NumPy Arrays & Vectorization

Understand array shape, dtype, indexing, aggregation and element-wise operations.

By the end of this lesson

You will be able to

Create one- and two-dimensional arrays.
Inspect shape, ndim and dtype.
Select values with slicing and boolean masks.
Use vectorized operations and reductions.

Shape tells you how values are arranged

A one-dimensional array has one axis. A two-dimensional array has rows and columns. The shape tuple describes the size along each axis, while dtype describes the element type.

import numpy as np

counts = np.array([12, 18, 25, 19])
print(counts.shape)  # (4,)
print(counts.ndim)   # 1
print(counts.dtype)

matrix = np.array([[12, 18], [25, 19]])
print(matrix.shape)  # (2, 2)

Operate on arrays directly

Vectorized expressions operate on corresponding values without writing a Python loop. Boolean masks select values by condition. Aggregations such as sum and mean can operate over the whole array or along a chosen axis.

temperatures = np.array([18.0, 20.5, 22.0, 17.5])
above_20 = temperatures[temperatures > 20]

scaled = (temperatures - temperatures.mean()) / temperatures.std()
print(above_20)
print(scaled)

Apply what you learned

Exercise

Summarize numeric sensor values

Use NumPy to calculate a basic summary and filter values without a row-by-row loop.

Create an array of temperatures.
Inspect its shape, dimensionality and dtype.
Compute its mean and standard deviation.
Select values above the mean using a boolean mask.
Explain the axis argument with a two-dimensional example.

Done when

The summary is computed with NumPy operations and the selected values match the boolean condition.

Free references

Study resources

1 links

01
The absolute basics for beginners Article
Read array creation, indexing, operations, aggregation and broadcasting. Skip advanced topics for now.

Done

Check your understanding

Self-test

2 questions

Question 1

What does shape == (3, 4) tell you?
Question 2

What is vectorization?

Completion check

Move on when

You can create, inspect, slice and aggregate arrays while explaining the shape of each result.

Mark passed and continue Back to module