Suddividere e impilare in 2D

Le competenze di suddivisione e impilamento non servono solo con array RGB 3D: sono ottime per creare sottoinsiemi e organizzare dati di qualsiasi tipo e dimensione!

Ora farai un tuffo nel passato per riorganizzare l'array monthly_sales come un array 3D. Ricorda che la prima dimensione di monthly_sales rappresenta le righe con le vendite di un singolo mese per tre settori, mentre la seconda dimensione rappresenta le colonne con i dati mensili di vendita per un singolo settore.

Il tuo compito è suddividere questi dati in dati di vendita trimestrali e impilare i dati trimestrali in modo che la nuova terza dimensione rappresenti i quattro array 2D delle vendite trimestrali. numpy è caricato come np e l'array monthly_sales è disponibile.

Questo esercizio fa parte del corso

Introduzione a NumPy

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# Split monthly_sales into quarterly data
q1_sales, q2_sales, q3_sales, q4_sales = ____
print(q1_sales)

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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: Introducing arrays Exercise 2: Your first NumPy array 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: Salvare e caricare array Exercise 2: Caricare file .npy Exercise 3: Chiedere aiuto Exercise 4: Aggiorna e salva Exercise 5: Acrobazie con gli array Exercise 6: Aumentare Monet Exercise 7: Trasponi il tuo capolavoro Exercise 8: Stacking e splitting Exercise 9: Suddividere e impilare in 2D

Esercizio in corso

Exercise 10: Suddividere i dati RGB Exercise 11: Impilare i dati RGB Exercise 12: Complimenti!