semvec/pypi-packages huggingface dataset:
AproximacaoRaizes 0.0.1
Conjunto de ferramentas para implementação de métodos elementares de análise numérica
Requires Python: >=3.9

# Métodos Numéricos Elementares

Implementação dos métodos elementares de análise numérica em python.
## Ferramentas disponíveis:

v0.0.1:
- Biseção
- Posição falsa
- Ponto fixo
- Newton Raphson
- Secante

# Referência:
[Cálculo numérico: Aspectos teóricos e computacionais. 2ª Ed. Livro por: Marcia Ruggiero e Vera Lúcia Lopes](https://www.google.com/search?q=C%C3%A1lculo+Num%C3%A9rico:+Aspectos+Te%C3%B3ricos+e+Computacionais&sxsrf=APwXEdfgqS5mIzsXo3Rk-RsZe0XDspg7lA:1685770539697&source=lnms&sa=X&ved=2ahUKEwik18PRsKb_AhWsjZUCHSdsAf4Q_AUoAHoECAoQAg&biw=1292&bih=668&dpr=1)
elementares-metodosnumericos 0.0.1
Conjunto de ferramentas para implementação de métodos elementares de análise numérica
Requires Python: >=3.9

# Métodos Numéricos Elementares

Implementação dos métodos elementares de análise numérica em python.
## Ferramentas disponíveis:

v0.0.3:
- Biseção
- Posição falsa
- Ponto fixo
- Newton Raphson
- Secante

# Referência:
[Cálculo numérico: Aspectos teóricos e computacionais. 2ª Ed. Livro por: Marcia Ruggiero e Vera Lúcia Lopes](https://www.google.com/search?q=C%C3%A1lculo+Num%C3%A9rico:+Aspectos+Te%C3%B3ricos+e+Computacionais&sxsrf=APwXEdfgqS5mIzsXo3Rk-RsZe0XDspg7lA:1685770539697&source=lnms&sa=X&ved=2ahUKEwik18PRsKb_AhWsjZUCHSdsAf4Q_AUoAHoECAoQAg&biw=1292&bih=668&dpr=1)
Metodos-Numericos 0.0.1
Conjunto de ferramentas para implementação de métodos elementares de análise numérica
Requires Python: ==3.9

# Métodos Numéricos Elementares

Implementação dos métodos elementares de análise numérica em python.
## Ferramentas disponíveis:

v0.0.1:
- Biseção
- Posição falsa
- Ponto fixo
- Newton Raphson
- Secante

# Referência:
[Cálculo numérico: Aspectos teóricos e computacionais. 2ª Ed. Livro por: Marcia Ruggiero e Vera Lúcia Lopes](https://www.google.com/search?q=C%C3%A1lculo+Num%C3%A9rico:+Aspectos+Te%C3%B3ricos+e+Computacionais&sxsrf=APwXEdfgqS5mIzsXo3Rk-RsZe0XDspg7lA:1685770539697&source=lnms&sa=X&ved=2ahUKEwik18PRsKb_AhWsjZUCHSdsAf4Q_AUoAHoECAoQAg&biw=1292&bih=668&dpr=1)
mad-gui 1.0.2
Python GUI for annotating and processing time series data.
Requires Python: >=3.8,<3.10

# MaD GUI
**M**achine Learning 
**a**nd 
**D**ata Analytics 
**G**raphical 
**U**ser 
**I**nterface

[![Test and Lint](https://github.com/mad-lab-fau/mad-gui/workflows/Test%20and%20Lint/badge.svg)](https://github.com/mad-lab-fau/mad-gui/actions/workflows/test_and_lint.yml)
[![Documentation Status](https://readthedocs.org/projects/mad-gui/badge/?version=latest)](https://mad-gui.readthedocs.io/en/latest/?badge=latest)


[![PyPI version shields.io](https://img.shields.io/pypi/v/mad-gui)](https://pypi.org/project/mad-gui/)
![PyPI - Downloads](https://img.shields.io/pypi/dm/mad-gui)

![GitHub all releases](https://img.shields.io/github/downloads/mad-lab-fau/mad-gui/total?style=social)

## What is it?
The MaD GUI is a framework for processing time series data. Its use-cases include visualization, annotation (manual or automated), and algorithmic processing of visualized data and annotations. More information:

 - [Documentation](https://mad-gui.readthedocs.io/en/latest/README.html) 
 - [Github Repository](https://github.com/mad-lab-fau/mad-gui)
 - [YouTube Playlist](https://www.youtube.com/watch?v=cSFFSTUM4e0&list=PLf4GpKhBjGcswKIkNeahNt5nDxt8oXPue)

## Using our example

In a python 3.8 environment, execute the following commands or use the section [Development installation](https://github.com/mad-lab-fau/mad-gui#development-installation):
```
pip install mad_gui
mad-gui
```

This is just to get a first feeling of how the GUI looks like, you can test with our example data:
You can [download our example data](https://github.com/mad-lab-fau/mad-gui#example-data) to
test our built-in exemplary importer, exemplary algorithms and exemplary label. 
To see how to open our example data within the GUI, please refer to our section about the 
[User Interface](https://github.com/mad-lab-fau/mad-gui#user-interface).

## Using your data / algorithms

Very short, you will create and inject plugins, similar to this:

```
from mad_gui import start_gui
from my_plugin_package imoprt MyAlgorithm

start_gui(plugins=MyAlgorithm)
```

For more information on how to create your plugins, refer to [our readme](https://github.com/mad-lab-fau/mad-gui#developing-plugins) or our [more extensive documentation](https://mad-gui.readthedocs.io/en/latest/plugin_importer.html).
DedupliPy 0.8
End-to-end deduplication solution
Requires Python: >=3.6.9

<!--- BADGES: START --->
[![Version](https://img.shields.io/pypi/v/deduplipy)](https://pypi.org/project/deduplipy/)
![](https://img.shields.io/github/license/fritshermans/deduplipy)
[![Downloads](https://pepy.tech/badge/deduplipy)](https://pepy.tech/project/deduplipy)
[![Conda - Platform](https://img.shields.io/conda/pn/conda-forge/deduplipy?logo=anaconda&style=flat)][#conda-forge-package]
[![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/deduplipy?logo=anaconda&style=flat&color=orange)][#conda-forge-package]
[![Conda Recipe](https://img.shields.io/static/v1?logo=conda-forge&style=flat&color=green&label=recipe&message=deduplipy)][#conda-forge-feedstock]
[![Docs - GitHub.io](https://img.shields.io/static/v1?logo=readthdocs&style=flat&color=pink&label=docs&message=deduplipy)][#docs-package]

[#pypi-package]: https://pypi.org/project/deduplipy/
[#conda-forge-package]: https://anaconda.org/conda-forge/deduplipy
[#conda-forge-feedstock]: https://github.com/conda-forge/deduplipy-feedstock
[#docs-package]: https://deduplipy.readthedocs.io/en/latest/
<!--- BADGES: END --->

# DedupliPy

<a href="https://deduplipy.readthedocs.io/en/latest/"><img src="https://deduplipy.readthedocs.io/en/latest/_images/logo.png" width="15%" height="15%" align="left" /></a>

Deduplication is the task to combine different representations of the same real world entity. This package implements
deduplication using active learning. Active learning allows for rapid training without having to provide a large,
manually labelled dataset.

DedupliPy is an end-to-end solution with advantages over existing solutions:

- active learning; no large manually labelled dataset required
- during active learning, the user gets notified when the model converged and training may be finished
- works out of the box, advanced users can choose settings as desired (custom blocking rules, custom metrics,
  interaction features)

Developed by [Frits Hermans](https://www.linkedin.com/in/frits-hermans-data-scientist/)

## Documentation

Documentation can be found [here](https://deduplipy.readthedocs.io/en/latest/)

## Installation

### Normal installation

**With pip**

Install directly from PyPI.

```
pip install deduplipy
```

**With conda**

Install using conda from conda-forge channel.

```
conda install -c conda-forge deduplipy
```


### Install to contribute

Clone this Github repo and install in editable mode:

```
python -m pip install -e ".[dev]"
python setup.py develop
```

## Usage

Apply deduplication your Pandas dataframe `df` as follows:

```python
myDedupliPy = Deduplicator(col_names=['name', 'address'])
myDedupliPy.fit(df)
```

This will start the interactive learning session in which you provide input on whether a pair is a match (y) or not (n).
During active learning you will get the message that training may be finished once algorithm training has converged.
Predictions on (new) data are obtained as follows:

```python
result = myDedupliPy.predict(df)
```