Your first NumPy array

Once you're comfortable with NumPy, you'll find yourself converting Python lists into NumPy arrays all the time for increased speed and access to NumPy's excellent array methods.

sudoku_list is a Python list containing a sudoku game:

[[0, 0, 4, 3, 0, 0, 2, 0, 9],
 [0, 0, 5, 0, 0, 9, 0, 0, 1],
 [0, 7, 0, 0, 6, 0, 0, 4, 3],
 [0, 0, 6, 0, 0, 2, 0, 8, 7],
 [1, 9, 0, 0, 0, 7, 4, 0, 0],
 [0, 5, 0, 0, 8, 3, 0, 0, 0],
 [6, 0, 0, 0, 0, 0, 1, 0, 5],
 [0, 0, 3, 5, 0, 8, 6, 9, 0],
 [0, 4, 2, 9, 1, 0, 3, 0, 0]]

You're going to change sudoku_list into a NumPy array so you can practice with it in later lessons, for example by creating a 4D array of sudoku games along with their solutions!

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Introduction to NumPy

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Oefeninstructies

Import NumPy using its generally accepted alias.
Convert sudoku_list into a NumPy array called sudoku_array.
Print the class type() of sudoku_array to check that your code has worked properly.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import NumPy
import ____

# Convert sudoku_list into an array
sudoku_array = ____

# Print the type of sudoku_array 
print(____)

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Deze oefening maakt deel uit van de cursus

Introduction to 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

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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: 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!