merve.py

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
import argparse

# Parser 
parser = argparse.ArgumentParser()

parser.add_argument("--path","-p")
parser.add_argument("--target","-t")

parsed = parser.parse_args()

# Initialize Variables
path = parsed.path
target_name = parsed.target

# Scikit Objects
scaler = StandardScaler()
le = LabelEncoder()

def read_dataset(path):
    return pd.read_csv(path)

def inspect_columns(df):
    columns = list(df.columns)
    columns.remove(target_name)

    columns_to_encode = []
    columns_to_drop = []

    for column in columns:
        if df[column].nunique() == 1:
            columns_to_drop.append(column)

        elif (df[column].nunique() <= 5) and (df[column].nunique() >= 1):
            columns_to_encode.append(column)

        else:
            df[column] = le.fit_transform(df[column])
    
    print("columns_to_encode: ", columns_to_encode) 
    print("columns_to_drop: ", columns_to_drop)

    df.drop(labels=columns_to_drop, axis=1, inplace=True)
    df = pd.get_dummies(df, columns=columns_to_encode, prefix_sep="__", drop_first=True)

    return df
    
def do_scale(df):

    columns = list(df.columns)
    columns.remove(target_name)

    for column in columns:
        df[column] = scaler.fit_transform(df[[column]])

    return df

def save_df(df):
    df.to_csv("output.csv")
    print("Saved!")

def main():
    df = save_df(do_scale(inspect_columns(read_dataset(path))))

if __name__ == "__main__":
    main()