Intermediate Python

# NumPy is imported, seed is set

# Starting step
step = 50

# Roll the dice
dice = np.random.randint(1,7)

# Finish the control construct
if dice <= 2 :
    step = step - 1
elif dice <= 5 :
    step = step + 1
else :
    step = step + np.random.randint(1,7)

# Print out dice and step
print(dice)
print(step)

# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Use randint() to simulate a dice
print(np.random.randint(1,7))

# Use randint() again
print(np.random.randint(1,7))

# Import numpy as np
import numpy as np

# Set the seed
np.random.seed(123)

# Generate and print random float
print(np.random.rand())

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

# Use .apply(str.upper)
for lab, row in cars.iterrows() :
    cars["COUNTRY"] = cars["country"].apply(str.upper)
    print(cars)

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

# Code for loop that adds COUNTRY column
for lab, row in cars.iterrows() :
    cars.loc[lab, "COUNTRY"] = row["country"].upper()
    
# Print cars
print(cars)

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

# Adapt for loop
for lab, row in cars.iterrows() :
    print(lab + ": " + str(row['cars_per_cap']))

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

# Iterate over rows of cars
for lab, row in cars.iterrows() :
    print(lab)
    print(row)

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'austria':'vienna' }
          
# Iterate over europe
for key , value in europe.items() :
    print("the capital of " + str(key) + "is" + str(value))

# 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 :
    print("the" + x[0] + "is" + str(x[1]) + "sqm")

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

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

# Initialize offset
offset = -6

# Code the while loop
while offset != 0 :
    print("correcting...")
    if offset > 0 :
      offset = offset - 1
    else : 
      offset = offset + 1    
    print(offset)

# Initialize offset
offset = 8

# Code the while loop
while offset != 0 :
    print("correcting...")
    offset = offset - 1
    print(offset)

# 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)

# 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)

# Print out drives_right column as Series
print(cars.loc[:,'drives_right'])

# Print out drives_right column as DataFrame
print(cars.loc[:,['drives_right']])

# Print out cars_per_cap and drives_right as DataFrame
print(cars.loc[:,['cars_per_cap','drives_right']])