Hacks 1 

def collatz(i):
    while i > 1:
        print(i, end=' ')
        if (i % 2):
            # i is odd
            i = 3*i + 1
        else:
            # i is even
            i = i//2
    print(1, end='')
    
    while i != 1:
        if i % 2 > 0:
                i =((3 * i) + 1)
                list_.append(i)
        else:
            i = (i / 2)
            list_.append(i)
    return list_

print('Please enter a number: ', end='')
while True:
    try:
        i = int(input())
        list_ = [i]
        break
    except ValueError:
        print('Invaid selection, try again: ', end='')

col = collatz(i)
 
i = int(input('Enter i: '))
print('Sequence: ', end='')
print('Number of iterations:', len(col) - 1)
collatz(i)

Please enter a number: 6 3 10 5 16 8 4 2 1Sequence: Number of iterations: 0
6 3 10 5 16 8 4 2 1
[6]

Hacks 2 

def bubblesort(numList):
    indexingLen = len(numList) -1
    sorted = False
    
    while not sorted:
        sorted = True
        for i in range(0, indexingLen):
            if numList[i] > numList[i+1]:
                sorted = False
                numList[i], numList[i+1] = numList[i+1], numList[i]
    return numList

print(bubblesort([4,5,2,6,1,0,3]))

#  As you can see, this code works but is very inefficient. 
#  this is a list/array sorting function that sorts by
#  looking at the left indice and comparing to the right indice and repeating it. 
# This doesn't matter with small lists like this, but if the list legnth were to be
#  in the millions, then it would be a big problem. 

[0, 1, 2, 3, 4, 5, 6]

sortList = [4,5,2,6,1,0,3]

sortList.sort()

print(sortList)

[0, 1, 2, 3, 4, 5, 6]

The second example is much more efficient at sorting because it saves us time and uses a better algorithm. The first one uses the bubble sort algorithm which is very slow with large datasets. The second one uses the in-built python sort() function which makes use of the Tim Sort algorithm. This function also saved more memory compared to the first one.

Algorithm efficiency: I would say that algorithm efficiency is more than just how many steps it takes for a computer to solve a problem; less steps means more efficient. Algorithm efficiency is more about measuring the amount of resources it takes for a computer to solve a problem. Things like memory usage, power usage, and time complexity

Here is a way that I automated my day. I used the schedule library from python to help me out.

import schedule as se
import time 

def wakeUp():
    print("Wake up for school!")
    
def eatBreakfast():
    print("don't forget to eat breakfast")
    
def finHw():
    print("it's time to finish your homework")
    
se.every().day.at("06:00").do(wakeUp)
se.every().day.at("07:00").do(eatBreakfast)
se.every().day.at("15:00").do(finHw)