Why dtypes matter for performance in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array with integer data type.

NumPy

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

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

Abool

Bfloat64

Cstr

Dint32

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'float64' when integers are needed.

Using 'str' which is for text data.

Using int32 sets the array data type to 32-bit integers, which is efficient for integer data.

2fill in blank

medium

Complete the code to create a NumPy array of floats for better precision.

NumPy

import numpy as np
arr = np.array([1.0, 2.5, 3.1], dtype=[1])
print(arr.dtype)

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

Aint64

Bbool

Cfloat64

Dint32

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer types for decimal numbers causes truncation.

Using boolean type which only stores True or False.

float64 is used for floating-point numbers with double precision, suitable for decimal values.

3fill in blank

hard

Fix the error in the code by choosing the correct dtype for a boolean array.

NumPy

import numpy as np
arr = np.array([True, False, True], dtype=[1])
print(arr.dtype)

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

Abool

Bfloat64

Cint32

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer or float types wastes memory for boolean data.

Using string type causes errors when printing dtype.

The bool dtype is used for arrays of True/False values, which is memory efficient.

4fill in blank

hard

Fill both blanks to create a NumPy array of integers and convert it to float for better precision.

NumPy

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

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

Aint32

Bfloat64

Cint64

Dbool

Attempts:

3 left

💡 Hint

Common Mistakes

Using boolean dtype initially causes wrong data representation.

Converting to integer instead of float loses decimal precision.

Start with an integer array (int32) and convert it to floating-point (float64) for decimal precision.

5fill in blank

hard

Fill both blanks to create a float array, check its size, and convert it to int to save memory.

NumPy

import numpy as np
arr = np.array([1.5, 2.3, 3.7], dtype=[1])
print(arr.nbytes)
arr_int = arr.astype([2])
print(arr_int.nbytes)
print(arr_int)

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

Afloat64

Bint32

Cfloat32

Dint64

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same dtype for both arrays does not change memory usage.

Converting to float instead of int does not reduce memory.

Start with float64 for precision, then convert to int32 to reduce memory size. The printed sizes show memory usage before and after conversion.