Master programming syntax, solve interactive challenges & run live code in Syntaxis Explore Arena →

menu_book Course Curriculum (NumPy) expand_more

Array Creation

array() from Python sequences arange() linspace() zeros() and ones() full() eye() identity matrices empty() and initialization dtype selection

Shape & Indexing

ndim, shape, and size reshape() ravel() versus flatten() transpose() and .T basic slicing negative indexing Boolean masking fancy integer indexing

Broadcasting & Vectorization

broadcasting rules scalar broadcasting row and column broadcasting newaxis / None where() selection vectorized arithmetic universal functions (ufuncs) vectorize() trade-offs

Aggregation & Statistics

sum() by axis mean() and median() min(), max(), argmin(), argmax() std() and var() percentile() and quantile() any() and all() cumsum() and cumprod() nan-aware reductions

Combining & Splitting

concatenate() stack() vstack() and hstack() column_stack() split() array_split() repeat() tile()

Linear Algebra & Random

dot() and @ matrix multiplication shapes solve() linear systems det() determinant inv() caveats eigenvalues with eig() default_rng() random sampling without replacement

NumPy / Linear Algebra & Random

matrix multiplication shapes

Learn matrix multiplication shapes in NumPy with a dedicated explanation, a concept-specific example, practical cautions, and a mastery check.

12 min read • Advanced

numpy-042.py

python

import numpy as np
A=np.ones((2,3)); B=np.arange(12).reshape(3,4)
C=A @ B
print(C.shape, C)

What to watch

Content source & attribution

Original Syntaxis Content · Original Syntaxis Content

Lesson quizzes

matrix multiplication shapes Mastery Check →

solve() linear systems