Arrays, not lists
Typed, contiguous numeric data is fast and memory-efficient.
Open source · Scientific computing
NumPy
NumPy gives Python real numerical arrays. Nearly every data, science, and machine-learning library builds on it.
- GitHub stars
- 32.7K
- Language
- Python / C
- License
- BSD-3-Clause
- Runs on
- Any Python platform
01
/ 03What it is
The array the ecosystem assumes.
NumPy gives Python real numerical arrays. Nearly every data, science, and machine-learning library builds on it.
Vectorized operations
Apply one operation to an entire array without Python loops.
The foundation layer
pandas, SciPy, PyTorch, and MONAI all exchange NumPy arrays.
02
/ 03Capabilities
Core skills used everywhere.
The primitives behind almost every notebook.
N-dimensional arrays
Efficient containers for homogeneous numerical data.
Broadcasting
Combine arrays of different shapes without explicit loops.
Linear algebra
Matrix products, decompositions, and solvers run in compiled code.
03
/ 03How to use
Run your first array.
Install NumPy and replace a loop with vectorized math.
01
Install NumPy
Install explicitly in a clean environment.
terminal
pip install numpy02
Create an array
Build an array and multiply every value at once.
array.py
import numpy as np
values = np.array([1, 2, 3, 4])
print(values * 2)Keep exploring
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