3D-array maken

In de eerste les heb je een tweedimensionale NumPy-array sudoku_game gemaakt. Stel dat je honderden sudoku-arrays hebt, en je wilt de oplossing voor deze, sudoku_solution, opslaan in dezelfde array als het bijbehorende spel om je sudokugegevens beter te organiseren. Dat kun je doen door de twee 2D-arrays op elkaar te stapelen om een 3D-array te maken.

numpy is geladen als np, en de arrays sudoku_game en sudoku_solution zijn beschikbaar.

Deze oefening maakt deel uit van de cursus

Introductie tot NumPy

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Oefeninstructies

Maak een 3D-array game_and_solution door de twee 2D-arrays, sudoku_game en sudoku_solution, op elkaar te stapelen; in de uiteindelijke array moet sudoku_game vóór sudoku_solution staan.
Print game_and_solution.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create the game_and_solution 3D array
game_and_solution = ____

# Print game_and_solution
print(____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot 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: Kennismaken met arrays Exercise 2: Je eerste NumPy-array Exercise 3: Arrays helemaal zelf maken Exercise 4: Een reeks-array Exercise 5: Array-dimensionaliteit Exercise 6: 3D-array maken

Huidige oefening

Exercise 7: De vierde dimensie Exercise 8: Afvlakken en herstructureren Exercise 9: NumPy-datatypen Exercise 10: Het argument dtype Exercise 11: Datatypes inschatten Exercise 12: Een kleiner sudokuspel

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!