semvec/pypi-packages huggingface dataset:
statannotcolor 0.1.6
add color to statannot
Requires Python: >=3.5

# Disclaimer

This repository is based on webermarcolivier/statannot (commit 1835078 of Feb 21, 2020, tagged "v0.2.3"). Another version available can be found in https://github.com/trevismd/statannotations

Additions/modifications since that version are below represented in bold (previous fixes are not listed).

## What is it

Python package to optionnally compute statistical test and add statistical annotations on an existing boxplot/barplot generated by seaborn. We constantly add new features including customized colors, locations, etc.

## Features

- Single function to add statistical annotations on an existing boxplot/barplot generated by seaborn boxplot.
- Integrated statistical tests (binding to `scipy.stats` methods):
    - Mann-Whitney
    - t-test (independent and paired)
    - Welch's t-test
    - Levene test
    - Wilcoxon test
    - Kruskal-Wallis test
- Smart layout of multiple annotations with correct y offsets.
- Annotations can be located inside or outside the plot.
- Format of the statistical test annotation can be customized: star annotation, simplified p-value, or explicit p-value.
- Optionally, custom p-values can be given as input. In this case, no statistical test is performed.

## Installation

The latest stable release can be installed from PyPI:

```python
pip install statannotcolor
```
You may instead want to use the development version from Github:

```python
pip install ttps://github.com/XianchengLI/statannotcolor.git
```

## Documentation

See example jupyter notebook `example/example.ipynb`.

## Usage

Here is a minimal example:





More examples are available in the jupyter notebook `example/example.ipynb`.


## Examples

![Example 1](/example/example_non-hue_outside.png)

![Example 2](/example/example_hue_layout.png)

## Requirements

+ Python >= 3.5
+ numpy >= 1.12.1
+ seaborn >= 0.8.1
+ matplotlib >= 2.2.2
+ pandas >= 0.23.0
+ scipy >= 1.1.0

## Cite

A Digital Object Identifiers (DOIs) was created for the repository for easier referencing in the academic literature.
pymarlin 0.3.6
Lightweight Deeplearning Library
Requires Python: >=3.6

# PyMarlin, a lightweight PyTorch library for agile deep learning!
[![Unit Tests](https://github.com/microsoft/PyMarlin/actions/workflows/test.yml/badge.svg)](https://github.com/microsoft/PyMarlin/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/microsoft/PyMarlin/branch/main/graph/badge.svg?token=wUF3ZODLpN)](https://codecov.io/gh/microsoft/PyMarlin)
[![Docs](https://github.com/microsoft/PyMarlin/actions/workflows/deploy-website.yml/badge.svg)](https://microsoft.github.io/PyMarlin/)
[![AzureML Canary](https://github.com/microsoft/PyMarlin/actions/workflows/canary.yml/badge.svg)](https://github.com/microsoft/PyMarlin/actions/workflows/canary.yml)
[![pypi](https://img.shields.io/pypi/v/pymarlin)](https://pypi.org/project/pymarlin/)

PyMarlin was developed with the goal of simplifying the E2E Deep Learning experimentation lifecycle for data scientists using PyTorch. The library enables an agile way to quickly prototype a new AI scenario on dev box and seamlessly scale it training multi-node DDP GPU training with AzureML or other cloud services.

## Key features
- Provides public and enterprise **data pre-processing** recipes, which provides out of the box vanilla and parallel processing. It requires no additional code to run for AzureML or other environments easily.
- Provides **scalable model training** with support for Single Process, VM, multi-GPU, multi-node, distributed Data Parallel, mixed-precision (AMP, Apex) training. ORT and DeepSpeed based training are going to be available soon!
- Provides out of the box **Plugins** that can be used for all typical NLP tasks like Sequence Classification, Named Entity Recognition and Seq2Seq text generation.
- Provides **reusable modules** for model checkpointing, stats collection, Tensorboard and compliant AML logging which can be customized based on your scenario.
- Provides **custom arguments parser** that allows for saving all the default values for arguments related to a scenario in an YAML config file, merging user provided arguments at runtime.
- All core modules are thoroughly **linted**,**unit tested** and even ran E2E (multi-node, GPU) in AzureML.
- PyMarlin is minimal and has a easy to understand codebase. PyMarlin was designed to make it easy for others to understand the entire codebase and customize according to their needs.

## Installation

    pip install pymarlin

Read the [installation doc](https://microsoft.github.io/PyMarlin/docs/installation) for more information.

## Start exploring!

### Full documentation website
Full website with [guides and SDK reference](https://microsoft.github.io/PyMarlin/).

### Train your first model with pymarlin
Check out the [CIFAR image classification example](hhttps://microsoft.github.io/PyMarlin/docs/examples/cifar).

### GLUE task benchmarking
Explore how to use pymarlin to [benchmark your language models on GLUE tasks](https://microsoft.github.io/PyMarlin/docs/examples/glue-tasks).

## We want your feedback!
Reach out to us with your [feedback and suggestions](https://github.com/microsoft/PyMarlin/issues).