100 Essential NumPy Operations and Code Snippets

This guide compiles a list of 100 frequently used NumPy operations, providing clear descriptions and executable code snippets for each task. The examples cover basic array creation, elementwise arithmetic, statistical functions, reshaping, broadcasting, linear algebra, random sampling, and many specialized utilities.

Below are representative code examples from the collection:

import numpy as np

# 1. Import NumPy and alias as np import numpy as np

# 2. Print NumPy version and configuration print(np.__version__) print(np.show_config())

# 3. Create a zero vector of length 10 Z = np.zeros(10) print(Z)

# 4. Find memory size of an array Z = np.zeros((10, 10)) print("%d bytes" % (Z.size * Z.itemsize))

# 5. Get documentation for a function np.info(np.add)

# 6. Create a vector with a specific element set to 1 Z = np.zeros(10) Z[4] = 1 print(Z)

# 7. Generate a range of integers Z = np.arange(10, 50) print(Z)

# 8. Reverse a vector Z = np.arange(50) Z = Z[::-1] print(Z)

# 9. Create a 3×3 matrix with values 0‑8 Z = np.arange(9).reshape(3, 3) print(Z)

# 10. Find indices of non‑zero elements nz = np.nonzero([1, 2, 0, 0, 4, 0]) print(nz)

... (the article continues with similar concise explanations and code for tasks such as creating identity matrices, random arrays, statistical summaries, masking, broadcasting, advanced indexing, linear algebra operations, custom dtypes, and performance‑oriented tricks) ...

Each section follows the pattern of a brief description, a hint, and a pre block containing the exact Python code needed to perform the operation, making the resource valuable for both beginners and experienced developers working with NumPy.