Dividir y apilar en 2D

Las habilidades de dividir y apilar no solo sirven con arrays RGB 3D: son excelentes para submuestrear y organizar datos de cualquier tipo y dimensión.

Ahora daremos un breve viaje al pasado para reorganizar el array monthly_sales como un array 3D. Recuerda que la primera dimensión de monthly_sales son filas con las ventas de un único mes en tres industrias, y la segunda dimensión son columnas con los datos de ventas mensuales de una sola industria.

Tu tarea es dividir estos datos en ventas trimestrales y apilar las ventas trimestrales de modo que la nueva tercera dimensión represente los cuatro arrays 2D de ventas trimestrales. numpy está cargado como np, y el array monthly_sales está disponible.

Este ejercicio forma parte del curso

Introducción a NumPy

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# Split monthly_sales into quarterly data
q1_sales, q2_sales, q3_sales, q4_sales = ____
print(q1_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: 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 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: Guardar y cargar arrays Exercise 2: Cargar archivos .npy Exercise 3: Obtener ayuda Exercise 4: Actualizar y guardar Exercise 5: Acróbatas de arrays Exercise 6: Aumentar a Monet Exercise 7: Transponer tu obra maestra Exercise 8: Apilado y división Exercise 9: Dividir y apilar en 2D

Ejercicio actual

Exercise 10: Dividir datos RGB Exercise 11: Apilar datos RGB Exercise 12: ¡Enhorabuena!