semvec/pypi-packages huggingface dataset:
geeksales 0.1.1
A PySpark utility package for sales data analysis and visualization
License: MIT
Requires Python: >=3.7

# GeekSales

A powerful PySpark utility package for sales data analysis and visualization.

## Features

- Create and configure Spark sessions with optimized settings
- Calculate sales metrics and aggregations
- Generate comprehensive sales dashboards
- Create various sales visualizations:
  - Sales by region
  - Customer order distribution
  - Sales trends over time
  - Top products by sales

## Installation

```bash
pip install geeksales
```

## Usage

```python
from geeksales import spark_utils

# Create a Spark session
spark = spark_utils.create_spark_session("Sales Analysis")

# Load your sales data
df = spark.read.csv("sales_data.csv", header=True, inferSchema=True)

# Add sales metrics
df_with_metrics = spark_utils.add_sales_metrics(df)

# Generate sales summary
summary = spark_utils.get_sales_summary(df_with_metrics)

# Create visualizations
spark_utils.plot_sales_by_region(df_with_metrics)
spark_utils.plot_customer_order_distribution(df_with_metrics)
spark_utils.plot_sales_trend(df_with_metrics)
spark_utils.plot_product_sales(df_with_metrics)

# Create a complete dashboard
dashboard = spark_utils.create_sales_dashboard(df_with_metrics)
```

## Requirements

- Python 3.7 or higher
- PySpark 3.0 or higher
- Matplotlib 3.0 or higher
- Seaborn 0.11 or higher
- Pandas 1.0 or higher

## License

This project is licensed under the MIT License - see the LICENSE file for details.
bankkonto 1.0.1
Python validation library for Swedish bank account numbers
License: MIT
Keywords: Bank, Bank account, Swedish bank account, validation

# Bankkonto

![Build and Test](https://github.com/hbldh/bankkonto/workflows/Build%20and%20Test/badge.svg)
[![Upload Python Package](https://github.com/hbldh/bankkonto/actions/workflows/python-publish.yml/badge.svg)](https://github.com/hbldh/bankkonto/actions/workflows/python-publish.yml)

A tool for cleaning, parsing and ultimately validating Swedish bank account numbers, primarily written to be used for web applications with form data.

## Installation

```bash
pip install bankkonto
```

## Documentation

Will be available on ReadTheDocs eventually.  

## Usage

Direct usage for validating a bank account number:

```python
>>> import bankkonto

>>> bankkonto.validate('9029', '5735211')
True

>>> bankkonto.validate('9029', '5735214')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/hbldh/Repos/bankkonto/account.py", line 117, in validate
    bank_account_number, bank_name, bank_account_number[-1]))
bankkonto.exceptions.BankkontoValidationError: Bank account number 5735214 for Länsförsäkringar Bank has invalid control digit: 4`
```

Clean an entered string containing both clearing number and bank account number:

```python
>>> import bankkonto
>>> cn, bn = bankkonto.clean_and_split('8156-6 111.222.333-2')
>>> print(cn)
'81566'
>>> print(bn)
'1112223332'
>>> bankkonto.validate(cn, bn)
True
```

There is also a WTForms validator at `bankkonto.flask.BankkontoValidator` that can be used with e.g. Flask-WTF. 

## Tests

Run with pytest:

```bash
pytest tests
```

Install `pytest-cov` first, and get coverage report:

```bash
pytest tests --cov bankkonto --cov-report term-missing
```
asmr-toolkit 1.0.10
A modern CLI tool for audio conversion and processing
License: MIT
Requires Python: >=3.8

# ASMR Toolkit

[![Tests](https://github.com/xiao-cold/asmr-toolkit/workflows/Tests/badge.svg)](https://github.com/xiao-cold/asmr-toolkit/actions?query=workflow%3ATests)
[![Lint](https://github.com/xiao-cold/asmr-toolkit/workflows/Lint/badge.svg)](https://github.com/xiao-cold/asmr-toolkit/actions?query=workflow%3ALint)
[![codecov](https://codecov.io/gh/xiao-cold/asmr-toolkit/graph/badge.svg?token=QWZT1Q3S0D)](https://codecov.io/gh/xiao-cold/asmr-toolkit)
![PyPI - Version](https://img.shields.io/pypi/v/asmr-toolkit)


一个现代化的命令行工具，用于音频转换和处理。

## 特性

- 高质量音频转换为 Opus 格式
- 批量处理支持
- 多线程并行转换
- 保持目录结构
- 简单直观的命令行界面

## 安装

### 依赖

- Python 3.8+（推荐 Python 3.13）
- FFmpeg

### 使用 pip 或 uv 安装

```bash
# 使用 pip
pip install asmr-toolkit

# 或使用 uv（推荐）
uv pip install asmr-toolkit
```

## 快速开始

### 转换单个文件

```bash
asmr convert music.mp3
```

这将在同一目录下创建 `music.opus.ogg` 文件。

### 批量转换

```bash
# 转换目录中的所有音频文件（输出到 music_folder/out/）
asmr convert --recursive music_folder/

# 指定输出目录
asmr convert --output-dir converted/ --recursive music_folder/

# 设置比特率
asmr convert --bitrate 192k music.mp3

# 使用多线程加速处理
asmr convert --jobs 4 --recursive music_folder/
```

## 文档

完整文档请访问 [docs/README.md](docs/README.md)。

## 贡献

欢迎贡献！请查看 [docs/CONTRIBUTING.md](CONTRIBUTING.md) 了解如何参与项目开发。本项目使用 uv 进行包管理，使用 Ruff 进行代码格式化。

## 许可证

本项目采用 MIT 许可证 - 详情请参阅 [LICENSE](LICENSE) 文件。