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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.

Este ejercicio forma parte del curso

Python for MATLAB Users

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Instrucciones del ejercicio

  • Create a DataFrame animals from the list of dictionaries animals.
  • 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.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# 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)))
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