Learn NUMPY with Real Code Examples

Use vectorized operations instead of Python loops

Pre-allocate arrays to improve speed

Leverage broadcasting for memory-efficient computations

Use in-place operations where possible

Consider parallelization libraries for very large arrays

Validate inputs when arrays come from untrusted sources

Avoid buffer overflows with large arrays

Sanitize data before using in ML models

Be careful with memory-mapped files

Ensure secure storage of sensitive datasets

Profiling execution time of array operations

Check memory usage of large arrays

Use assertions to validate numerical results

Integrate with visualization tools

Log preprocessing steps in pipelines

Use vectorized operations for readability and speed

Comment and document array manipulations

Keep arrays immutable unless necessary

Follow Python style guides

Unit-test numerical functions for correctness