Monitoring memory usage in NumPy - Mini Project: Build & Apply

Monitoring memory usage

📖 Scenario: You are working on a data science project that uses numpy arrays. You want to monitor how much memory your arrays use to optimize your program's performance.

🎯 Goal: Build a simple Python script that creates a numpy array, sets a threshold for memory usage, checks the array's memory usage, and prints a message if the usage exceeds the threshold.

📋 What You'll Learn

Create a numpy array with specific values

Define a memory usage threshold variable

Calculate the memory usage of the array in bytes

Compare the memory usage with the threshold

Print a message showing the memory usage and if it exceeds the threshold

💡 Why This Matters

🌍 Real World

Monitoring memory usage helps keep programs efficient and prevents crashes due to running out of memory.

💼 Career

DevOps engineers and data scientists often monitor memory to optimize applications and infrastructure.

1

Create a numpy array

Import numpy as np and create a numpy array called data with these exact values: [10, 20, 30, 40, 50].

NumPy

# Your code here

Need a hint?

Use np.array() to create the array with the given list of numbers.

2

Set memory usage threshold

Create a variable called memory_threshold and set it to 100 to represent 100 bytes.

NumPy

import numpy as np

data = np.array([10, 20, 30, 40, 50])
# Your code here

Need a hint?

Just assign the number 100 to the variable memory_threshold.

3

Calculate memory usage of the array

Create a variable called memory_used and set it to the number of bytes used by the data array using the nbytes attribute.

NumPy

import numpy as np

data = np.array([10, 20, 30, 40, 50])
memory_threshold = 100
# Your code here

Need a hint?

Use data.nbytes to get the memory size in bytes.

4

Print memory usage and check threshold

Write a print statement that shows the text "Memory used:" followed by the value of memory_used. Then write an if statement that prints "Memory usage exceeds threshold!" if memory_used is greater than memory_threshold.

NumPy

import numpy as np

data = np.array([10, 20, 30, 40, 50])
memory_threshold = 100
memory_used = data.nbytes
# Your code here

Need a hint?

Use print("Memory used:", memory_used) and an if statement to compare memory_used and memory_threshold.