When to use NumPy over Pandas - Mini Project: Build & Apply

When to use NumPy over Pandas

📖 Scenario: You work as a data analyst in a company that collects sales data. You have two tools to handle data: NumPy and Pandas. You want to learn when it is better to use NumPy instead of Pandas for your calculations.

🎯 Goal: Build a small program that creates sales data, sets a threshold, uses NumPy to filter data faster, and prints the filtered results.

📋 What You'll Learn

Create a NumPy array with sales numbers

Create a threshold variable to filter sales

Use NumPy to select sales above the threshold

Print the filtered sales array

💡 Why This Matters

🌍 Real World

Data analysts often choose NumPy for fast number crunching and Pandas for data tables with labels.

💼 Career

Knowing when to use NumPy helps you write faster, simpler code for numerical tasks in data science jobs.

1

Create a NumPy array with sales data

Import NumPy as np and create a NumPy array called sales with these exact values: 100, 250, 300, 150, 400.

Pandas

import numpy as np
# Create a NumPy array called sales with values 100, 250, 300, 150, 400
# Your code here

Need a hint?

Use np.array([...]) to create the array.

2

Set a sales threshold

Create a variable called threshold and set it to 200.

Pandas

import numpy as np
sales = np.array([100, 250, 300, 150, 400])
# Create a variable threshold and set it to 200
# Your code here

Need a hint?

Just assign the number 200 to the variable threshold.

3

Filter sales above the threshold using NumPy

Create a new NumPy array called high_sales that contains only the values from sales that are greater than threshold. Use NumPy boolean indexing.

Pandas

import numpy as np
sales = np.array([100, 250, 300, 150, 400])
threshold = 200
# Create high_sales with sales values greater than threshold
# Your code here

Need a hint?

Use sales[sales > threshold] to filter the array.

4

Print the filtered sales

Print the high_sales array to show the sales values above the threshold.

Pandas

import numpy as np
sales = np.array([100, 250, 300, 150, 400])
threshold = 200
high_sales = sales[sales > threshold]
# Print high_sales
# Your code here

Need a hint?

Use print(high_sales) to display the filtered array.