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.

Practice

1. Which numpy integer type uses the least amount of memory?

easy

A. int64

B. int16

C. int8

D. int32

Solution

1. Step 1: Understand integer type sizes

   Each numpy integer type uses a different number of bits: int8 uses 8 bits, int16 uses 16 bits, int32 uses 32 bits, and int64 uses 64 bits.

2. Step 2: Identify the smallest size

   Since 8 bits is the smallest size, int8 uses the least memory.

3. Final Answer:

   int8 -> Option C

4. Quick Check:

   Smallest bits = int8 [OK]

Hint: Smaller int types use fewer bits and less memory [OK]

Common Mistakes:

• Confusing int8 with int16 or larger types
• Thinking int64 uses less memory
• Assuming int32 is smallest

2. Which of the following is the correct way to create a numpy array of type int16?

easy

A. np.array([1, 2, 3], dtype='int16')

B. np.array([1, 2, 3], type='int16')

C. np.array([1, 2, 3], dtype=int8)

D. np.array([1, 2, 3], type=int16)

Solution

1. Step 1: Recall numpy array creation syntax

   To specify the data type, use the parameter dtype with a string or numpy type.

2. Step 2: Check each option

   np.array([1, 2, 3], dtype='int16') uses dtype='int16', which is correct. Options B and D use type which is invalid. np.array([1, 2, 3], dtype=int8) uses dtype=int8, which is valid syntax but wrong type.

3. Final Answer:

   np.array([1, 2, 3], dtype='int16') -> Option A

4. Quick Check:

   Use dtype='int16' to set type [OK]

Hint: Use dtype='intXX' to set integer type in numpy array [OK]

Common Mistakes:

• Using 'type' instead of 'dtype'
• Passing dtype without quotes for string type
• Confusing int8 and int16 types

3. What will be the output of this code?

import numpy as np
arr = np.array([127, 128], dtype='int8')
print(arr)

medium

A. [127 128]

B. [127 -128]

C. [127 0]

D. Error: value out of range

Solution

1. Step 1: Understand int8 range

   int8 can store values from -128 to 127. The value 128 is outside this range.

2. Step 2: Observe overflow behavior

   When 128 is stored in int8, it wraps around to -128 due to overflow.

3. Final Answer:

   [127 -128] -> Option B

4. Quick Check:

   int8 overflow wraps 128 to -128 [OK]

Hint: int8 max is 127; values above wrap to negative [OK]

Common Mistakes:

• Expecting 128 to store correctly in int8
• Assuming error raised on overflow
• Thinking 128 becomes 0

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([1000, 2000], dtype='int8')
print(arr)

medium

A. No error, prints [1000 2000]

B. SyntaxError due to dtype

C. ValueError: integer out of range

D. Values overflow and wrap around silently

Solution

1. Step 1: Check dtype and values

   The array uses int8 which can only hold -128 to 127, but values 1000 and 2000 are much larger.

2. Step 2: Understand numpy overflow behavior

   Instead of error, numpy silently wraps values using modulo 256, causing overflow.

3. Final Answer:

   Values overflow and wrap around silently -> Option D

4. Quick Check:

   int8 overflow wraps large values silently [OK]

Hint: Large values in small int types wrap silently, no error [OK]

Common Mistakes:

• Expecting ValueError on overflow
• Thinking values print as is
• Confusing syntax error with dtype

5. You have a large dataset of whole numbers ranging from -30000 to 30000. Which numpy integer type should you choose to store this data efficiently without overflow?

hard

A. int16

B. int32

C. int8

D. int64

Solution

1. Step 1: Identify the value range needed

   The data ranges from -30000 to 30000, so the integer type must cover at least this range.

2. Step 2: Check integer type ranges

   int8 covers -128 to 127 (too small), int16 covers -32768 to 32767 (fits range), int32 and int64 cover larger ranges but use more memory.

3. Step 3: Choose the smallest type that fits

   int16 fits the range and uses less memory than int32 or int64.

4. Final Answer:

   int16 -> Option A

5. Quick Check:

   int16 range covers ±30000 efficiently [OK]

Hint: Pick smallest int type covering data range to save memory [OK]

Common Mistakes:

• Choosing int8 which is too small
• Picking int64 unnecessarily
• Ignoring negative range limits