toolkit.py

import pandas as pd
import numpy as np


class DataParser:
    @staticmethod
    def open_file(file_path, index_col="Index"):
        '''
        Opens a file and returns it

        Parameters:
        file_path (str): A string path to the file
        index_col (str, optional): Index column (default is Index)
        Returns:
        data (pd.Series): Dataset of the file
        '''
        try:
            data = pd.read_csv(file_path, index_col=index_col)
            return data
        except Exception as e:
            print(e)
            exit(1)


    @staticmethod
    def replace_nan_values(data):
        '''
        Replaces NaN values of a Dataset

        Parameters:
        data (pd.Series): A dataset with NaN values
        Returns:
        data (pd.Series): A dataset without NaN values
        '''
        for col in data.columns:
            if data[col].isnull().any():
                non_nan_values = data[col][data[col].notnull()]
                if not(non_nan_values.empty):
                    column_mean = sum(non_nan_values) / len(non_nan_values)
                    data[col] = data[col].fillna(column_mean)
        return data

    @staticmethod
    def clean_data(data):
        '''
        Removes non-numeric data and replace NaN values

        Parameters:
        data (pd.Series): A dataset
        Returns:
        num_data (pd.Series): A dataset without non-numeric data and NaN values
        nan_data (pd.Series): A dataset without non-numeric data
        '''
        try:
            columns_name = []
            for name in data.columns:
                if isinstance(data[name].iloc[0], int) or isinstance(data[name].iloc[0], float):
                    columns_name.append(name)
            num_data = data.loc[:, columns_name]
            nan_data = num_data.copy()
            num_data = DataParser.replace_nan_values(num_data)
        except Exception as e:
            print(e)
            exit(1)
        return num_data, nan_data
    
    @staticmethod
    def sigmoid(x):
        return 1 / (1 + np.exp(-x))