Creación de un array 3D

En la primera lección creaste un array de NumPy bidimensional, sudoku_game. Imagina que tienes cientos de arrays de partidas de sudoku y quieres guardar la solución de esta, sudoku_solution, junto con su partida correspondiente para organizar mejor tus datos de sudoku. Puedes conseguirlo apilando los dos arrays 2D uno encima del otro para crear un array 3D.

numpy está cargado como np, y los arrays sudoku_game y sudoku_solution están disponibles.

Este ejercicio forma parte del curso

Introducción a NumPy

Instrucciones del ejercicio

Crea un array 3D llamado game_and_solution apilando los dos arrays 2D, sudoku_game y sudoku_solution, uno encima del otro; en el array final, sudoku_game debe aparecer antes que sudoku_solution.
Imprime game_and_solution.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Create the game_and_solution 3D array
game_and_solution = ____

# Print game_and_solution
print(____)

Editar y ejecutar código

Este ejercicio forma parte del curso

Introducción a NumPy

PrincipianteNivel de habilidad

4.8+

Comienza el curso gratis

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

Ejercicio actual

Exercise 7: La cuarta dimensión 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!