semvec/pypi-packages huggingface dataset:
libreria-G5 0.1.3
This library contains different functions to facilitate data preprocessing.
Requires Python: >=3.6


# G5 Library
This library contains different functions to facilitate data preprocessing.

## How to Install the Library
Installing the library is very simple. In the terminal, run:

```bash
pip install libreria-G5==0.1.3
```
to make sure you install de last version
> ** Note:**  
> To use this library, keep in mind that the following libraries need to be installed: pandas, numpy, sklearn, matplotlib, seaborn

## How to Use the Library
The first step is to import the library:
```bash
from libreria_G5 import *
```
to import all functions, or
```bash
from libreria_G5 import Structure
```
to import only the model.

To use the functions, we need to call the model, and we can assign it any name we want:
```bash
modelo = Structure()
```
> ** Note:**  
> If no additional parameters are specified, the model will use its default values.
#### Library Variables
Structure( valor_limite = 65, metodo_object = 'moda', metodo_number = 'mean', quantile_min = 0.15, quantile_max = 0.75, mantener_columna = 'no')

- limit_value: Defines the maximum percentage of missing values a column can have. If it exceeds this limit, the column will be removed. It can range between 0 and 100.
- method_object: The method used for imputing missing values in object-type variables. It can be mode, forward fill (previous value), or backward fill (next value).
- method_number: The method used for imputing missing values in numerical variables. Options include mode, mean, median, maximum, minimum, previous value, or next value.
- quantile_min: Defines the minimum quantile, i.e., the lower limit for data considered too low. It can range between 0 and 1.
- quantile_max: Defines the maximum quantile, i.e., the upper limit for data considered too high. It can range between 0 and 1.
- keep_column: Determines whether, when using the encoder, the original column should be kept and a new one created or if the existing column should be replaced. It can be "yes" or "no".

### Functions in the Library
Once the model is set up, we can use its functions. The model includes various functions:

- .processing(_df_): This function takes a dataset as input and returns a cleaned dataset. It removes columns with a higher percentage of missing values than the defined limit and uses the selected imputation methods to fill in missing values in the remaining columns.
- .remove_outliers(_df, columns_): This function removes outliers. The limits are set based on quantile variables, and values outside these limits are eliminated.
- .normalize(_df_): This function normalizes the data using a MinMaxScaler.
- .string_to_number(_df_): Uses a LabelEncoder to convert object-type data into numerical data. If mantener_columna is set to "yes," a new column will be created with the original column name + '_encoded'. If set to "no," the existing column will be replaced.
- .visual_analysis_of_numerical_variables(_df_): Generates a graph showing the distribution of each numerical column and a heatmap displaying the relationships between columns.
sbcli-jm 0.0.1
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```