How to deal with non trivial missing values when using pandas read_csv

missing_values_read_csv.py

import pandas as pd
import numpy as np
import time

url = "http://archive.ics.uci.edu/ml/machine-learning-databases/mammographic-masses/mammographic_masses.data"
names = ['BI-RADS', 'Age', 'Shape', 'Margin', 'Density', 'Severity']


def manual_convert():
    df = pd.read_csv(url, names=names)
    df = df.replace('?', np.NAN)
    df.loc[:, names[:-1]] = df.loc[:, names[:-1]].apply(pd.to_numeric)


def use_na_values():
    df = pd.read_csv(url, names=names, na_values=["?"])


def use_converters():
    df = pd.read_csv(
        url,
        names=names,
        converters={"BI-RADS": lambda x: x if x != "?" else np.NAN}
        )


def repeat(func, n=10):
    times = []
    for _ in range(n):
        start = time.time()
        func()
        end = time.time()
        times.append(end-start)
    return sum(times)/len(times)

n = 100
print("manual_convert", repeat(manual_convert, n))
print("use_na_values", repeat(use_na_values, n))
print("use_converters", repeat(use_converters, n))