if
, elif
, and else
branches.and
and or
.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:
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 the if
statement is true, the body of the if
(i.e., the set of lines indented underneath it) is executed, and “greater” is printed. If the test is false, the body of the else
is executed instead, and “not greater” is printed. Only one or the other is ever executed before continuing on with program execution to print “done”:
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(num,' 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”. 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')
-3 is negative
Note that to test for equality we use a double equals sign ==
rather than a single equals sign =
which is used to assign values.
We can also combine tests using and
and or
. and
is only true if both parts are true:
at least one part is false
while or
is true if at least one part is true:
at least one test is true
True
and False
True
and False
are special words in Python called booleans
, which represent truth values. A statement such as 1 < 0
returns the value False
, while -1 < 0
returns the value 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. We are about to use functions provided by the numpy
module again. Therefore, if you’re working in a new Python session, make sure to load the module with:
From the first couple of plots, we saw that maximum daily inflammation exhibits a strange behavior and raises one unit a day. Wouldn’t it be a good idea to detect such behavior and report it as suspicious? Let’s do that! However, instead of checking every single day of the study, let’s merely check if maximum inflammation in the beginning (day 0) and in the middle (day 20) of the study are equal to the corresponding day numbers.
max_inflammation_0 = numpy.max(data, axis=0)[0]
max_inflammation_20 = numpy.max(data, axis=0)[20]
if max_inflammation_0 == 0 and max_inflammation_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:
And if neither of these conditions are true, we can use else
to give the all-clear:
Let’s test that out:
data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
max_inflammation_0 = numpy.max(data, axis=0)[0]
max_inflammation_20 = numpy.max(data, axis=0)[20]
if max_inflammation_0 == 0 and max_inflammation_20 == 20:
print('Suspicious looking maxima!')
elif numpy.sum(numpy.min(data, axis=0)) == 0:
print('Minima add up to zero!')
else:
print('Seems OK!')
Suspicious looking maxima!
data = numpy.loadtxt(fname='inflammation-03.csv', delimiter=',')
max_inflammation_0 = numpy.max(data, axis=0)[0]
max_inflammation_20 = numpy.max(data, axis=0)[20]
if max_inflammation_0 == 0 and max_inflammation_20 == 20:
print('Suspicious looking maxima!')
elif numpy.sum(numpy.min(data, axis=0)) == 0:
print('Minima add up to zero!')
else:
print('Seems OK!')
Minima add up to zero!
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.
Consider this code:
Which of the following would be printed if you were to run this code? Why did you pick this answer?
C gets printed because the first two conditions, 4 > 5
and 4 == 5
, are not true, but 4 < 5
is true.
True
and False
booleans are not 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.
Sometimes it is useful to check whether some condition is not true. The Boolean operator not
can do this explicitly. After reading and running the code below, write some if
statements that use not
to test the rule that you formulated in the previous challenge.
Write some conditions that print True
if the variable a
is within 10% of the variable b
and False
otherwise. Compare your implementation with your partner’s: do you get the same answer for all possible pairs of numbers?
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
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?
positive_sum = 0
negative_sum = 0
test_list = [3, 4, 6, 1, -1, -5, 0, 7, -8]
for num in test_list:
if num > 0:
positive_sum += num
elif num == 0:
pass
else:
negative_sum += num
print(positive_sum, negative_sum)
Here pass
means “don’t do anything”. In this particular case, it’s not actually needed, since if num == 0
neither sum needs to change, but it illustrates the use of elif
and pass
.
In our data
folder, large data sets are stored in files whose names start with “inflammation-” and small data sets – in files whose names start with “small-”. We also have some other files that we do not care about at this point. We’d like to break all these files into three lists called large_files
, small_files
, and other_files
, respectively. Add code to the template below to do this. Note that the string method startswith
returns True
if and only if the string it is called on starts with the string passed as an argument, that is:
True
But
False
Use the following Python code as your starting point:
filenames = ['inflammation-01.csv',
'myscript.py',
'inflammation-02.csv',
'small-01.csv',
'small-02.csv']
large_files = []
small_files = []
other_files = []
Your solution should:
In the end the three lists should be:
for filename in filenames:
if filename.startswith('inflammation-'):
large_files.append(filename)
elif filename.startswith('small-'):
small_files.append(filename)
else:
other_files.append(filename)
print('large_files:', large_files)
print('small_files:', small_files)
print('other_files:', other_files)
{% include links.md %}
if condition
to start a conditional statement, elif condition
to provide additional tests, and else
to provide a default.==
to test for equality.X and Y
is only true if both X
and Y
are true.X or Y
is true if either X
or Y
, or both, are true.True
and False
represent truth values.