Array attributes (shape, dtype, ndim, size) in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the shape attribute of a NumPy array tell you?

The shape attribute shows the size of the array in each dimension. For example, (3, 4) means 3 rows and 4 columns.

beginner

What information does the dtype attribute provide in a NumPy array?

The dtype attribute tells you the type of data stored in the array, like integers (int32) or floating-point numbers (float64).

beginner

Explain the ndim attribute of a NumPy array.

The ndim attribute tells you how many dimensions the array has. For example, 1 means a vector, 2 means a matrix, and so on.

beginner

What does the size attribute represent in a NumPy array?

The size attribute shows the total number of elements in the array, no matter how many dimensions it has.

intermediate

How can you use these attributes to understand a NumPy array quickly?

By checking shape, dtype, ndim, and size, you learn the array's layout, data type, number of dimensions, and total elements. This helps you know what kind of data you have and how it is organized.

What does arr.shape return for a NumPy array?

A The total number of elements in the array

B The number of dimensions of the array

C The data type of the array elements

D The size of each dimension of the array

shape gives the size of each dimension, like (rows, columns).

If arr.ndim is 3, what does it mean?

A The array has 3 dimensions

B The array has 3 elements

C The array has 3 rows

D The array has 3 columns

ndim tells how many dimensions the array has.

Which attribute tells you the total number of elements in a NumPy array?

A shape

B size

C dtype

D ndim

size is the total count of all elements in the array.

What does arr.dtype show?

A The shape of the array

B The number of dimensions

C The data type of elements

D The total size of the array

dtype tells the type of data stored, like int or float.

If an array has shape = (4, 5), what is its size?

A 20

B 5

C 9

D 4

Size is the product of shape dimensions: 4 * 5 = 20.

Describe what each of these NumPy array attributes means: shape, dtype, ndim, and size.

Think about how these attributes help you understand the array's structure and data.

How would you use the shape and size attributes to find out how many elements are in each dimension of a NumPy array?

Remember that size is the total number of elements, and shape tells the size of each dimension.