2D splits en stacks

Splits en stacks zijn niet alleen handig bij 3D RGB-arrays: ze zijn ideaal om gegevens van elk type en elke dimensie te subsetten en te organiseren!

Je gaat nu even terug in de tijd om de monthly_sales-array te herstructureren als een 3D-array. Denk eraan dat de eerste dimensie van monthly_sales rijen zijn met de verkopen van één maand over drie sectoren, en de tweede dimensie kolommen met maandelijkse verkoopgegevens voor één sector.

Jouw taak is om deze gegevens op te splitsen in kwartaalgegevens en de kwartaalgegevens te stacken zodat de nieuwe derde dimensie de vier 2D-arrays met kwartaalverkopen voorstelt. numpy is geladen als np, en de monthly_sales-array is beschikbaar.

Deze oefening maakt deel uit van de cursus

Introductie tot 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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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: 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: Arrays opslaan en laden Exercise 2: NPY-bestanden laden Exercise 3: Hulp vragen Exercise 4: Bijwerken en opslaan Exercise 5: Array-acrobatiek Exercise 6: Monet uitbreiden Exercise 7: Je meesterwerk transponeren Exercise 8: Stappelen en splitsen Exercise 9: 2D splits en stacks

Huidige oefening

Exercise 10: RGB-gegevens splitsen Exercise 11: RGB-gegevens stapelen Exercise 12: Gefeliciteerd!