Memory usage analysis in Pandas - Mini Project: Build & Apply

Memory usage analysis

📖 Scenario: You work as a data analyst. You have a small dataset about sales of different products. You want to understand how much memory this data uses in your computer. This helps you learn about data size and efficiency.

🎯 Goal: You will create a pandas DataFrame with sales data, then check its memory usage. Finally, you will print the memory usage in bytes.

📋 What You'll Learn

Create a pandas DataFrame with exact data given

Use pandas method to find memory usage of the DataFrame

Print the memory usage value

💡 Why This Matters

🌍 Real World

Data scientists often check memory usage to optimize data storage and processing speed, especially with large datasets.

💼 Career

Understanding memory usage helps data analysts and scientists write efficient code and manage resources well in real projects.

1

Create the sales DataFrame

Import pandas as pd. Create a DataFrame called sales with these exact columns and values: 'Product' with ['Pen', 'Notebook', 'Eraser'], 'Units' with [10, 5, 8], and 'Price' with [1.5, 3.0, 0.5].

Pandas

# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Set up memory usage variable

Create a variable called mem_usage and set it to the memory usage of the sales DataFrame by using the memory_usage() method with deep=True and then summing the result.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Units': [10, 5, 8],
    'Price': [1.5, 3.0, 0.5]
})
# Your code here

Need a hint?

Use sales.memory_usage(deep=True) to get memory per column, then .sum() to add them.

3

Calculate total memory usage

Use the variable mem_usage to hold the total memory usage in bytes of the sales DataFrame by summing the memory usage of all columns with deep=True.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Units': [10, 5, 8],
    'Price': [1.5, 3.0, 0.5]
})
mem_usage = sales.memory_usage(deep=True).sum()
# Your code here

Need a hint?

This step repeats the calculation to reinforce the concept. Make sure you use deep=True and .sum().

4

Print the memory usage

Print the value of mem_usage to show the total memory usage in bytes of the sales DataFrame.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Units': [10, 5, 8],
    'Price': [1.5, 3.0, 0.5]
})
mem_usage = sales.memory_usage(deep=True).sum()
# Your code here

Need a hint?

Use print(mem_usage) to display the memory usage.