Slicing and indexing trees

Imagine you are a researcher working with data from New York City's tree census. Each row of the tree_census 2D array lists information for a different tree: the tree ID, block ID, trunk diameter, and stump diameter in that order. Living trees do not have stump diameters, which explains why there are so many zeros in that column. Column order is important because NumPy does not have column names! The first and last three rows of tree_census are shown below.

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 this exercise, you'll be working specifically with the second column, representing block IDs: your research requires you to select specific city blocks for further analysis using NumPy slicing and indexing. numpy is loaded as np, and the tree_census 2D array is available.

This exercise is part of the course

Introduction to NumPy

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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

# Print the first five block_ids
print(____)

Edit and Run Code

This exercise is part of the course

Introduction to NumPy

BeginnerSkill Level

4.8+

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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

Current Exercise

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!