Making Choices

Our previous lessons have shown us how to manipulate data, define our own functions, and repeat things. However, the programs we have written so far always do the same things, regardless of what data they're given. We want programs to make choices based on the values they are manipulating

Objectives

Explain the similarities and differences between tuples and lists.
Write conditional statements including if, elif, and else branches.
Correctly evaluate expressions containing and and or.
Correctly write and interpret code containing nested loops and conditionals.
Explain the advantages of putting frequently-modified code in a function.

### Conditionals

The tool Python gives us for making choices is called a conditional statement, and looks like this:

num = 37
if num > 100:
    print 'greater'
else:
    print 'not greater'
print 'done'

not greater
done

The second line of this code uses the keyword if to tell Python that we want to make a choice. If the test that follows it is true, the body of the if (i.e., the lines indented underneath it) are executed. If the test is false, the body of the else is executed instead. Only one or the other is ever executed:

Executing a Conditional

Conditional statements don't have to include an else. If there isn't one, Python simply does nothing if the test is false:

num = 53
print 'before conditional...'
if num > 100:
    print '53 is greater than 100'
print '...after conditional'

before conditional...
...after conditional

We can also chain several tests together using elif, which is short for "else if". This makes it simple to write a function that returns the sign of a number:

def sign(num):
    if num > 0:
        return 1
    elif num == 0:
        return 0
    else:
        return -1

print 'sign of -3:', sign(-3)

sign of -3: -1

One important thing to notice the code above is that we use a double equals sign == to test for equality rather than a single equals sign because the latter is used to mean assignment. This convention was inherited from C, and while many other programming languages work the same way, it does take a bit of getting used to...

We can also combine tests using and and or. and is only true if both parts are true:

if (1 > 0) and (-1 > 0):
    print 'both parts are true'
else:
    print 'one part is not true'

one part is not true

while or is true if either part is true:

if (1 < 0) or ('left' < 'right'):
    print 'at least one test is true'

at least one test is true

In this case, "either" means "either or both", not "either one or the other but not both".

Challenges

True and False aren't the only values in Python that are true and false. In fact, any value can be used in an if or elif. After reading and running the code below, explain what the rule is for which values are considered true and which are considered false. (Note that if the body of a conditional is a single statement, we can write it on the same line as the if.)
```
if '': print 'empty string is true'
```
```
if 'word': print 'word is true'
```
```
if []: print 'empty list is true'
```
```
if [1, 2, 3]: print 'non-empty list is true'
```
```
if 0: print 'zero is true'
```
```
if 1: print 'one is true'
```
Write a function called near that returns True if its first parameter is within 10% of its second and False otherwise. Compare your implementation with your partner's: do you return the same answer for all possible pairs of numbers?

Nesting

Another thing to realize is that if statements can be combined with loops just as easily as they can be combined with functions. For example, if we want to sum the positive numbers in a list, we can write this:

numbers = [-5, 3, 2, -1, 9, 6]
total = 0
for n in numbers:
    if n >= 0:
        total = total + n
print 'sum of positive values:', total

sum of positive values: 20

We could equally well calculate the positive and negative sums in a single loop:

pos_total = 0
neg_total = 0
for n in numbers:
    if n >= 0:
        pos_total = pos_total + n
    else:
        neg_total = neg_total + n
print 'negative and positive sums are:', neg_total, pos_total

negative and positive sums are: -6 20

We can even put one loop inside another:

for consonant in 'bcd':
    for vowel in 'ae':
        print consonant + vowel

ba
be
ca
ce
da
de

As the diagram below shows, the inner loop runs from start to finish each time the outer loop runs once:

Execution of Nested Loops

This is our first hand-made data visualization: the colors show where x is less than, equal to, or greater than y.

Challenges

Python (and most other languages in the C family) provides in-place operators that work like this:
```
x = 1  # original value
x += 1 # add one to x, assigning result back to x
x *= 3 # multiply x by 3
print x
6
```
Rewrite the code that sums the positive and negative numbers in a list using in-place operators. Do you think the result is more or less readable than the original?

Making Choices Analyzing Inflammation

Let's say that we want to split up our original inflmmation dataset into individuals who had above average inflammation and those with below average inflammation over the entire time course.

First, let's make sure that our data is loaded:

data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')

Next, need to know what is above average and below average inflammation for an individual. For this, we can think back to one of our first exercises where we calculated the average inflammation:

print data.mean()

and save that value to a variable:

ave_inflammation = data.mean()

Now, let's use a loop to look at how individuals with high or low inflammation scores improved over time:

for indv in range(0,data.shape[0]):
    if data[indv,:].mean() < ave_inflammation:
        first_ten = data[indv,0:10].mean()
        last_ten = data[indv,data.shape[1]-10:data.shape[1]].mean()
        print last_ten - first_ten

Note: numpy arrays have specific built-in functions for looping through elements in the array, however, this is a little easier to relate back to what we have talked about today.

Challenges

Write a function called ave_change(data, cutoff, direction=">") that calculates the average change in inflammation over the first ten days to the last ten days for individuals with average inflammation above the average inflammation of the entire cohort (cutoff). Allow for the option to calculate the change in inflammation for individuals with average inflammation below the cutoff (direction). Use the outlined function below as a guide. As always, remember to document your code!

def ave_change(data, cutoff, direction=">"):
	if direction == ">":
		# if want to calculate improvement for individuals above cutoff
	else:
		# if want to calculate improvement for indvidiuals below cutoff