# 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)
# 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)
# 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")
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