Stop Using .iterrows(). Here's What Actually Fast Looks Like

You're looping over a DataFrame. It feels natural. It's killing your performance.

# What most tutorials say

for index, row in df.iterrows():

    df.at[index, 'tax'] = row['price'] * 0.17

Here's the progression you should actually know:

# Level 1: Vectorization — 10-100x faster 

df['tax'] = df['price'] * 0.17



# Level 2: .apply() when logic is conditional 

df['tax'] = df['price'].apply(lambda x: x * 0.17 if x > 0 else 0)



# Level 3: np.where — the fastest option 

import numpy as np

df['tax'] = np.where(df['price'] > 0, df['price'] * 0.17, 0)

Pandas wraps NumPy. NumPy operates on entire arrays at the C level. The moment you loop row by row, you throw that away.

The shift: don't think "what do I do to each row?" rather you should ask "what transformation applies to this column?"

That's it. Notebooks that took minutes will now run in seconds.