NumPy Cheatsheet - NumPy Functions & Array Operations Reference

This reference is for developers who frequently process arrays in Python, from quick data cleaning to embedding matrix math: creating arrays with np.array/np.zeros, pulling out data by index and slice, reshaping, drawing statistics with sum/mean, and matrix multiplication with a @ b. Unlike a bare API manual, entries follow the practical chain of create → select → reshape → compute → save. After reading you should be able to pick the right function for each stage and dodge the most common broadcast and view-vs-copy pitfalls.

Languages·40 commands·Last updated 2026-07-21

Array Creation 9

np.array([1, 2, 3])

Create array from a list

np.zeros((3, 4))

3x4 array of zeros

np.ones((2, 3))

2x3 array of ones

np.arange(0, 10, 2)

Evenly spaced array [0, 2, 4, 6, 8]

np.linspace(0, 1, 5)

5 points evenly spaced from 0 to 1

np.eye(3)

3x3 identity matrix

np.random.rand(3, 3)

3x3 uniform random numbers

np.random.randn(3, 3)

3x3 standard normal random numbers

np.random.randint(0, 10, (2, 3))

2x3 random integers

Indexing & Slicing 6

a[0]

First element

a[1:3]

Slice: take items 2 and 3

a[a > 5]

Boolean indexing: items > 5

a[[0, 2, 4]]

Fancy indexing: selected positions

a[1:3, 2:4]

2D slice: rows 1-2, cols 2-3

np.where(a > 5, a, 0)

Conditional replace: keep > 5, else 0

Shape Manipulation 8

a.shape

View array shape

a.reshape(2, 3)

Reshape to 2x3

a.flatten()

Flatten to 1D

a.T

Transpose

np.concatenate([a, b], axis=0)

Concatenate along rows

np.vstack([a, b])

Stack vertically

np.hstack([a, b])

Stack horizontally

np.split(a, 3)

Split into 3 equal parts

Math & Statistics 10

a.sum()

Sum all elements

a.mean()

Mean

a.std()

Standard deviation

a.min() / a.max()

Min / max

a.argmin() / a.argmax()

Index of min / max

a.cumsum()

Cumulative sum

np.dot(a, b)

Matrix dot product

a @ b

Matrix multiplication (Python 3.5+)

np.sort(a)

Sort

np.unique(a)

Unique values

Broadcasting & Saving 7

a + 1

Broadcast: add 1 to every element

a * 2

Broadcast: multiply every element by 2

a + b (compatible shapes)

Broadcasted arithmetic (compatible shapes)

np.save("data.npy", a)

Save as .npy file

np.load("data.npy")

Load .npy file

np.savetxt("data.csv", a, delimiter=",")

Save as CSV

np.loadtxt("data.csv", delimiter=",")

Load from CSV

Tips

NumPy arrays are homogeneous - all elements share one dtype; mixing types is auto-promoted.
Broadcasting rule: compare dimensions from the rightmost; they must be equal or one of them is 1.
a * b is element-wise; use a @ b or np.dot(a, b) for matrix multiplication.

Official References

Each command links to its official documentation below, so you can verify the latest usage and read deeper.

Official Docs

Maintained by LaoHand

Publicly updated on Jul 21, 2026, continuously proofread against official docs.

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