libreria-G5 0.1.1

This library contains different functions to facilitate data preprocessing.

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:

pip install libreria_G5

** 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:

from libreria_G5 import *

to import all functions, or

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:

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.