Array attributes (shape, dtype, ndim, size) in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the number of dimensions of the array.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(arr.[1])

Drag options to blanks, or click blank then click option'

Andim

Bshape

Csize

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'shape' instead of 'ndim' to get the number of dimensions.

Using 'size' which gives total elements, not dimensions.

The ndim attribute tells how many dimensions the array has.

2fill in blank

medium

Complete the code to print the total number of elements in the array.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr.[1])

Drag options to blanks, or click blank then click option'

Asize

Bshape

Cndim

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'shape' which returns a tuple, not a single number.

Using 'ndim' which returns number of dimensions, not elements.

The size attribute gives the total number of elements in the array.

3fill in blank

medium

Complete the code to print the data type of the array elements.

NumPy

import numpy as np
arr = np.array([1, 2, 3])
print(arr.[1])

Drag options to blanks, or click blank then click option'

Ashape

Bdtype

Csize

Dndim

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'shape' or 'size' instead of 'dtype'.

Using 'ndim' which returns number of dimensions.

The dtype attribute gives the data type of the array elements.

4fill in blank

hard

Fill both blanks to print the number of rows and columns of the array.

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.[1][0], arr.[2][1])

Drag options to blanks, or click blank then click option'

Andim

Bshape

Csize

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ndim' which returns a single number, not indexable.

Using 'size' which is total elements, not dimensions.

The shape attribute returns a tuple of the array dimensions. shape[0] is the number of rows, shape[1] is the number of columns.

5fill in blank

hard

Fill all three blanks to compute the total number of elements by multiplying the dimensions.

NumPy

import numpy as np
arr = np.zeros((2, 3, 4))
print(arr.[1][0] * arr.[2][1] * arr.[3][2])

Drag options to blanks, or click blank then click option'

Andim

Bshape

Csize

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'size' directly instead of computing from shape.

Trying to index 'ndim' or 'dtype'.

The product of the values in the shape tuple gives the total number of elements (2 * 3 * 4 = 24).