Proyección de ventas

Quieres poder planificar las operaciones del año que viene proyectando cuáles serán las ventas, y has reunido multiplicadores específicos para cada mes y sector. Estos multiplicadores están guardados en un array llamado monthly_industry_multipliers. Por ejemplo, el multiplicador en monthly_industry_multipliers[0, 0] de 0.98 significa que se proyecta que el sector de licorerías tenga el 98% de las ventas de este enero en el enero del próximo año.

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 está cargado como np, y los arrays monthly_sales y monthly_industry_multipliers están disponibles. Las columnas de monthly_sales, en orden, se refieren a ventas de licorerías, restaurantes y grandes almacenes.

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Introducción 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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Este ejercicio forma parte del curso

Introducción 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: Resumir datos Exercise 2: Totales de ventas Exercise 3: Representar promedios Exercise 4: Ventas acumuladas Exercise 5: Operaciones vectorizadas Exercise 6: Cálculos de impuestos Exercise 7: Proyección de ventas

Ejercicio actual

Exercise 8: Vectorizar .upper()Exercise 9: Difusión (Broadcasting)Exercise 10: ¿Se puede hacer broadcasting o no?Exercise 11: Broadcasting por columnas Exercise 12: Difundir a lo largo de las filas

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!