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food-consumption-exercise
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/
food-consumption-exercise
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Import numpy with the alias np.
Create two DataFrames: one that holds the rows of food_consumption for 'Belgium' and another that holds rows for 'USA'. Call these be_consumption and usa_consumption.
Calculate the mean and median of kilograms of food consumed per person per year for both countries.
PART 1
Solution:-------------------------------------------------------------------------------------------------
# Import numpy with alias np
import numpy as np
# Filter for Belgium
be_consumption = food_consumption[food_consumption['country'] == 'Belgium']
# Filter for USA
usa_consumption = food_consumption[food_consumption['country'] == 'USA']
# Calculate mean and median consumption in Belgium
print(np.mean(be_consumption['consumption']))
print(np.median(be_consumption['consumption']))
# Calculate mean and median consumption in USA
print(np.mean(usa_consumption['consumption']))
print(np.median(usa_consumption['consumption']))
PART 2
Solution:-----------------------------------------------------------------------------------------------
# Import numpy as np
import numpy as np
# Subset for Belgium and USA only
be_and_usa = food_consumption[(food_consumption['country'] == "Belgium") | (food_consumption['country'] == 'USA')]
# Group by country, select consumption column, and compute mean and median
print(be_and_usa.groupby('country')['consumption'].agg([np.mean, np.median]))