Float types (float16, float32, float64) in NumPy - Mini Project: Build & Apply

Working with Float Types in NumPy

📖 Scenario: You are analyzing temperature data collected from different sensors. Each sensor records temperatures with different precision levels. You want to store these temperatures using different float types to understand how precision affects the data.

🎯 Goal: Create NumPy arrays with float16, float32, and float64 types and observe their differences.

📋 What You'll Learn

Create a NumPy array with float16 type

Create a NumPy array with float32 type

Create a NumPy array with float64 type

Print the arrays and their data types

💡 Why This Matters

🌍 Real World

Sensors and devices often record measurements with different precision levels. Choosing the right float type helps balance memory use and accuracy.

💼 Career

Data scientists and engineers must understand data types to optimize performance and storage when working with large datasets.

1

Create a NumPy array with float16 type

Import NumPy as np. Create a NumPy array called temps_float16 with values [36.6, 37.1, 36.8] and set its data type to np.float16.

NumPy

import numpy as np
# Create temps_float16 array with values [36.6, 37.1, 36.8] and dtype np.float16
# Your code here

Need a hint?

Use np.array() with the dtype parameter set to np.float16.

2

Create a NumPy array with float32 type

Create a NumPy array called temps_float32 with values [36.6, 37.1, 36.8] and set its data type to np.float32.

NumPy

import numpy as np

temps_float16 = np.array([36.6, 37.1, 36.8], dtype=np.float16)
# Create temps_float32 array with values [36.6, 37.1, 36.8] and dtype np.float32
# Your code here

Need a hint?

Use np.array() with the dtype parameter set to np.float32.

3

Create a NumPy array with float64 type

Create a NumPy array called temps_float64 with values [36.6, 37.1, 36.8] and set its data type to np.float64.

NumPy

import numpy as np

temps_float16 = np.array([36.6, 37.1, 36.8], dtype=np.float16)
temps_float32 = np.array([36.6, 37.1, 36.8], dtype=np.float32)
# Create temps_float64 array with values [36.6, 37.1, 36.8] and dtype np.float64
# Your code here

Need a hint?

Use np.array() with the dtype parameter set to np.float64.

4

Print the arrays and their data types

Print the arrays temps_float16, temps_float32, and temps_float64. Then print their data types using the .dtype attribute.

NumPy

import numpy as np

temps_float16 = np.array([36.6, 37.1, 36.8], dtype=np.float16)
temps_float32 = np.array([36.6, 37.1, 36.8], dtype=np.float32)
temps_float64 = np.array([36.6, 37.1, 36.8], dtype=np.float64)
# Print the arrays and their data types
# Your code here

Need a hint?

Use print() to display each array and then print its .dtype.