Type promotion in operations in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array of 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'

Aint32

Bstr

Cfloat64

Dbool

Attempts:

3 left

💡 Hint

Common Mistakes

Using a float dtype when integers are needed.

Using string dtype which is not numeric.

The dtype int32 creates an integer array. This is important for type promotion in operations.

2fill in blank

medium

Complete the code to add an integer array and a float array, showing type promotion.

NumPy

import numpy as np
arr_int = np.array([1, 2, 3], dtype='int32')
arr_float = np.array([1.0, 2.0, 3.0], dtype="[1]")
result = arr_int + arr_float
print(result.dtype)

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

Afloat32

Bint32

Cbool

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using int dtype for float array, which prevents promotion.

Using string dtype causing errors.

Adding int32 and float32 arrays promotes the result to float32 to avoid losing decimal information.

3fill in blank

hard

Fix the error in the code to correctly multiply an integer array by a float scalar with type promotion.

NumPy

import numpy as np
arr = np.array([1, 2, 3], dtype='int16')
scalar = [1]
result = arr * scalar
print(result.dtype)

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

A'2'

B2

C2.0

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer scalar which does not promote type.

Using string scalar causing errors.

Using a float scalar (2.0) promotes the integer array to float in the multiplication result.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if [2] > 3}
print(lengths)

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

Alen(word)

Blen(words)

Cword

Dlen(word) > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(words) which is total list length.

Using word instead of length for filtering.

The comprehension uses len(word) to get length and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths if length is greater than 2.

NumPy

words = ['cat', 'dog', 'a', 'bird']
[1] = {word.[2](): [3] for word in words if len(word) > 2}
print(result)

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

Aresult

Bupper

Clen(word)

Dlower

Attempts:

3 left

💡 Hint

Common Mistakes

Using lower() instead of upper().

Not assigning the comprehension to a variable.

The dictionary comprehension maps each word in uppercase to its length, filtering words longer than 2 characters.

Practice

1. What happens in NumPy when you add an integer array to a float array?

easy

A. The result array will have integer type, truncating floats.

B. The result array will have float type due to type promotion.

C. NumPy throws a type error and stops execution.

D. The result array will have string type.

Solution

1. Step 1: Understand type promotion in mixed operations

   When adding int and float arrays, NumPy promotes the int to float to avoid losing decimal information.

2. Step 2: Check the resulting data type

   The output array will have float dtype because float has higher precision than int.

3. Final Answer:

   The result array will have float type due to type promotion. -> Option B

4. Quick Check:

   int + float = float [OK]

Hint: Mixed int and float always promote to float [OK]

Common Mistakes:

• Assuming result stays integer and truncates decimals
• Thinking NumPy throws an error on mixed types
• Believing result becomes string type

2. Which of the following NumPy operations correctly promotes types when multiplying an int array by a float scalar?

easy

A. np.array([1, 2, 3]) * 2.0

B. np.array([1, 2, 3]) * '2'

C. np.array([1, 2, 3]) * 2

D. np.array([1, 2, 3]) * np.array(['2', '3', '4'])

Solution

1. Step 1: Identify valid type promotion in multiplication

   Multiplying an int array by a float scalar promotes the result to float.

2. Step 2: Check each option for correct type promotion

   np.array([1, 2, 3]) * 2.0 multiplies by 2.0 (float), so result is float array. np.array([1, 2, 3]) * 2 multiplies by int 2, no promotion. The options involving strings cause errors.

3. Final Answer:

   np.array([1, 2, 3]) * 2.0 -> Option A

4. Quick Check:

   int array * float scalar = float array [OK]

Hint: Multiply int array by float scalar for float result [OK]

Common Mistakes:

• Using string instead of numeric scalar
• Expecting int result when multiplying by float
• Confusing string multiplication with numeric

3. What is the dtype of the result when you run this code?

import numpy as np
arr1 = np.array([1, 2, 3], dtype=np.int32)
arr2 = np.array([1.5, 2.5, 3.5], dtype=np.float64)
result = arr1 + arr2
dtype_result = result.dtype

medium

A. int32

B. object

C. float32

D. float64

Solution

1. Step 1: Identify input array dtypes

   arr1 is int32, arr2 is float64.

2. Step 2: Understand type promotion rules

   When adding int32 and float64, NumPy promotes to float64 to preserve precision.

3. Final Answer:

   float64 -> Option D

4. Quick Check:

   int32 + float64 = float64 [OK]

Hint: Higher precision float wins in mixed int-float ops [OK]

Common Mistakes:

• Assuming result is int32
• Confusing float32 with float64
• Expecting object dtype for numeric arrays

4. Consider this code snippet:

import numpy as np
arr = np.array([1, 2, 3], dtype=np.int8)
result = arr + 1.5

Why might the result's dtype not be what you expect if you change arr to np.int16?

medium

A. Because int16 promotes to string, causing unexpected dtype.

B. Because int16 does not promote and causes an error.

C. Because int16 promotes to float64, changing result dtype.

D. Because int16 promotes to int8, lowering precision.

Solution

1. Step 1: Understand type promotion with int8 and int16

   Adding float 1.5 to int8 promotes to float64. Similarly, int16 also promotes to float64.

2. Step 2: Explain why dtype changes with int16

   int16 has higher precision than int8 but still promotes to float64 when added to float, so result dtype is float64, not int16.

3. Final Answer:

   Because int16 promotes to float64, changing result dtype. -> Option C

4. Quick Check:

   int16 + float = float64 [OK]

Hint: Adding float promotes int16 to float64, not int16 [OK]

Common Mistakes:

• Thinking int16 causes error with float addition
• Assuming int16 promotes down to int8
• Believing result becomes string dtype

5. You have two arrays:

arr_int = np.array([1000, 2000], dtype=np.int16)
arr_float = np.array([0.1, 0.2], dtype=np.float32)

You want to add them and keep the result as float32 to save memory. Which code correctly achieves this with type promotion?

hard

A. result = arr_int.astype(np.float32) + arr_float

B. result = arr_int + arr_float.astype(np.float64)

C. result = arr_int + arr_float

D. result = arr_int.astype(np.int32) + arr_float

Solution

1. Step 1: Understand default type promotion

   Adding int16 and float32 promotes to float64 by default, increasing memory.

2. Step 2: Convert int16 to float32 before addition

   By converting arr_int to float32, adding float32 arrays keeps result as float32, saving memory.

3. Step 3: Check other options

   result = arr_int + arr_float.astype(np.float64) promotes to float64, increasing memory. result = arr_int + arr_float promotes to float64 by default. result = arr_int.astype(np.int32) + arr_float promotes int16 to int32, then adds float32, promoting to float64.

4. Final Answer:

   result = arr_int.astype(np.float32) + arr_float -> Option A

5. Quick Check:

   Cast int to float32 first to keep float32 result [OK]

Hint: Cast int to float32 before adding float32 to keep dtype [OK]

Common Mistakes:

• Ignoring default promotion to float64
• Casting float to float64 increasing memory
• Not casting int before addition