NumPy Cheatsheet - NumPy Functions & Array Operations Reference

All essential NumPy commands organized by use case, with 40+ entries you can copy and run directly. Find the right command fast when you need it.

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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

Commands are compiled from the official docs below. Click to verify the latest usage.

Official Docs

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

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