Projecting sales

You'd like to be able to plan for next year's operations by projecting what sales will be, and you've gathered multipliers specific to each month and industry. These multipliers are saved in an array called monthly_industry_multipliers. For example, the multiplier at monthly_industry_multipliers[0, 0] of 0.98 means that the liquor store industry is projected to have 98% of this January's sales in January of next year.

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]])

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.

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Introduction to NumPy

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# Create an array of monthly projected sales for all industries
projected_monthly_sales = ____
print(projected_monthly_sales)

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Deze oefening maakt deel uit van de cursus

Introduction to NumPy

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Meet the incredible NumPy array! Learn how to create and change array shapes to suit your needs. Finally, discover NumPy's many data types and how they contribute to speedy array operations.

Exercise 1: Introducing arrays Exercise 2: Your first NumPy array Exercise 3: Creating arrays from scratch Exercise 4: A range array Exercise 5: Array dimensionality Exercise 6: 3D array creation Exercise 7: The fourth dimension Exercise 8: Flattening and reshaping Exercise 9: NumPy data types Exercise 10: The dtype argument Exercise 11: Anticipating data types Exercise 12: A smaller sudoku game

Sharpen your NumPy data wrangling skills by slicing, filtering, and sorting New York City’s tree census data. Create new arrays by pulling data based on conditional statements, and add and remove data along any dimension to suit your purpose. Along the way, you’ll learn the shape and dimension compatibility principles to prepare for super-fast array math.

Exercise 1: Indexing and slicing arrays Exercise 2: Slicing and indexing trees Exercise 3: Stepping into 2D Exercise 4: Sorting trees Exercise 5: Filtering arrays Exercise 6: Filtering with masks Exercise 7: Fancy indexing vs. np.where()Exercise 8: Creating arrays from conditions Exercise 9: Adding and removing data Exercise 10: Compatible or not?Exercise 11: Adding rows Exercise 12: Adding columns Exercise 13: Deleting with np.delete()

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.

Exercise 1: Summarizing data Exercise 2: Sales totals Exercise 3: Plotting averages Exercise 4: Cumulative sales Exercise 5: Vectorized operations Exercise 6: Tax calculations Exercise 7: Projecting sales

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Exercise 8: Vectorizing .upper()Exercise 9: Broadcasting Exercise 10: Broadcastable or not?Exercise 11: Broadcasting across columns Exercise 12: Broadcasting across rows

NumPy meets the art world in this final chapter as we use image data from a Monet masterpiece to explore how you can use to augment image data. You’ll use flipping and transposing functionality to quickly transform our masterpiece. Next, you’ll pull the Monet array apart, make changes, and reconstruct it using array stacking to see the results.

Exercise 1: Saving and loading arrays Exercise 2: Loading .npy files Exercise 3: Getting help Exercise 4: Update and save Exercise 5: Array acrobatics Exercise 6: Augmenting Monet Exercise 7: Transposing your masterpiece Exercise 8: Stacking and splitting Exercise 9: 2D split and stack Exercise 10: Splitting RGB data Exercise 11: Stacking RGB data Exercise 12: Congratulations!