numpy

NumPy - Numerical Python

The fundamental package for numerical computing in Python, providing multi-dimensional arrays and fast operations.

When to Use

• Working with multi-dimensional arrays and matrices
• Performing element-wise operations on arrays
• Linear algebra computations (matrix multiplication, eigenvalues, SVD)
• Random number generation and statistical distributions
• Fourier transforms and signal processing basics
• Mathematical operations (trigonometric, exponential, logarithmic)
• Broadcasting operations across different array shapes
• Vectorizing Python loops for performance
• Reading and writing numerical data to files
• Building numerical algorithms and simulations
• Serving as foundation for pandas, scikit-learn, SciPy

Reference Documentation

Official docs: https://numpy.org/doc/
Search patterns: np.array, np.zeros, np.dot, np.linalg, np.random, np.broadcast

Core Principles

Use NumPy For

Task	Function	Example
Create arrays	array, zeros, ones	np.array([1, 2, 3])
Mathematical ops	+, *, sin, exp	np.sin(arr)
Linear algebra	dot, linalg.inv	np.dot(A, B)
Statistics	mean, std, percentile	np.mean(arr)
Random numbers	random.rand, random.normal	np.random.rand(10)
Indexing	[], boolean, fancy	arr[arr > 0]
Broadcasting	Automatic	arr + scalar
Reshaping	reshape, flatten	arr.reshape(2, 3)

Do NOT Use For

• String manipulation (use built-in str or pandas)
• Complex data structures (use pandas DataFrame)
• Symbolic mathematics (use SymPy)
• Deep learning (use PyTorch, TensorFlow)
• Sparse matrices (use scipy.sparse)

Quick Reference

Installation

# pip
pip install numpy

# conda
conda install numpy

# Specific version
pip install numpy==1.26.0

Standard Imports

import numpy as np

# Common submodules
from numpy import linalg as la
from numpy import random as rand
from numpy import fft

# Never import *
# from numpy import *  # DON'T DO THIS!