What is NumPy?

The NumPy library (Numerical Python) is the foundation of scientific computing in Python. Its core type is the ndarray: a multidimensional array of elements that share one dtype.

Core concepts

• ndarray — n-dimensional array with shape, dtype, and ndim
• Vectorization — apply operations to whole arrays without Python loops
• Broadcasting — align differently shaped arrays for element-wise math
• ufuncs — universal functions (np.add, np.sqrt) that run in C

Typical use cases

• Image and signal processing (2D/3D arrays)
• Feature matrices for machine learning
• Simulation and Monte Carlo sampling
• Linear algebra and statistics

Import convention

import numpy as np

x = np.zeros((2, 3))
print(x.shape)  # (2, 3)

Important interview questions and answers

1. Q: Homogeneous vs heterogeneous?
   A: All elements share one dtype—unlike Python lists that can mix int, str, and objects.
2. Q: Why import as np?
   A: Universal convention—readable and matches documentation across the ecosystem.

Self-check

1. Name four core NumPy concepts.
2. Give one real-world use case for ndarrays.

Tip: Always print shape and dtype when debugging arrays.