preprocessing.py

from sklearn.impute import SimpleImputer
import pandas as pd
import numpy as np
import json

def data_quality(df: pd.DataFrame):
    print("Missing values before handling:")
    print(df.isnull().sum())
    print("Duplicate rows before handling:")
    print(int(df.duplicated().sum()))
    df.drop_duplicates(inplace=True)
    print("Duplicate rows after handling:")
    print(int(df.duplicated().sum()))
    return df

def standardize_data_types(df: pd.DataFrame) -> pd.DataFrame:
    # Convert string-based dates to datetime, but ignore boolean values
    for col in df.columns:
        if df[col].dtype == 'object' and not df[col].isin([True, False]).all():  
            try:
                df[col] = pd.to_datetime(df[col], errors='coerce')  # Invalid values become NaT
            except Exception as e:
                print(f"Skipping column {col}: {e}")

    # Convert numeric strings to actual numbers
    for col in df.select_dtypes(include=['object']).columns:
        if df[col].str.replace('.', '', 1).str.isnumeric().all():
            df[col] = pd.to_numeric(df[col])

    return df


def handle_missing_data(df: pd.DataFrame) -> pd.DataFrame:
    print("Before Imputation (NA Counts):")
    print(df.isnull().sum())

    numeric_col = df.select_dtypes(include=['number']).columns
    if not numeric_col.empty:
        num_imputer = SimpleImputer(strategy='median')
        df[numeric_col] = num_imputer.fit_transform(df[numeric_col])

    categorical_col = df.select_dtypes(include=['object', 'category']).columns
    if not categorical_col.empty:
        cat_imputer = SimpleImputer(strategy='most_frequent')
        df[categorical_col] = cat_imputer.fit_transform(df[categorical_col])

    print("After Imputation (NA Counts):")
    print(df.isnull().sum())

    return df


def handle_outliers(df: pd.DataFrame) -> pd.DataFrame:
    numeric_col = df.select_dtypes(include=['number','int64', 'float64']).columns
    if not numeric_col.empty:
        for col in numeric_col:
            Q1 = df[col].quantile(0.25)
            Q3 = df[col].quantile(0.75)
            IQR = Q3 - Q1
            lower_bound = Q1 - 1.5 * IQR
            upper_bound = Q3 + 1.5 * IQR
            df[col] = df[col].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)
    return df

def generate_final_report(df: pd.DataFrame, file_path: str):
    with open(file_path, "w") as file:
        file.write("FINAL DATA PREPROCESSING REPORT\n")
        file.write("=" * 50 + "\n\n")
        file.write("Missing Values (After Preprocessing):\n")
        missing_values = df.isnull().sum()
        for col, count in missing_values.items():
            file.write(f"{col}: {count} missing values\n")
        file.write("\nDuplicate Rows (After Preprocessing):\n")
        file.write(f"Total Duplicate Rows: {df.duplicated().sum()}\n\n")
        file.write("Preprocessing Completed Successfully!\n")

def save_cleaned_data(df: pd.DataFrame, file_path: str):
    df.to_csv(file_path, index=False)
    return file_path