Slicen en indexeren van bomen

Stel, je bent onderzoeker en werkt met gegevens uit de boomtelling van New York City. Elke rij van de 2D-array tree_census bevat informatie over een andere boom: het boom-ID, blok-ID, stamdiameter en stronklengte, in die volgorde. Levend(e) bomen hebben geen stronklengte, wat verklaart waarom er zoveel nullen in die kolom staan. De kolomvolgorde is belangrijk, want NumPy heeft geen kolomnamen! De eerste en laatste drie rijen van tree_census staan hieronder.

array([[     3, 501451,     24,      0],
       [     4, 501451,     20,      0],
       [     7, 501911,      3,      0],
       ...,
       [  1198, 227387,     11,      0],
       [  1199, 227387,     11,      0],
       [  1210, 227386,      6,      0]])

In deze oefening werk je specifiek met de tweede kolom, die de blok-ID’s weergeeft: voor je onderzoek moet je bepaalde stadsblokken selecteren voor verdere analyse met NumPy-slicing en -indexering. numpy is geladen als np, en de 2D-array tree_census is beschikbaar.

Deze oefening maakt deel uit van de cursus

Introductie tot NumPy

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# Select all rows of block ID data from the second column
block_ids = ____

# Print the first five block_ids
print(____)

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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: Indexeren en slicen van arrays Exercise 2: Slicen en indexeren van bomen

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Exercise 3: Stap voor stap in 2D Exercise 4: Bomen sorteren Exercise 5: Arrays filteren Exercise 6: Filteren met maskers Exercise 7: Fancy indexing vs. np.where()Exercise 8: Arrays maken op basis van voorwaarden Exercise 9: Gegevens toevoegen en verwijderen Exercise 10: Compatibel of niet?Exercise 11: Rijen toevoegen Exercise 12: Kolommen toevoegen Exercise 13: Verwijderen met 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!