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

1. 1Use the .apply() function to perform operations on each row or column of a DataFrame
2. 2Utilize vectorized operations to perform element-wise calculations on entire DataFrames at once
3. 3Leverage the .loc[] accessor to select and manipulate specific rows and columns
4. 4Apply NumPy's universal functions to perform efficient element-wise operations
5. 5Use Dask DataFrames for parallel computing on large datasets

Data scientists and analysts who work with large datasets can benefit from this knowledge to improve their code's efficiency and scalability

💡 .iterrows() is slow because it creates a new Series for each row, whereas vectorized operations and other alternatives can process entire DataFrames at once

💡 Ditch .iterrows() for faster pandas performance! Use .apply(), vectorized ops, .loc[], and NumPy's universal functions instead