Performance & Optimization
Make your Pandas code fly. Vectorization, Categorical types, and memory tricks for large datasets.
Make your Pandas code fly. Vectorization, Categorical types, and memory tricks for large datasets. This hands-on tutorial focuses on practical implementation of performance & optimization concepts.
Module 9: Performance & Optimization
Pandas is fast, but bad code can make it slow. This module teaches you how to write professional, high-performance Pandas code that handles millions of rows without crashing.
Lesson 19: Vectorization (Again!)
Just like NumPy, NEVER loop over rows in Pandas.
- Bad:
for index, row in df.iterrows(): ... - Good:
df['col'] = df['col'] * 2
Vectorized operations are implemented in C and can be 1000x faster than Python loops.
Lesson 20: Memory Optimization
Categorical Data
Storing text columns repeated millions times (e.g., "Male", "Male", "Female") is wasteful. Convert them to Category dtype.
df['Gender'] = df['Gender'].astype('category')
This stores the unique strings once and uses integers under the hood, saving huge amounts of RAM.
Downcasting Numbers
Do you need int64 (up to 9 quintillion) for a column of ages (0-100)? No. Use int8 or float32.
Practice: The Optimizer
Challenge:
- Create a DataFrame with 10 rows of "Yes" and "No" strings.
- Check the memory usage (
df.memory_usage(deep=True)). - Convert the column to "category" type and check memory usage again.
Quiz
Question 1 of 5Which method of iterating over a DataFrame is generally the slowest and should be avoided?
Key Takeaways
✅ Avoid loops. Use vectorization.
✅ Use astype('category') for text columns with repeated values.
✅ Check df.memory_usage(deep=True) to find heavy columns.