NumPy Cheat Sheet — Science Coding Edition

NumPy Cheat Sheet — Science Coding Edition - Printable Version

+- The Lumin Archive (https://theluminarchive.co.uk)
+-- Forum: The Lumin Archive — Core Forums (https://theluminarchive.co.uk/forumdisplay.php?fid=3)
+--- Forum: Publications & Research (https://theluminarchive.co.uk/forumdisplay.php?fid=12)
+---- Forum: Educational Resources (https://theluminarchive.co.uk/forumdisplay.php?fid=45)
+---- Thread: NumPy Cheat Sheet — Science Coding Edition (/showthread.php?tid=82)

NumPy Cheat Sheet — Science Coding Edition - Leejohnston - 11-13-2025

NumPy Cheat Sheet — Science Coding Edition

NumPy is the foundation of scientific computing in Python.
It powers simulations, data analysis, physics calculations, AI, and large-scale maths.

This sheet gives you the essential commands and concepts every science coder needs.

-----------------------------------------------------------------------

1. Importing NumPy

The standard import:

Code:
import numpy as np

-----------------------------------------------------------------------

2. Creating Arrays

1D array:

Code:
a = np.array([1, 2, 3, 4])

2D array (matrix):

Code:
b = np.array([

    [1, 2],

    [3, 4]

])

Zeros & ones:

Code:
np.zeros(5)

np.ones((3,3))

Range of values:

Code:
np.arange(0, 10, 2)  # 0,2,4,6,8

Even spacing:

Code:
np.linspace(0, 1, 5)

-----------------------------------------------------------------------

3. Array Operations (Fast Maths)

NumPy does maths element-by-element:

Code:
a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

a + b  # [5, 7, 9]

a * b  # [4, 10, 18]

a ** 2  # [1, 4, 9]

Scalar operations:

Code:
a + 10      # [11, 12, 13]

a * 3      # [3, 6, 9]

-----------------------------------------------------------------------

4. Useful Functions

Code:
np.sum(a)

np.mean(a)

np.max(a)

np.min(a)

np.std(a)      # standard deviation

np.sqrt(a)

np.exp(a)

np.log(a)

-----------------------------------------------------------------------

5. Indexing & Slicing

Select elements like Python lists:

Code:
a = np.array([10, 20, 30, 40])

a[0]        # 10

a[1:3]      # [20, 30]

a[-1]      # 40

2D indexing:

Code:
b = np.array([

    [1, 2, 3],

    [4, 5, 6]

])

b[0, 1]    # 2

b[:, 0]    # first column → [1, 4]

b[1, :]    # second row → [4, 5, 6]

-----------------------------------------------------------------------

6. Shape & Reshaping

Code:
a.shape

a.reshape(2, 3)

Example:

Code:
x = np.arange(6)        # [0 1 2 3 4 5]

x.reshape(2, 3)        # 2 rows, 3 columns

-----------------------------------------------------------------------

7. Matrix Operations (Important for Physics & AI)

Matrix multiplication:

Code:
np.dot(A, B)

Code:
A @ B

Transpose:

Code:
A.T

Inverse (if square matrix):

Code:
np.linalg.inv(A)

Determinant:

Code:
np.linalg.det(A)

Solve linear equations:

Code:
np.linalg.solve(A, b)

-----------------------------------------------------------------------

8. Random Numbers (Useful in Simulations)

Code:
np.random.rand(3)        # random numbers 0–1

np.random.randn(3)        # normal distribution

np.random.randint(0, 10)  # random integer

Random arrays:

Code:
np.random.rand(2, 3)

-----------------------------------------------------------------------

9. Loading & Saving Data

Load CSV:

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

Save array:

Code:
np.savetxt("output.csv", data, delimiter=",")

-----------------------------------------------------------------------

10. Common Mistakes

❌ Using lists instead of arrays for scientific maths
✔ NumPy arrays are MUCH faster and support vectorised maths

❌ Forgetting that * multiplies element-by-element
✔ Use dot() or @ for matrix multiplication

❌ Mixing shapes (e.g., 3×3 with 2×2)
✔ Check array dimensions with .shape

❌ Using Python math functions
✔ Use np.sqrt(), np.log(), np.exp() for arrays

❌ Forgetting to import NumPy
✔ import numpy as np

-----------------------------------------------------------------------

Summary

NumPy gives you:
• fast arrays
• vectorised maths
• matrix operations
• scientific functions
• random numbers
• reshaping & slicing
• data loading

It’s the foundation of all serious scientific computing in Python.