Pembuatan array 3D

Dalam pelajaran pertama, Anda membuat sudoku_game, sebuah array NumPy dua dimensi. Misalkan Anda memiliki ratusan array permainan sudoku, dan Anda ingin menyimpan solusinya, sudoku_solution, sebagai bagian dari array yang sama dengan permainan yang sesuai agar data sudoku Anda lebih terorganisasi. Anda dapat melakukannya dengan menumpuk dua array 2D tersebut satu di atas yang lain untuk membuat array 3D.

numpy sudah dimuat sebagai np, dan array sudoku_game serta sudoku_solution sudah tersedia.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

Petunjuk latihan

Buat array 3D bernama game_and_solution dengan menumpuk dua array 2D, sudoku_game dan sudoku_solution, satu di atas yang lain; dalam array akhir, sudoku_game harus muncul sebelum sudoku_solution.
Cetak game_and_solution.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create the game_and_solution 3D array
game_and_solution = ____

# Print game_and_solution
print(____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar NumPy

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus 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: Mengenal array Exercise 2: Array NumPy pertama Anda Exercise 3: Membuat array dari awal Exercise 4: Array rentang Exercise 5: Dimensi array Exercise 6: Pembuatan array 3D

Latihan Saat Ini

Exercise 7: Dimensi keempat Exercise 8: Meratakan dan mengubah bentuk Exercise 9: Tipe data NumPy Exercise 10: Argumen dtype Exercise 11: Menantisipasi tipe data Exercise 12: Gim sudoku yang lebih kecil

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!