Default integer (int64) and float (float64) columns consume substantial RAM. Downcasting to int8, int16, or float32 can reduce memory consumption by over 70%.
Pandas Memory Optimization
Downcast datatypes and downsize DataFrame memory footprints.
Key Takeaways: Pandas Memory Optimization
Mastering Pandas Memory Optimization helps you write clean and reliable Pandas code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.
Pandas Memory Optimization Code Example
Pandasimport pandas as pd
df = pd.DataFrame({"id": range(1000)})
print("Initial memory:", df["id"].memory_usage(index=False), "bytes")
df["id"] = pd.to_numeric(df["id"], downcast="unsigned")
print("Optimized memory:", df["id"].memory_usage(index=False), "bytes")Output
Initial memory: 8000 bytes Optimized memory: 2000 bytes
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