Integer types (int8, int16, int32, int64) in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy int8 overflow behavior

What is the output of this code snippet using numpy int8 type?

NumPy

import numpy as np
x = np.array([127], dtype=np.int8)
y = x + 1
print(y[0])

A -128

B 128

C OverflowError

D 127

💡 Hint

Remember that int8 can only hold values from -128 to 127 and wraps around on overflow.

Adding 1 to the maximum int8 value 127 causes overflow and wraps around to -128.

❓ data_output

intermediate

Size in bytes of different numpy integer types

What is the output of this code showing the itemsize of numpy integer types?

NumPy

import numpy as np
sizes = {t: np.dtype(t).itemsize for t in ['int8', 'int16', 'int32', 'int64']}
print(sizes)

A {'int8': 1, 'int16': 2, 'int32': 4, 'int64': 8}

B {'int8': 8, 'int16': 16, 'int32': 32, 'int64': 64}

C {'int8': 0, 'int16': 1, 'int32': 2, 'int64': 4}

D {'int8': 2, 'int16': 4, 'int32': 8, 'int64': 16}

💡 Hint

Itemsize is the number of bytes used to store each integer type.

int8 uses 1 byte, int16 uses 2 bytes, int32 uses 4 bytes, and int64 uses 8 bytes.

🔧 Debug

advanced

Identify the error in numpy int16 overflow addition

What error will this code raise when adding two numpy int16 arrays?

NumPy

import numpy as np
x = np.array([32767], dtype=np.int16)
y = np.array([1], dtype=np.int16)
z = x + y
print(z[0])

A TypeError

B OverflowError

C ValueError

D No error, output is -32768

💡 Hint

Check how numpy handles overflow for fixed-size integer types.

Numpy does not raise an error on overflow for fixed-size integers; it wraps around.

🧠 Conceptual

advanced

Range of values for numpy int32 type

What is the correct range of values that a numpy int32 type can represent?

A -128 to 127

B -2,147,483,648 to 2,147,483,647

C -32,768 to 32,767

D 0 to 4,294,967,295

💡 Hint

int32 is a signed 32-bit integer.

Signed 32-bit integers range from -2^31 to 2^31 - 1.

🚀 Application

expert

Choosing the smallest numpy integer type for given data

You have a dataset with integer values ranging from -100 to 100. Which numpy integer type is the most memory efficient to store this data without overflow?

A np.uint8

B np.int32

C np.int8

D np.int16

💡 Hint

Check the range of int8 and uint8 types.

int8 can store values from -128 to 127, which covers -100 to 100 safely.