La cuarta dimensión

Imprimir arrays es una buena forma de comprobar la salida del código para arrays pequeños como sudoku_game_and_solution, pero se vuelve poco práctico con arrays más grandes o de mayor dimensión. Otra comprobación importante es mirar el .shape del array.

Ahora vas a crear un array 4D que contiene dos sudokus y sus soluciones. numpy está cargado como np. El array 3D game_and_solution que creaste en el ejemplo anterior está disponible, junto con new_sudoku_game y new_sudoku_solution.

Este ejercicio forma parte del curso

Introducción a NumPy

Instrucciones del ejercicio

Crea otro array 3D llamado new_game_and_solution con un par diferente de juego 2D y solución 2D: new_sudoku_game y new_sudoku_solution. new_sudoku_game debe aparecer antes que new_sudoku_solution.
Crea un array 4D llamado games_and_solutions formando un array con los dos arrays 3D: game_and_solution y new_game_and_solution, en ese orden.
Imprime la forma (shape) de games_and_solutions.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Create a second 3D array of another game and its solution 
new_game_and_solution = ____

# Create a 4D array of both game and solution 3D arrays
games_and_solutions = ____

# Print the shape of your 4D array
print(____)

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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: Presentación de los arrays Exercise 2: Tu primer array de NumPy Exercise 3: Crear arrays desde cero Exercise 4: Un array de rango Exercise 5: Dimensionalidad de arrays Exercise 6: Creación de un array 3D Exercise 7: La cuarta dimensión

Ejercicio actual

Exercise 8: Aplanar y cambiar la forma Exercise 9: Tipos de datos de NumPy Exercise 10: El argumento dtype Exercise 11: Anticipar tipos de datos Exercise 12: Un sudoku más pequeño

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: 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!