Proiezione delle vendite

Vorresti pianificare le operazioni del prossimo anno stimando le vendite future, e hai raccolto dei moltiplicatori specifici per ogni mese e settore. Questi moltiplicatori sono salvati in un array chiamato monthly_industry_multipliers. Per esempio, il moltiplicatore in monthly_industry_multipliers[0, 0] pari a 0.98 significa che per il settore dei negozi di alcolici si prevede il 98% delle vendite di questo gennaio nel gennaio del prossimo anno.

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 è già caricato come np, e gli array monthly_sales e monthly_industry_multipliers sono disponibili. Le colonne di monthly_sales, in ordine, si riferiscono alle vendite di negozi di alcolici, ristoranti e grandi magazzini.

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Introduzione a 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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Questo esercizio fa parte del corso

Introduzione a 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: Riassumere i dati Exercise 2: Totali delle vendite Exercise 3: Rappresentare le medie Exercise 4: Vendite cumulative Exercise 5: Operazioni vettorializzate Exercise 6: Calcoli delle tasse Exercise 7: Proiezione delle vendite

Esercizio in corso

Exercise 8: Vettorizzare .upper()Exercise 9: Broadcasting Exercise 10: Broadcasting o no?Exercise 11: Broadcasting tra colonne Exercise 12: Broadcasting per righe

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!