Integer types (int8, int16, int32, int64) in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array with 8-bit integers.

NumPy

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

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

Afloat32

Bint8

Cstr

Dbool

Attempts:

3 left

💡 Hint

Common Mistakes

Using a float or string dtype instead of an integer type.

Forgetting to specify the dtype.

The int8 type creates an array of 8-bit integers, which can store small integer values efficiently.

2fill in blank

medium

Complete the code to create a NumPy array with 16-bit integers.

NumPy

import numpy as np
arr = np.array([1000, 2000, 3000], dtype=[1])
print(arr.dtype)

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

Aint32

Bint8

Cint16

Dfloat64

Attempts:

3 left

💡 Hint

Common Mistakes

Using int8 which is too small for these values.

Using floating point types instead of integer types.

The int16 type stores 16-bit integers, which can hold larger integer values than int8.

3fill in blank

hard

Fix the error in the code to create a 32-bit integer array.

NumPy

import numpy as np
arr = np.array([100000, 200000, 300000], dtype=[1])
print(arr.dtype)

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

Aint32

Bint8

Cint16

Dfloat32

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing int8 or int16 which cause overflow.

Using floating point types unnecessarily.

The values are too large for int8 or int16. Using int32 allows storing larger integers.

4fill in blank

hard

Fill both blanks to create a NumPy array with 64-bit integers and print its type.

NumPy

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

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

A10000000000

Bint8

Cint64

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Using a small integer value that fits smaller types.

Using int8 which cannot hold large numbers.

The number 10000000000 fits in 64-bit integers but not smaller types. The dtype int64 stores 64-bit integers.

5fill in blank

hard

Fill all four blanks to create a dictionary mapping integer types to their bit sizes.

NumPy

int_sizes = { '[1]': [2], '[3]': [4] }

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

Aint8

B8

Cint16

D16

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up bit sizes for integer types.

Using incorrect keys or values.

The dictionary maps int8 to 8 bits and int16 to 16 bits.