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.

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