np.broadcast_to() for explicit broadcasting in NumPy - Mini Project: Build & Apply

Using np.broadcast_to() for Explicit Broadcasting

📖 Scenario: Imagine you are working with sales data for a small store. You have the sales numbers for one product over a week, but you want to compare it with a target sales array that has the same shape as the weekly data for easy calculations.

🎯 Goal: You will create a numpy array for weekly sales, then use np.broadcast_to() to expand a single target sales value to match the weekly sales shape. Finally, you will print the broadcasted target sales array.

📋 What You'll Learn

Create a numpy array called weekly_sales with the exact values: [10, 15, 20, 25, 30, 35, 40]

Create a variable called target_sales with the exact value 20

Use np.broadcast_to() to create a new array called broadcasted_target that has the same shape as weekly_sales but with all values equal to target_sales

Print the broadcasted_target array

💡 Why This Matters

🌍 Real World

Broadcasting helps when you want to compare or combine data of different shapes without manually repeating values. For example, comparing daily sales to a fixed target.

💼 Career

Data scientists often use broadcasting to efficiently perform operations on arrays of different shapes, saving time and memory.

1

Create the weekly sales data array

Create a numpy array called weekly_sales with these exact values: [10, 15, 20, 25, 30, 35, 40]

NumPy

import numpy as np
# Create the numpy array weekly_sales with the given values
# Your code here

Need a hint?

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

2

Set the target sales value

Create a variable called target_sales and set it to the integer value 20

NumPy

import numpy as np
weekly_sales = np.array([10, 15, 20, 25, 30, 35, 40])
# Create the variable target_sales with value 20
# Your code here

Need a hint?

Just assign the number 20 to the variable target_sales.

3

Broadcast the target sales value to match weekly sales shape

Use np.broadcast_to() to create a new array called broadcasted_target that has the same shape as weekly_sales but with all values equal to target_sales

NumPy

import numpy as np
weekly_sales = np.array([10, 15, 20, 25, 30, 35, 40])
target_sales = 20
# Use np.broadcast_to() to create broadcasted_target with the shape of weekly_sales
# Your code here

Need a hint?

Use np.broadcast_to(target_sales, weekly_sales.shape) to expand the single value to the shape of weekly_sales.

4

Print the broadcasted target sales array

Print the broadcasted_target array to see the result

NumPy

import numpy as np
weekly_sales = np.array([10, 15, 20, 25, 30, 35, 40])
target_sales = 20
broadcasted_target = np.broadcast_to(target_sales, weekly_sales.shape)
# Print the broadcasted_target array
# Your code here

Need a hint?

Use print(broadcasted_target) to display the array.