Why does numpy use different integer types like int8, int16, int32, and int64 instead of just one integer type?

hard📝 Conceptual Q10 of 15

NumPy - Array Data Types

ABecause numpy does not support floating point numbers

BTo optimize memory usage and performance based on data size

CTo make code more complex and harder to read

DTo force users to learn multiple data types

Step-by-Step Solution

Solution:

Step 1: Understand memory optimization
Using smaller integer types saves memory when large ranges are not needed.
Step 2: Consider performance benefits
Smaller types can improve speed and reduce storage in large datasets.
Final Answer:
To optimize memory usage and performance based on data size -> Option B
Quick Check:
Different integer types balance memory and range needs [OK]

Quick Trick: Choose integer type based on value range to save memory [OK]

Common Mistakes:

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