Broadcasten over rijen

In de vorige set oefeningen gebruikte je monthly_industry_multipliers om verkoopvoorspellingen te maken. Ter herinnering: monthly_industry_multipliers ziet er zo uit:

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]])

Stel dat je je niet prettig voelt bij zulke specifieke schattingen. In plaats daarvan wil je monthly_industry_multipliers gebruiken om één gemiddelde multiplier per sector te vinden. Daarna gebruik je die multiplier om de verkoop voor volgend jaar te projecteren.

numpy is voor je geïmporteerd als np, en de arrays monthly_sales en monthly_industry_multipliers zijn beschikbaar. De kolommen van monthly_sales verwijzen in deze volgorde naar verkopen van slijterijen, restaurants en warenhuizen.

Deze oefening maakt deel uit van de cursus

Introductie tot NumPy

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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

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Deze oefening maakt deel uit van de cursus

Introductie tot 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: Gegevens samenvatten Exercise 2: Totale verkoop Exercise 3: Gemiddelden plotten Exercise 4: Cumulatieve verkoop Exercise 5: Gevectoriseerde bewerkingen Exercise 6: Belastingberekeningen Exercise 7: Verkoop prognosticeren Exercise 8: Vectoriseren van .upper()Exercise 9: Broadcasting Exercise 10: Broadcastable of niet?Exercise 11: Broadcasten over kolommen Exercise 12: Broadcasten over rijen

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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!