Broadcasting across rows

In the last set of exercises, you used monthly_industry_multipliers, to create sales predictions. Recall that monthly_industry_multipliers looks like this:

array([[0.98, 1.02, 1.  ],
       [1.00, 1.01, 0.97],
       [1.06, 1.03, 0.98],
       [1.08, 1.01, 0.98],
       [1.08, 0.98, 0.98],
       [1.1 , 0.99, 0.99],
       [1.12, 1.01, 1.  ],
       [1.1 , 1.02, 1.  ],
       [1.11, 1.01, 1.01],
       [1.08, 0.99, 0.97],
       [1.09, 1.  , 1.02],
       [1.13, 1.03, 1.02]])

Assume you're not comfortable being so specific with your estimates. Rather, you'd like to use monthly_industry_multipliers to find a single average multiplier for each industry. Then you'll use that multiplier to project next year's sales.

numpy is loaded for you as np, and the monthly_sales and monthly_industry_multipliers arrays are available. The monthly_sales columns in order refer to liquor store, restaurant, and department store sales.

This exercise is part of the course

Introduction to NumPy

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Find the mean sales projection multiplier for each industry
mean_multipliers = ____
print(mean_multipliers)

Edit and Run Code

This exercise is part of the course

Introduction to NumPy

BeginnerSkill Level

4.8+

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Leverage NumPy’s speedy vectorized operations to gather summary insights on sales data for American liquor stores, restaurants, and department stores. Vectorize Python functions for use in your NumPy code. Finally, use broadcasting logic to perform mathematical operations between arrays of different sizes.

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