if, `elif`, and `else` branches.
In our last lesson, 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.
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')
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 the
if statement 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.
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')
We can also chain several tests together using elif, which is short for “else if”. The following Python code uses elif to print the sign of a number.
num = -3 if num > 0: print(num, "is positive") elif num == 0: print(num, "is zero") else: print(num, "is negative")
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.
We can also combine tests using
and is only true if both parts are true:
if (1 > 0) and (-1 > 0): print('both parts are true') else: print('at least one part is false')
or is true if at least one part is true:
if (1 < 0) or (-1 < 0): print('at least one test is true')
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 and data.max(axis=0) == 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 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!')
lets test that out then!
import numpy as np data = np.loadtxt(fname='data/inflammation-01.csv', delimiter=',') if data.max(axis=0) == 0 and data.max(axis=0) == 20: print('Suspicious looking maxima!') elif data.min(axis=0).sum() == 0: print('Minima add up to zero!') else: print('Seems OK!')
data = numpy.loadtxt(fname='inflammation-03.csv', delimiter=',') if data.max(axis=0) == 0 and data.max(axis=0) == 20: print('Suspicious looking maxima!') elif data.min(axis=0).sum() == 0: print('Minima add up to zero!') else: print('Seems OK!')
In this way, we have asked 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.
if 4 > 5: print('A') elif 4 == 5: print('B') elif 4 < 5: print('C')
Falseare special words in Python called
booleanswhich represent true and false statements. However, they aren’t the only values in Python that are true and false. In fact, any value can be used in an
elif. After reading and running the code below, explain what the rule is for which values are considered true and which are considered false.
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')
Trueif the variable
ais within 10% of the variable
Falseotherwise. Compare your implementation with your partner’s: do you get the same answer for all possible pairs of numbers?
x = 1 # original value x += 1 # add one to x, assigning result back to x x *= 3 # multiply x by 3 print(x)Write some code that sums the positive and negative numbers in a list separately, using in-place operators. Do you think the result is more or less readable than writing the same without in-place operators?