Challenge: Choosing Optimal Data Structures
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Your task is to choose the most appropriate data structure (list, ndarray, set, or tuple) for each of the different scenarios below. Based on the nature of the data and the specific requirements for each collection, fill in the blanks (___) to create the correct data structures. Make sure to use the appropriate brackets for each structure, and if creating a NumPy array, initialize it based on a list.
- For
monthly_sales, the data is numerical, and you will need to perform frequent calculations on it. user_ids, each ID should be unique, and you will need to frequently check if a certain ID is present.productshould be a fixed (unchangeable) record.tasksshould be an ordered collection and allow for frequent additions and removals.
Solución
import numpy as np
# Store monthly sales data (numerical data requiring calculations)
monthly_sales = np.array([320, 450, 500, 480, 350, 610, 720, 640, 510, 470, 420, 580])
# Keep track of unique user IDs who logged in during a day
user_ids = {1023, 2045, 1098, 2045, 3098, 1023}
# Define a product record
product = (2134, 'Wireless Mouse')
# Store an ordered collection of tasks where new tasks can be added or removed frequently
tasks = ['Send report', 'Review code', 'Update website', 'Fix bug']
print(type(monthly_sales))
print(type(user_ids))
print(type(product))
print(type(tasks))
¿Todo estuvo claro?
¡Gracias por tus comentarios!
Sección 2. Capítulo 3
import numpy as np
# Store monthly sales data (numerical data requiring calculations)
monthly_sales = ___320, 450, 500, 480, 350, 610, 720, 640, 510, 470, 420, 580
# Keep track of unique user IDs who logged in during a day
user_ids = ___1023, 2045, 1098, 2045, 3098, 1023
# Define a product record
product = ___2134, 'Wireless Mouse'
# Store an ordered collection of tasks where new tasks can be added or removed frequently
tasks = ___'Send report', 'Review code', 'Update website', 'Fix bug'
print(type(monthly_sales))
print(type(user_ids))
print(type(product))
print(type(tasks))
