Specifying dtype during creation in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy array with specified dtype

What is the output of this code snippet?

import numpy as np
arr = np.array([1, 2, 3], dtype='float32')
print(arr.dtype)

NumPy

import numpy as np
arr = np.array([1, 2, 3], dtype='float32')
print(arr.dtype)

A int64

B int32

C float64

D float32

💡 Hint

Check the dtype argument passed during array creation.

The dtype argument 'float32' forces the array elements to be 32-bit floating point numbers, so arr.dtype is float32.

❓ data_output

intermediate

Resulting array values with dtype conversion

What is the output array when specifying dtype as int8 for this input?

import numpy as np
arr = np.array([300, -130, 127], dtype='int8')
print(arr)

NumPy

import numpy as np
arr = np.array([300, -130, 127], dtype='int8')
print(arr)

A [-44, 126, 127]

B [44, 126, 127]

C [44, -126, 127]

D [-44, -126, 127]

💡 Hint

Remember int8 stores values from -128 to 127 and wraps around on overflow.

int8 values wrap around using modulo 256 arithmetic. 300 % 256 = 44, -130 % 256 = 126, 127 unchanged. Thus, the array is [44 126 127].

🔧 Debug

advanced

Identify error when specifying dtype incorrectly

What error does this code raise?

import numpy as np
arr = np.array([1, 2, 3], dtype='flot64')

NumPy

import numpy as np
arr = np.array([1, 2, 3], dtype='flot64')

A TypeError: data type 'flot64' not understood

B ValueError: invalid dtype string

C SyntaxError: invalid syntax

D No error, creates array with default dtype

💡 Hint

Check if the dtype string is a valid numpy type.

The dtype string 'flot64' is misspelled and not recognized by numpy, causing a TypeError.

🚀 Application

advanced

Choosing dtype for memory optimization

You have a large dataset of integers ranging from 0 to 1000. Which dtype should you specify to minimize memory usage without losing data?

A np.int8

B np.int32

C np.uint16

D np.float64

💡 Hint

Consider the range of values and the size limits of each dtype.

np.uint16 can store integers from 0 to 65535 using 2 bytes, which covers 0 to 1000 efficiently. np.int8 and np.int32 either have too small or unnecessarily large ranges. np.float64 uses more memory and is for floats.

🧠 Conceptual

expert

Effect of dtype on arithmetic operations

Given two numpy arrays:

import numpy as np
arr1 = np.array([1000], dtype='int16')
arr2 = np.array([1000], dtype='int16')
result = arr1 + arr2
print(result, result.dtype)

What is the output and dtype of result?

NumPy

import numpy as np
arr1 = np.array([1000], dtype='int16')
arr2 = np.array([1000], dtype='int16')
result = arr1 + arr2
print(result, result.dtype)

A [2000] int16

B [2000] int32

C [-25536] int16

D [-25536] int32

💡 Hint

int16 max value is 32767; check if addition overflows.

1000 + 1000 = 2000 fits within int16 range (-32768 to 32767). NumPy preserves the input dtype for arithmetic on same-type integer arrays, so result is [2000] int16. No upcasting or overflow.