semvec/pypi-packages huggingface dataset:
lib-nfl 1.0.0
NFL is a number formatting library.
Requires Python: >=3.10

# Number Format Lib

NFL or Number Format Lib is a python module that is used to format numbers.

#### example
```python
40000 = '40.0k'
```

If you want to use the documentation go to the ```docs.md``` file.

#### setup

To install the module run this command in you're terminal:
```shell
pip install python-nfl 
```
phicloudmask 0.0.2
Universal Cloud Mask Detector for Optical Remote Sensing Imagery
Requires Python: <4.0,>=3.9

# phicloudmask

[![Release](https://img.shields.io/github/v/release/csaybar/phicloudmask)](https://img.shields.io/github/v/release/csaybar/phicloudmask)
[![Build status](https://img.shields.io/github/actions/workflow/status/csaybar/phicloudmask/main.yml?branch=main)](https://github.com/csaybar/phicloudmask/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/csaybar/phicloudmask/branch/main/graph/badge.svg)](https://codecov.io/gh/csaybar/phicloudmask)
[![Commit activity](https://img.shields.io/github/commit-activity/m/csaybar/phicloudmask)](https://img.shields.io/github/commit-activity/m/csaybar/phicloudmask)
[![License](https://img.shields.io/github/license/csaybar/phicloudmask)](https://img.shields.io/github/license/csaybar/phicloudmask)

Sensor Independent Cloud and Shadow Masking with Ambiguous Labels and Multimodal Inputs

- **Github repository**: <https://github.com/csaybar/phicloudmask/>
- **Documentation** <https://csaybar.github.io/phicloudmask/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:csaybar/phicloudmask.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPi or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [Pypi](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/csaybar/phicloudmask/settings/secrets/actions/new).
- Create a [new release](https://github.com/csaybar/phicloudmask/releases/new) on Github.
- Create a new tag in the form `*.*.*`.

For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
ShapeClimPy 0.0.1
ShapeClimPy is a library designed to simplify and automate the basic steps of processing and analyzing meteorological data
Requires Python: >=3.8

# ShapeClimPy - библиотека для обработки климатических данных

ShapeClimPy - это библиотека, разработанная для упрощения и автоматизации основных этапов обработки и анализа метеорологических данных. Эта библиотека предоставляет набор инструментов для парсинга, очистки и восстановления данных с портала "Погода и климат", а также набор функций, упрощающих работу с растровыми данными (таких, как NetCDF и GeoTIFF).

## Возможности

1. **Парсинг и очистка данных**:
   - Парсинг данных с портала "Погода и климат". На выходе CSV-файл, содержащий метеорологические данные для указанных метеостанций
   - Автоматизированная очистка с возможностью указания специфических параметров
   - Восстановление данных, обработка пропущенных значений и выбросов

2. **Управление растровыми данными**:
   - Конкатенация нескольких наборов растровых данных (NetCDF, GeoTIFF, Hgt)
   - Ресэмплинг и агрегирование растровых данных
   - Расчет зональной статистики для пользовательских регионов
   - Сравнение прогнозных данных различных моделей сценариев SSP для заданных территорий

3. **Простота использования**:
   - Интуитивно понятный и удобный API
   - Подробная документация и примеры
   - Поддержка Python 3.8+ и Jupyter Notebook

## Установка

Вы можете установить Библиотеку для обработки метеорологических данных, используя pip:
edexplore 1.0.1
A simple widget for interactive EDA / QA for those who use Pandas in Jupyter Notebook.

# EDExplore
A simple widget for interactive EDA / QA for those who use Pandas in Jupyter Notebook.    
    
<img src="https://github.com/nagaprakashv/edexplore/assets/13671867/9ddef93e-433f-40f1-b629-886b8b00a333" width=40% height=40%>     

***
Requirements: Pandas, Jupyter Notebook, and IPyWidgets.

## How to install?
Installing IPyWidgets:     
`pip install ipywidgets`   
or      
`conda install -c conda-forge ipywidgets`   

At times, you may need to enable the ipywidgets notebook extension:    
`jupyter nbextension enable --py widgetsnbextension`

After downloading / extracting the code, go to the directory (in command line) for installation:        
`pip install .`

## How to use?
`import pandas as pd`    
`from edexplore import interact`    
`df = pd.read_csv("xzy.csv")`     
`interact(df)`    
    
<img src="https://github.com/nagaprakashv/explore/assets/13671867/7cd53826-2cf0-41f4-a335-d59bf4fd0af2"" width=100% height=100%>     
     

***
Special thanks to Ashwin Rajeev, who introduced IPyWidgets to me long back!
***
Copyright (c) 2024 Nagaprakash Venkatesan , 
[FreeBSD License](https://opensource.org/license/BSD-2-Clause)