semvec/pypi-packages huggingface dataset:
sentimini 1.0.0
Tiny Sentiment Analyzer – rule-based & lexicon-based
Requires Python: >=3.7
Keywords: sentiment-analysis, nlp, lightweight, polarity

# sentimini – Sentiment Analyzer
Sentiment analysis package is very lightweight package for analysis sentiment.

Uses rule-based + lexicon approach for quick polarity detection.

## Installation

```bash
pip install sentimini
```

## Usage

```bash
from sentimini import polarity, score

print(polarity("I love this product!"))   # positive
print(polarity("This is terrible..."))    # negative
print(polarity("It is okay."))            # neutral

print(score("I really love this!"))
# { "polarity": "positive", "score": 0.8 }
```

## Features

✅ Tiny & fast (no ML models)

✅ Works offline

✅ Rule-based with lexicon

✅ Supports negations & intensifiers

✅ Extensible word lists
somador3000 0.1.5
Soma 3000 ao número passado como argumento
Requires Python: <4.0,>=3.13

# 🔢 Somador3000

É um pacote Python de linha de comando que soma 3000 ao número passado como argumento.

🌟 O Somador3000 está disponível no [PyPI! Clique aqui para acessar a página oficial do pacote e conferir mais detalhes, avaliações e instruções de instalação.](https://pypi.org/project/somador3000/)


## 🚀 Instalação Rápida (Usuários)

Para usuários que querem apenas usar o pacote:

```bash
pip install somador3000
```

Depois basta executar:

```bash
somador3000 42  # or python -m somador3000 42
# Output: O resultado de 42.0 + 3000 é: 3042.0
```


# 🛠 Instalação para Desenvolvedores
Para contribuir com o desenvolvimento:

1. Clone o repositório:

```bash
git clone https://github.com/GustavoRizzo/somador3000.git
cd somador3000
```

2. Instale as dependências com Poetry:

```bash
poetry install
```

3. Execute em modo desenvolvimento:

```bash
poetry run somador3000 42
# Output: O resultado de 42.0 + 3000 é: 3042.0
```

## 🌐 Rodando como Web App (FastAPI)

Se você quiser rodar a aplicação como uma API web (FastAPI), basta instalar as dependências opcionais e executar o servidor:

```bash
poetry install --with web-app
```

Depois, rode o servidor com:

```bash
task run-web-app
```

Acesse a documentação interativa (Swagger) em: http://127.0.0.1:8000/docs


# ✨ Funcionalidades

- ✅ Soma rápida de qualquer número com 3000

- 🎯 Precisão decimal

- 📦 Fácil instalação via pip


# 🤝 Contribuindo

Contribuições são bem-vindas! Siga esses passos:

1. 🍴 Faça um fork do projeto

2. 🌿 Crie uma branch para sua feature (git checkout -b feature/incrivel)

3. 💾 Commit suas mudanças (git commit -m 'Adiciona feature incrível')

4. 🚀 Faça push para a branch (git push origin feature/incrivel)

5. 🔄 Abra um Pull Request


# 📜 Licença

Este projeto está licenciado sob a licença MIT - veja o arquivo LICENSE para detalhes.


# 🙏 Agradecimentos

- À comunidade Python por ferramentas incríveis

- Aos contribuidores que ajudarem a melhorar este projeto

- A você por usar o Somador3000! ❤️
ustat-var 0.3.4
Computing unbiased variance estimators
Requires Python: >=3.9

## Overview

This package contains the non-parametric unbiased estimators of the variance of teacher effects described in [Rose, Schellenberg, and Shem-Tov (2022)](https://www.nber.org/papers/w30274). These unbiased estimators are $U$-statistics, which provide minimum-variance unbiased estimators of population parameters for arbitrary probability distributions. The $U$-statistic approach overcomes several issues experienced by Empirical Bayes (EB) techniques when estimating an agent's 'value-added'.
This readme is meant to be brief overview of how to install the package and how to use it -- the complete package documentation can be found [here](https://ustat-var.readthedocs.io/en/latest/).
### Authors

-   [Evan K. Rose](https://ekrose.github.io/) (University of Chicago)
-   [Jonathan T. Schellenberg](https://sites.google.com/view/jonathanschellenberg/) (Amazon Web Services)
-   [Yotam Shem-Tov](https://yotamshemtov.github.io/) (UCLA)
-   [Jack Mulqueeney](https://jkmulq.github.io) (University of Chicago)

## Installation

To install:

```         
python3 -m pip install ustat_var
```

## Usage

The package contains two functions that compute the non-parametric estimators presented in Appendix C of [Rose, Schellenberg, and Shem-Tov (2022)](https://www.nber.org/papers/w30274).

The first, `ustat.varcovar` estimates the variance-covariance, taking in two matrices as inputs:

``` python
import ustat_var as ustat
import numpy as np

# Seed and data
np.random.seed(18341)
X,Y = ustat.generate_test_data.generate_data(n_teachers = 10, n_time = 5, n_arrays = 2, var_fixed = 1, var_noise = 1.0, cov_factor = 0.5)

# Variance-covariance
ustat.varcovar(X, X) # Var(X)
ustat.varcovar(X, Y) # Cov(X, Y)
```

The second, `ustat_samp_covar`, estimates the sampling variance/covariance of `varcovar`. It takes four matrices as inputs, where `ustat_samp_covar(A, B, C, D)` yields $\hat{Cov}\big(\hat{Cov}(A,B) - Cov(A,B), \hat{Cov}(C,D) - Cov(C,D)\big)$. For example:

``` python
 # Base implementation through ustat_samp_covar.ustat_samp_covar() functions:
ustat.ustat_samp_covar.ustat_samp_covar(X, X, X, X) # Sampling variance of Var(X)
ustat.ustat_samp_covar.ustat_samp_covar(X, Y, X, Y) # Sampling variance of Cov(X, Y)

# Faster implementation available in ustat_samp_covar.ustat_samp_covar_fast() function
ustat.ustat_samp_covar.ustat_samp_covar_fast(X, X, X, X) # Also computes sampling variance of Var(X), but faster than above
ustat.ustat_samp_covar.ustat_samp_covar_fast(X, Y, X, Y) # Also computes sampling variance of Cov(X, Y), but faster than above
```

You can find further details about each function in the package [documentation](https://ustat-var.readthedocs.io/en/latest/).
newsdata-client 0.1.0
Newsdata Python Client
Requires Python: >=3.13
Keywords: newsdata, news, newsdata-api

# newsdata-client

Python client for [Newsdata API](https://newsdata.io/documentation)

# Installation

Install using pip:

```bash
pip install newsdata-client
```

# Usage

Import the `client` class into your project, and initialize using the `api_key`.

```python
from newsdata_client import NewsDataClient

news_client = NewsDataClient(api_key="pub_XXXXXXXXXXX")
```
# Endpoints

There are four endpoint functions you can use.
You can add keyword arguments for parameters you like.
You can read [this](https://newsdata.io/documentation#latest-news) for more detail
on the parameters available for all the endpoints

```python
# Latest news endpoint
response = news_client.latest() 

# Crypto related news endpoint
response = news_client.crypto()

# News archive endpoint, for this endpoint you will have to
# give atleast one parameter, otherwise it will raise NewsDataException
response = news_client.archive(category=["technology"])

# News sources endpoint
response = news_client.sources()
```

# License

This project in under [MIT License](https://github.com/Vikuuu/newsdata-client/blob/main/LICENSE)