Type promotion in operations in NumPy - Mini Project: Build & Apply

Type promotion in operations

📖 Scenario: Imagine you are working with two sets of numbers in a scientific experiment. One set contains whole numbers (integers), and the other set contains decimal numbers (floats). You want to combine these numbers and see how Python handles their types when doing math.

🎯 Goal: You will create two NumPy arrays with different data types, add them together, and observe how NumPy changes the data type of the result automatically. This is called type promotion.

📋 What You'll Learn

Create two NumPy arrays with specified data types

Add the two arrays together

Check and print the data type of the result

💡 Why This Matters

🌍 Real World

In real scientific and engineering work, data often comes in different types. Understanding how these types combine helps avoid mistakes and ensures accurate calculations.

💼 Career

Data scientists and engineers frequently work with mixed data types. Knowing type promotion helps in writing efficient and correct code for data analysis and modeling.

1

Create two NumPy arrays with different types

Import NumPy as np. Create a NumPy array called array_int with values [1, 2, 3] and data type int32. Create another NumPy array called array_float with values [1.5, 2.5, 3.5] and data type float64.

NumPy

import numpy as np
# Create array_int with values [1, 2, 3] and dtype int32
# Create array_float with values [1.5, 2.5, 3.5] and dtype float64

Hint

Use np.array() with the dtype parameter to set the data type.

2

Add the two arrays

Create a new variable called result that stores the sum of array_int and array_float.

NumPy

import numpy as np
array_int = np.array([1, 2, 3], dtype=np.int32)
array_float = np.array([1.5, 2.5, 3.5], dtype=np.float64)
# Add array_int and array_float and store in result

Hint

Use the + operator to add the two arrays element-wise.

3

Check the data type of the result

Create a variable called result_dtype that stores the data type of the result array using the .dtype attribute.

NumPy

import numpy as np
array_int = np.array([1, 2, 3], dtype=np.int32)
array_float = np.array([1.5, 2.5, 3.5], dtype=np.float64)
result = array_int + array_float
# Get the data type of result and store in result_dtype

Hint

Use result.dtype to get the data type of the array.

4

Print the result and its data type

Print the result array and then print the result_dtype variable to see the values and the data type after addition.

NumPy

import numpy as np
array_int = np.array([1, 2, 3], dtype=np.int32)
array_float = np.array([1.5, 2.5, 3.5], dtype=np.float64)
result = array_int + array_float
result_dtype = result.dtype
# Print the result array
# Print the result_dtype

Hint

Use two print() statements: one for result and one for result_dtype.

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