Integer types (int8, int16, int32, int64) in NumPy - Mini Project: Build & Apply

Working with Integer Types in NumPy

📖 Scenario: Imagine you are analyzing sensor data from a weather station. The data includes temperature readings that need to be stored efficiently using different integer types.

🎯 Goal: You will create NumPy arrays with specific integer types (int8, int16, int32, and int64) and observe their values and types.

📋 What You'll Learn

Create NumPy arrays with exact integer types: int8, int16, int32, int64

Assign specific values to each array as instructed

Print the arrays and their data types to see the difference

💡 Why This Matters

🌍 Real World

Storing sensor data efficiently is important in many fields like weather monitoring, where memory and speed matter.

💼 Career

Understanding integer types helps in data engineering and scientific computing jobs where data size and precision are critical.

1

Create a NumPy array with int8 type

Import NumPy as np and create a NumPy array called temp_int8 with values [120, -100, 0] and data type np.int8.

NumPy

import numpy as np
# Create a NumPy array called temp_int8 with values [120, -100, 0] and dtype np.int8
# Your code here

Need a hint?

Use np.array() with the dtype parameter set to np.int8.

2

Create a NumPy array with int16 type

Create a NumPy array called temp_int16 with values [32000, -32000, 0] and data type np.int16.

NumPy

import numpy as np
temp_int8 = np.array([120, -100, 0], dtype=np.int8)
# Create a NumPy array called temp_int16 with values [32000, -32000, 0] and dtype np.int16
# Your code here

Need a hint?

Use np.array() with the dtype parameter set to np.int16.

3

Create NumPy arrays with int32 and int64 types

Create two NumPy arrays: temp_int32 with values [2000000000, -2000000000, 0] and data type np.int32, and temp_int64 with values [9000000000000000000, -9000000000000000000, 0] and data type np.int64.

NumPy

import numpy as np
temp_int8 = np.array([120, -100, 0], dtype=np.int8)
temp_int16 = np.array([32000, -32000, 0], dtype=np.int16)
# Create temp_int32 with values [2000000000, -2000000000, 0] and dtype np.int32
# Create temp_int64 with values [9000000000000000000, -9000000000000000000, 0] and dtype np.int64
# Your code here

Need a hint?

Create two arrays using np.array() with dtype set to np.int32 and np.int64 respectively.

4

Print the arrays and their data types

Print each array (temp_int8, temp_int16, temp_int32, temp_int64) and their data types using print() and the .dtype attribute.

NumPy

import numpy as np
temp_int8 = np.array([120, -100, 0], dtype=np.int8)
temp_int16 = np.array([32000, -32000, 0], dtype=np.int16)
temp_int32 = np.array([2000000000, -2000000000, 0], dtype=np.int32)
temp_int64 = np.array([9000000000000000000, -9000000000000000000, 0], dtype=np.int64)
# Print each array and its dtype below
# Your code here

Need a hint?

Use print(array, array.dtype) for each array.