Filter observations from a DataFrame based on boolean arrays: Part 2

# Create arrays
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])

# my_house greater than 18.5 or smaller than 10
print(np.logical_or(my_house >18.5, my_house < 10))

# Both my_house and your_house smaller than 11
print(np.logical_and(my_house < 11, your_house < 11))

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Extract drives_right column as Series: dr
cars['drives_right']
dr = cars['drives_right']

# Use dr to subset cars: sel
sel = cars[dr]

# Print sel
print(sel)

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Create car_maniac: observations that have a cars_per_cap over 500
cpc = cars['cars_per_cap']
many_cars = cpc > 500
car_maniac = cars[many_cars]



# Print car_maniac
print(car_maniac)

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Import numpy, you'll need this
import numpy as np

# Create medium: observations with cars_per_cap between 100 and 500
cpc = cars['cars_per_cap']
between = np.logical_and(cpc > 100, cpc < 500)
medium = cars[between]

# Print medium
print(medium)

# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Change for loop to use enumerate() and update print()
for index, area in enumerate(areas) :
    print("room " + str(index) + ": " + str(area))

# house list of lists
house = [["hallway", 11.25], 
         ["kitchen", 18.0], 
         ["living room", 20.0], 
         ["bedroom", 10.75], 
         ["bathroom", 9.50]]
         
# Build a for loop from scratch
for x in house :
    #x[0] to access name of room
    #x[1] to access area in sqm
    print('the ' + x[0] + " is " + str(x[1]) + " sqm")