NumPy
What it is: Foundation of numerical computing in Python. N-dimensional arrays, linear algebra, mathematical operations.
What It Does Best
Fast array operations. Vectorized computations—no loops needed. 50-100x faster than pure Python lists.
Memory efficient. Arrays use less memory than Python lists. Critical for large datasets.
Universal foundation. Pandas, scikit-learn, TensorFlow, PyTorch—everything builds on NumPy.
Pricing
Free. Open source, BSD license.
When to Use It
✅ Any numerical computing in Python
✅ Array operations and linear algebra
✅ Foundation for other libraries
✅ Performance-critical operations
When NOT to Use It
❌ Tabular data (use Pandas instead)
❌ Don't need numerical operations
❌ Pure Python lists sufficient
Bottom line: Non-negotiable for Python data work. If you're doing math or arrays in Python, you're using NumPy. Learn this first.