xlsx", index=False)
```

## Parameters
- **df:** The DataFrame containing text data to translate.
- **input_lang:** (optional) The source language code. Default is 'auto' for automatic detection.
- **output_lang:** (optional) The target language code. Default is 'en' for English.


Make multilingual data processing effortless with Doctor Trans. If you find this package helpful, please refer to your developer friends! 🙂