data_preprocess.py
def modify_data(data):
    """
    Runs all the preprocessing functions on the dataset.

    Args:
        data (DataFrame): DataFrame containing the dataset with no preprocessing
        target: The dependent variable  of the dataset

    Returns:
        DataFrame: DataFrame with all the preprocessing done
        continuous: Array-like structure that contains the names of all the continuous features
    """
    [nominal, continuous] = sep_nondummies(data) 
    if nominal:
        nominal_dummified = create_dummies(data[nominal])
        for column in nominal_dummified.columns:
            nominal_dummified.loc[nominal_dummified[column].isnull(),nominal_dummified.columns.str.startswith(column+'_')] = np.nan
        data = data.drop(nominal, axis = 1)
        data = pd.concat([data,nominal_dummified], axis =1)
    return [data, continuous]

    Runs all the preprocessing functions on the dataset.

    Args:
        data (DataFrame): DataFrame containing the dataset with no preprocessing
        target: The dependent variable  of the dataset

    Returns:
        DataFrame: DataFrame with all the preprocessing done
        continuous: Array-like structure that contains the names of all the continuous features
    
Runs all the preprocessing functions on the dataset.
Runs all the preprocessing functions on the dataset .
def modify_data(data):
    [nominal, continuous] = sep_nondummies(data) 
    if nominal:
        nominal_dummified = create_dummies(data[nominal])
        for column in nominal_dummified.columns:
            nominal_dummified.loc[nominal_dummified[column].isnull(),nominal_dummified.columns.str.startswith(column+'_')] = np.nan
        data = data.drop(nominal, axis = 1)
        data = pd.concat([data,nominal_dummified], axis =1)
    return [data, continuous]
def modify_data ( data ) : [ nominal , continuous ] = sep_nondummies ( data ) if nominal : nominal_dummified = create_dummies ( data [ nominal ] ) for column in nominal_dummified . columns : nominal_dummified . loc [ nominal_dummified [ column ] . isnull ( ) , nominal_dummified . columns . str . startswith ( column + '_' ) ] = np . nan data = data . drop ( nominal , axis = 1 ) data = pd . concat ( [ data , nominal_dummified ] , axis = 1 ) return [ data , continuous ]
Runs all the preprocessing functions on the dataset.
Runs all the preprocessing functions on the dataset .
"""
    Runs all the preprocessing functions on the dataset.

    Args:
        data (DataFrame): DataFrame containing the dataset with no preprocessing
        target: The dependent variable  of the dataset

    Returns:
        DataFrame: DataFrame with all the preprocessing done
        continuous: Array-like structure that contains the names of all the continuous features
    """
DataFrame with all the preprocessing done
continuous: Array-like structure that contains the names of all the continuous features
DataFrame with all the preprocessing done continuous : Array - like structure that contains the names of all the continuous features
DataFrame
data
DataFrame containing the dataset with no preprocessing
DataFrame containing the dataset with no preprocessing
target
The dependent variable  of the dataset
The dependent variable of the dataset