Un array di intervallo

np.arange() è particolarmente utile per creare grafici. Il tuo compito è creare uno scatter plot con i valori di doubling_array sull'asse y.

doubling_array = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]

Ricorda che puoi creare uno scatter plot con il seguente codice:

plt.scatter(x_values, y_values)
plt.show()

Con doubling_array sull'asse y, ora ti servono i valori per l'asse x, che puoi creare con np.arange()!

numpy è già caricato come np e matplotlib.pyplot è importato come plt.

Questo esercizio fa parte del corso

Introduzione a NumPy

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Istruzioni dell'esercizio

Usando np.arange(), crea un array 1D chiamato one_to_ten che contenga tutti gli interi da uno a dieci (inclusi).
Crea uno scatter plot con doubling_array come valori di y e one_to_ten come valori di x.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create an array of integers from one to ten
one_to_ten = ____

# Create your scatterplot
____
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione 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: Introduzione agli array Exercise 2: Il tuo primo array NumPy Exercise 3: Creare array da zero Exercise 4: Un array di intervallo

Esercizio in corso

Exercise 5: Dimensionalità degli array Exercise 6: Creazione di un array 3D Exercise 7: La quarta dimensione Exercise 8: Appiattire e rimodellare Exercise 9: Tipi di dati in NumPy Exercise 10: L'argomento dtype Exercise 11: Prevedere i tipi di dato Exercise 12: Un sudoku più leggero

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!