Logic_Control Flow and Filtering.py

# Comparison of booleans
print(True == False)

# Comparison of integers
print(-5*15 != 75)

# Comparison of strings
print("pyscript" == "PyScript")

# Compare a boolean with an integer
print(True == 1)

# Comparison of integers
x = -3 * 6
print(x >= -10)

# Comparison of strings
y = "test"
print("test" <= y)

# Comparison of booleans
print(True > False)

# 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 or equal to 18
print(my_house >= 18)

# my_house less than your_house
print(my_house < your_house)

# Define variables
my_kitchen = 18.0
your_kitchen = 14.0

# my_kitchen bigger than 10 and smaller than 18?
print(my_kitchen>10 and my_kitchen<18)

# my_kitchen smaller than 14 or bigger than 17?
print(my_kitchen<14 or my_kitchen>17)

# Double my_kitchen smaller than triple your_kitchen?
print(my_kitchen*2 < your_kitchen*3)


# 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))

# Define variables
room = "kit"
area = 14.0

# if statement for room
if room == "kit" :
    print("looking around in the kitchen.")

# if statement for area
if (area > 15):
    print("big place!")

# Define variables
room = "bed"
area = 14.0

# if-elif-else construct for room
if room == "kit" :
    print("looking around in the kitchen.")
elif room == "bed":
    print("looking around in the bedroom.")
else :
    print("looking around elsewhere.")

# if-elif-else construct for area
if area > 15 :
    print("big place!")
elif area > 10 :
    print("medium size, nice!")
else :
    print("pretty small.")

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

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

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

# Print sel
print(sel)

# Convert code to a one-liner
sel = cars[cars['drives_right']]

# 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]
#or car_maniac = cars[cars['cars_per_cap'] > 500]

# 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
medium = cars[np.logical_and(cars['cars_per_cap']>100,cars['cars_per_cap']<500)]



# Print medium
print(medium)