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

Need a 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

Need a 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

Need a 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

Need a hint?

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