semvec/pypi-packages huggingface dataset:
package-estatistica 0.0.3
Engenheiro de dados e desenvolvedor de software
Requires Python: >=3.0

# Estatistica

Descricao. 
Este pacote eh usado para:
	- funcoes simples de estatistica  
	- apresentar resultados dos calculos

## Instalacao

Use gerenciador de pacote [pip](https://pip.pypa.io/en/stable/) para instalar o pacote package_estatistica

```bash
pip install package_estatistica
```

## Utilizacao

```python
from package_estatistica.funcoes import principal
```

## Autor
Alessandro Miranda Goncalves

## LicenÃ§a
[MIT](https://choosealicense.com/licenses/mit/)
markdrip 0.1.4
Simple MarkDown renderer
Requires Python: >=3.10,<4.0

# Markdrip

MarkDrip is a simple HTML generator.
Mistletoe is used for rendering.

The site is [here](https://comamoca.github.io/markdrip.github.io/) even in the rendered result.


## Install

Recommend using [pipx](https://github.com/pypa/pipx).

`pip install markdrip`

## How to use

`markdrip filename`

### Options

Run `markdrip --help`.

```
Usage: main.py [OPTIONS] TARGET

Options:
  --output TEXT  Output destination file path
  --theme TEXT   Applicable CSS file name. Does not include extension.
  --help         Show this message and exit.

```

## Custom CSS

CSS is stored under `~ / .markdrip`.
Note that when writing CSS, specify the tag name directly in the selector.

Ex.)

```
h1, h2, h3, h4, h5 {
	color: black
}
```
financial-ts 0.1.39
Python module for loading time series data.
Requires Python: >=3.6

# Python module for downloading time series data
This repository gets data from the Alpha Vantage API: https://www.alphavantage.co/.
You need an API key in order to use the utilities in this repo. 

You can retrieve price data from many stocks traded in stock markets.

## Load Data
To use this module, you will need to have your own
Alpha Vantage API key.
```
from loaders import loader

data_loader = loader.LoaderAlphaV(symbol="AAPL", interval=1)

df = data_loader.ts_intraday()

df_ext = data_loader.ts_intraday_extended(interval='5min&slice=year1month5')
```

## Preprocess
This module is focused on producing data that is ingestible by pytorch models. The results
is to return a Dataloader iteratior that can easily be used by the pytorch-framework.
The preprocess module contains code for preprocessing of time series data. 

The following steps are recommended for time series data:
1) Split in train-valid-test sets
2) Separate continous from categorical data
3) Make sequential data
4) Normalize data, where ```X(t) = x(t -1 , ..., t - n); X(t - 1) = x(t - 2), ..., x(t - n);  y(t) = x(t); y(t - 1) = x(t - 1)```, etc.
5) Transform to pytorch tensors.

There might be other options for the preprocessing that are equally well suited for your problem.

The ```class ContCatSplit``` can be used on a financial time series. It can create date-features and then we can split data in time series and date features. 


## Packaging
For details on packaging and folder structure, see the link https://packaging.python.org/en/latest/tutorials/packaging-projects/

On windows, install latest version of pip:
```
py -m pip install --ugrade pip
```
Create distribution package, make sure you have latest PyPA's build installed
```
py -m pip install --upgrade build
```
Build
```
py -m build
```
You need to have Twine installed:
```
py -m pip install --upgrade twine
```

To publish on PyPi do the following:
```
twine upload dist/*
```
Install your newly created packege with:
```
python3 -m pip install [your-package]
```


For publishing in Test PyPI do the following:
```
py -m pip install --index-url https://test.pypi.org/simple/ --no-deps example-package-YOUR-USERNAME-HERE
```