Why memory management matters in NumPy - See It in Action

Why memory management matters

📖 Scenario: Imagine you are working with a large collection of numbers representing daily temperatures for a city over several years. You want to analyze this data efficiently without using too much computer memory.

🎯 Goal: You will create a NumPy array with temperature data, set a memory limit, filter the data based on this limit, and then display the filtered results. This will help you understand why managing memory is important when working with big data.

📋 What You'll Learn

Create a NumPy array with exact temperature values

Define a memory limit variable

Filter the array to keep only temperatures below the memory limit

Print the filtered array

💡 Why This Matters

🌍 Real World

In real life, data scientists often work with large datasets. Managing memory helps computers run faster and prevents crashes.

💼 Career

Understanding memory management is important for data scientists to write efficient code that handles big data smoothly.

1

Create the temperature data array

Import NumPy as np and create a NumPy array called temperatures with these exact values: [23, 27, 31, 29, 35, 40, 22, 20].

NumPy

# Import numpy and create the temperatures array
# Your code here

Need a hint?

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

2

Set a memory limit for filtering

Create a variable called memory_limit and set it to 30. This will be the threshold to filter temperatures.

NumPy

import numpy as np

temperatures = np.array([23, 27, 31, 29, 35, 40, 22, 20])
# Set memory_limit to 30
# Your code here

Need a hint?

Just assign the number 30 to the variable memory_limit.

3

Filter temperatures below the memory limit

Create a new NumPy array called filtered_temps that contains only the values from temperatures which are less than memory_limit using a boolean mask.

NumPy

import numpy as np

temperatures = np.array([23, 27, 31, 29, 35, 40, 22, 20])
memory_limit = 30
# Filter temperatures less than memory_limit
# Your code here

Need a hint?

Use temperatures < memory_limit inside the brackets to filter.

4

Display the filtered temperatures

Print the filtered_temps array to see the temperatures below the memory limit.

NumPy

import numpy as np

temperatures = np.array([23, 27, 31, 29, 35, 40, 22, 20])
memory_limit = 30

filtered_temps = temperatures[temperatures < memory_limit]
# Print the filtered_temps array
# Your code here

Need a hint?

Use print(filtered_temps) to show the result.