AMATH Quiz 3

If statements

  • Determine the result of if-then statements by tracking the value of variables.

    • m

For loops

  • Write a for loop iterating over an array.

    • Ex:

    for variable in np.arange(array):
    #Formula or command goes here

    • np.arange(x) creates an array with x numbers inside

      • Remember arrays start counting at 0, so np.arange(20) would start at 0 and end at 19

  • Write a for loop using range.

    • Ex:

    for variable in range(range):
    #Formula or command goes here

    • It should be the default when creating a for loop where a variable increases by 1 each time

  • Recognize when a for loop should be used

    • A for loop is an efficient way of repeatedly doing the same process on different parts of a variable

  • Trace the flow of a for loop by keeping track of the value of variables.

  • Create an array of zeros in Python using np.zeros()

    • np.zeros(x) creates an array with x amount of zeroes

  • Populate a 1D array using a for loop.

    • Ex:

    for year in range(years + 1):
    if np.isin(year, count_years):
        model_pop[model_pop_index] = model_calculation #Think f(x) = function

  • Determine the result of a for loop with an if statement

Plotting data

  • Create a plot with markers from two arrays.

    • Ex:

fig, ax = plt.subplots()
ax.plot(count_years, model_pop, marker = 'o', linestyle = 'None', label = 'Model Population')
ax.plot(count_years, count_pop, marker = 'o', linestyle = 'None', label = 'Count Population')

ax.set_xlabel('Years after 1958')
ax.set_ylabel('Number of rabbits')
ax.set_title('Rabbits in Australia: Count vs. Model Comparison')
ax.legend()

fig.savefig('Rabbits.png')

  • Determine when to use a plot with markers.

    • When you don’t have enough data for a graph to be actually smooth

      • If you have a line, you should be able to point at any spot and get accurate information, so you should use markers only when that’s not possible

    • Markers show what data you do have

Python functions and anonymous functions

  • Recognize how using functions improve modularity and reusability

    • Functions provide an efficient way to find different information from the same variables without changing them every time

  • Create and call python functions.

    • Ex:

def f(x):
    y = x**2 + 3 
    return y  


# Use the function f
f_output = f(2)
print("The function f says that f(2) =", f_output)

  • Recognize variables that are defined globally vs. locally.

    • A variable INSIDE a function is local while a variable OUTSIDE a function is global.

  • Create and use anonymous functions.

    • Ex:

    f = lambda x: x**2 + 3
print(f(2))

    • Ex:

    add_two_numbers = lambda x, y: x+y

a = 2
b = 3
print(add_two_numbers(a, b))