How to Handle Missing Values in a CSV Numeric Column in Python

import csv
from pathlib import Path
import statistics

def clean_csv_numeric(input_path: str, output_path: str, column: str, strategy: str = "mean") -> None:
    """
    Handles missing values in a numeric column of a CSV file.
    Strategies: 'mean', 'median', 'drop', or 'fill' with a specified value.
    """
    rows = list(csv.DictReader(Path(input_path).open(newline="")))
    values = []
    for row in rows:
        if row[column] not in ("", None):
            try:
                values.append(float(row[column]))
            except ValueError:
                pass
    
    if strategy == "mean":
        fill_value = statistics.mean(values) if values else 0.0
    elif strategy == "median":
        fill_value = statistics.median(values) if values else 0.0
    elif strategy == "drop":
        rows = [r for r in rows if r[column] not in ("", None)]
        fill_value = None
    else:  # custom fill value passed as strategy (e.g., "0" or "10")
        fill_value = float(strategy)
    
    if fill_value is not None:
        for row in rows:
            if row[column] in ("", None):
                row[column] = f"{fill_value:.2f}"
    
    fieldnames = rows[0].keys() if rows else []
    with Path(output_path).open("w", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(rows)

if __name__ == "__main__":
    sample = "sample_data.csv"
    with Path(sample).open("w", newline="") as f:
        f.write("name,age,salary\nAlice,30,50000\nBob,,60000\nCharlie,25,\nDiana,35,70000\n")
    
    clean_csv_numeric(sample, "cleaned.csv", column="salary", strategy="mean")
    print(Path("cleaned.csv").read_text())