Data Wrangling - Drop the Missing Value

import pandas as pd
import matplotlib.pylab as plt
import numpy as np

url = r"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"
df = pd.read_csv(url, header=None)

headers = ["symboling","normalized-losses","make","fuel-type","aspiration", "num-of-doors","body-style",
         "drive-wheels","engine-location","wheel-base", "length","width","height","curb-weight","engine-type",
         "num-of-cylinders", "engine-size","fuel-system","bore","stroke","compression-ratio","horsepower",
         "peak-rpm","city-mpg","highway-mpg","price"]
df.columns = headers

#replace data with "?" with NAN so it will be treat as Null
df.replace("?", np.nan, inplace = True)

print(df["price"].head(20))

#Drop all rows that do not have price data (missing value) by using dropna()

df.dropna(subset=["price"], axis=0, inplace=True)
df.reset_index(drop=True, inplace=True)

print(df["price"].head(20))