Building programs with Python

Making Choices

In our numpy and visualisation lessons, we discovered something suspicious was going on in our inflammation data by drawing some plots. How can we use Python to automatically recognize the different features we saw, and take a different action for each? In this lesson, we’ll learn how to write code that runs only when certain conditions are true.

Conditionals

We can ask Python to take different actions, depending on a condition, with an if statement:

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 necessarily 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” as shown in the example code chunk below:

num = -3
    if num > 0:
        print 'Sign of a number',num,'is:',1
    elif num == 0:
        print 'Sign of a number',num,'is:',0
    else:
        print 'Sign of a number',num, 'is:',-1

sign of a number -3 is: -1

The keyword elif is short for else if, and is useful to avoid excessive indentation. An if ... elif ... elif ... sequence is a substitute for the switch or case statements found in other languages.

One important thing to notice in 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”.

Checking our Data

Now that we’ve seen how conditionals work, we can use them to check for the suspicious features we saw in our inflammation data. In the first couple of plots, the maximum inflammation per day seemed to rise like a straight line, one unit per day. We can check for this inside the for loop we wrote with the following conditional:

if data.max(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:
    print 'Suspicious looking maxima!'

We also saw a different problem in the third dataset; the minima per day were all zero (looks like a healthy person snuck into our study). We can also check for this with an elif condition:

elif data.min(axis=0).sum() == 0:
    print 'Minima add up to zero!'

And if neither of these conditions are true, we can use else to give the all-clear:

else:
    print 'Seems OK!'

In this way, we can ask Python to do something different depending on the condition of our data. Here we printed messages in all cases, but we could also imagine not using the else catch-all so that messages are only printed when something is wrong, freeing us from having to manually examine every plot for features we’ve seen before, or doing all manner of other things to respond to changing conditions in our data.

if 4 > 5:
    print 'A'
elif 4 <= 5:
    print 'B'
elif 4 < 5:
    print 'C'

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'