Boolean indexing for quick stats
Let's return to the animals
dataset, which is loaded as a list of dictionaries. You're going to use all you've learned and transform this data into a useable DataFrame, filter the data using Boolean indexing, and then perform some numpy
magic to find out some interesting animal facts.
This exercise is part of the course
Python for MATLAB Users
Exercise instructions
- Create a DataFrame
animals
from the list of dictionariesanimals
. - Create a Boolean index
mammals
by finding records where the "Class" is "Mammalia". - Create a Boolean index
birds
by finding records where the "Class" is "Aves". - Use
numpy
to find the mean of the "Litter/Clutch size" column for mammals and birds.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a DataFrame from animals
animals = pd.____(animals)
# Create Boolean indices for mammals and birds
mammals = animals['Class']=='____'
birds = animals['Class']=='____'
# Use numpy and the Boolean indices to determine mean Litter/Clutch size
litter = np.____(animals[mammals]['Litter/Clutch size'])
clutch = np.____(animals[____]['Litter/Clutch size'])
# Print the average Litter/Clutch size of each class
print('Mammals have an average of {} offspring in each litter.'.format(round(litter, 2)))
print('The average clutch size in a single brood is {} eggs.'.format(round(clutch, 2)))