Ordenar árboles

A veces es más fácil entender los datos cuando están ordenados según el valor que más te interesa. Tu nueva tarea de investigación es crear un array que contenga los diámetros de tronco del censo de árboles de la ciudad de Nueva York, ordenados de menor a mayor.

numpy está cargado como np, y el array 2D tree_census está disponible.

Este ejercicio forma parte del curso

Introducción a NumPy

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Instrucciones del ejercicio

Crea un array llamado sorted_trunk_diameters que seleccione solo la columna de diámetro de tronco de tree_census y la ordene de forma que los diámetros más pequeños estén al principio del array y los más grandes al final.

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# Extract trunk diameters information and sort from smallest to largest
sorted_trunk_diameters = ____
print(sorted_trunk_diameters)

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Este ejercicio forma parte del curso

Introducción 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: Indexación y segmentación de arrays Exercise 2: Corte e indexación de árboles Exercise 3: Avanzando a 2D Exercise 4: Ordenar árboles

Ejercicio actual

Exercise 5: Filtrar arrays Exercise 6: Filtrado con máscaras Exercise 7: Indexación avanzada vs. np.where()Exercise 8: Crear arrays a partir de condiciones Exercise 9: Añadir y eliminar datos Exercise 10: ¿Compatibles o no?Exercise 11: Añadir filas Exercise 12: Añadir columnas Exercise 13: Eliminar con 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!