How do you detect, handle, and fill missing values in a Pandas DataFrame?

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'name':   ['Alice', 'Bob', None, 'Dave'],
    'age':    [30, np.nan, 35, 28],
    'score':  [88, 72, np.nan, np.nan],
})

# --- Detection ---
df.isnull()               # boolean DataFrame — True where NaN
df.isnull().sum()         # count NaNs per column
df.isnull().sum() / len(df) * 100  # % missing per column
df.notnull()              # inverse of isnull

# --- Dropping ---
df.dropna()               # drop rows with ANY NaN
df.dropna(how='all')      # drop rows where ALL values are NaN
df.dropna(subset=['age']) # drop rows with NaN only in 'age'
df.dropna(axis=1, thresh=3)  # drop columns with fewer than 3 non-NaN values

# --- Filling ---
df['score'].fillna(df['score'].mean())   # fill with column mean
df['score'].fillna(method='ffill')       # forward fill (propagate last valid)
df['score'].fillna(method='bfill')       # backward fill
df.fillna({'age': 0, 'name': 'Unknown'})  # column-specific fills

# --- Interpolation ---
df['score'].interpolate(method='linear')  # linear interpolation between values

# --- Replace specific sentinel values ---
df.replace(-999, np.nan)  # treat -999 as missing