semvec/pypi-packages huggingface dataset:
lillib 0.2.0
A small collection of utility functions
Requires Python: >=3.6
Keywords: utilities, formatting, human-readable

# lillib

A small collection of utility functions for Python.

## Installation

You can install the package via pip:

```bash
pip install lillib
```

## Usage

### Human-readable byte sizes

Convert byte sizes into human-readable strings:

```python
from lillib import humanbytes

# Basic usage (uses binary 1024-based units with IEC prefixes by default)
print(humanbytes(1024))  # "1.00 KiB"
print(humanbytes(1500000))  # "1.43 MiB"

# Using decimal (1000-based) units
print(humanbytes(1000, decimal=True))  # "1.00 KB"

# Control decimal places
print(humanbytes(1024, sigfig=0))  # "1 KiB"
print(humanbytes(1024, sigfig=3))  # "1.000 KiB"

# Control IEC strictness for binary units
print(humanbytes(1024, strict_iec=False))  # "1.00 KB" (non-strict)
```

## Features

- `humanbytes()`: Convert byte sizes into human-readable strings with appropriate unit prefixes
  - Support for both decimal (1000-based) and binary (1024-based) units
  - Support for IEC standard binary prefixes (KiB, MiB, GiB) or traditional prefixes

## License

This project is licensed under the GNU Affero General Public License v3 - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
anbiopy 0.1.3
Python package designed for the analysis of microbiota data when incorporating other types of variables e.g., anthropometric
Requires Python: >=3.8

# Amapy Library

Author: Juan Sebastián Pardo
License: MIT License

## Introduction
anbiopy is a Python library designed for microbiota data analysis. It is specifically intented to use when users have the following data:
- OTU observations
- OTU taxonomy
- OTU Tree
- Any kind of anthropometric or additional data

Based on these, anbiopy is able to assist users in analyzing microbiota data and possible relationships with anthropometric variables e.g., alpha diversity vs height/weight/sex/etc. 


## Library Components
anbiopy consists of 3 categories of analysis: 
1. Basic microbiota analysis
2. Graphic analysis
3. Numeric analysis

In the following segments we will go through the functionalities of each component using examples. 

## Installation
Using `pip`
```bash
pip install anbiopy
```

## Dependencies
This library is built using:
- pandas
- numpy
- matplotlib
- seaborn
- scipy
- scikit-learn
- statsmodels
- scikit-bio

All these dependencies should be installed along with anbiopy