Disclaimer: Easyutils is a lightweight Python module designed for non-profit and personal use. It leverages scikit-learn for streamlined feature engineering. All rights to scikit-learn and other libraries used belong to their respective owners.

Important info: demonstration.ipynb is a notebook file which you can view. It shows how this module can be used on a structured dataset with the most efficiency.

Methods:

• Dataframe: The pandas DataFrame to be inspected.
• Functionality: Provides key insights into the DataFrame, including NaN values, duplicate rows, constant columns, and those with high missing values.

• Dataframe: The pandas DataFrame for column categorization.
• Functionality: Categorizes columns into numeric, object, boolean, and miscellaneous types, returning lists of column names for each category.

• Dataframe: The pandas DataFrame for column information.
• Exclude: List of columns to be excluded (optional).
• Functionality: Generates detailed information for each column based on its storage data type, excluding specified columns if provided.

• Dataframe: The pandas DataFrame for label extraction.
• Columns_to_extract: List of columns to be extracted as labels.
• Functionality: Extracts specified columns, converts them to numpy arrays, and returns them. Also drops these columns from the dataframe.

Methods:

• Data_array: The data array to be preprocessed.
• Method: The preprocessing method to be applied. Choose from "OneHotEncoder," "StandardScaler," "RobustScaler," or "MinMaxScaler."
• Save_location_pkl: The file path to save the fitted preprocessing object (pickle file).
• Functionality: Applies the specified preprocessing method to the data array, saves the fitted preprocessing object, and returns the transformed data.

• Object_location: The file path to load the fitted preprocessing object (pickle file).
• Data_array: The data array to be transformed using the loaded preprocessing object.
• Functionality: Loads the fitted preprocessing object, applies it to the data array, and returns the transformed data.

• Data_array: The data array to be transformed into binary values.
• Zero_class: The value to be mapped to 0.
• One_class: The value to be mapped to 1.
• Functionality: Maps specified classes to binary values (0 or 1) in the data array and returns the transformed data.

Methods:

• Data_array: The data array to be observed.
• Method: The decomposition method to be used, choose from "pca" or "tsvd."
• Check: Number of components to check explained variance (optional).
• Functionality: Observes the data using PCA or truncated singular value decomposition (tsvd) and visualizes cumulative explained variance. Can check explained variance with a specific number of components if specified.

• Data_array: The data array to be decomposed.
• Method: The decomposition method to be applied, choose from "pca" or "tsvd."
• Num_components: The number of components to retain during decomposition.
• Save_location_pkl: The file path to save the fitted decomposition object (pickle file).
• Functionality: Applies PCA or tsvd to the data array, saves the fitted decomposition object, and returns the transformed data.

• Object_location: The file path to load the fitted decomposition object (pickle file).
• Data_array: The data array to be transformed using the loaded decomposition object.
• Method: The decomposition method used, choose from "pca" or "tsvd."
• Functionality: Loads the fitted decomposition object and applies it to the data array, returning the transformed data.

• Object_location: The file path to load the fitted tsvd decomposition object (pickle file).
• Image_array: The flattened image array to be visualized.
• Width: The width of the original image.
• Height: The height of the original image.
• Functionality: Reconstructs and visualizes images from their truncated singular value decomposition.

Note: Easyutils is designed for structured datasets and aims to simplify common feature engineering tasks. It enhances convenience and efficiency in the preprocessing and decomposition of data.

Important: This module is intended for non-profit and personal use only. All credit for scikit-learn and other utilized libraries goes to their respective owners.