Making Choices

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.

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 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(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:

if (1 > 0) and (-1 > 0):
    print('both parts are true')
else:
    print('at least one part is false')

at least one part is false

while or is true if at least one part is true:

if (1 < 0) or (-1 < 0):
    print('at least one test 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.

callout

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. 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:

import numpy as np

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

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 = np.max(data, axis=0)[0]
max_inflammation_20 = np.max(data, axis=0)[20]

if max_inflammation_0 == 0 and max_inflammation_20 == 20:
    print('Suspicious looking maxima!')

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:

if max_inflammation_0 == 0 and max_inflammation_20 == 20:
    print('Suspicious looking maxima!')
elif np.sum(np.min(data, axis=0)) == 0:
    print('Minima add up to zero!')

Suspicious looking maxima!

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

if max_inflammation_0 == 0 and max_inflammation_20 == 20:
    print('Suspicious looking maxima!')
elif np.sum(np.min(data, axis=0)) == 0:
    print('Minima add up to zero!')
else:
    print('Seems OK!')

Suspicious looking maxima!

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.

How Many Paths?

Consider this code:

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

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')

word is true
non-empty list is true
one is true

if not '':
    print('empty string is not true')
if not 'word':
    print('word is not true')
if not not True:
    print('not not True is true')

empty string is not true
not not True is true

a = 5
b = 5.1

if abs(a - b) < 0.1 * abs(b):
    print('True')
else:
    print('False')

True

print(abs(a - b) < 0.1 * abs(b))

True

In-Place Operators

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

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)

21 -14

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.

Sorting a List Into Buckets

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.

"String".startswith("Str")

True

But

"String".startswith("str")

False

Use the following Python code as your starting point:

files = ['inflammation-01.csv', 'myscript.py', 'inflammation-02.csv', 'small-01.csv', 'small-02.csv']
large_files = []
small_files = []
other_files = []

Your solution should:

1. loop over the names of the files
2. figure out which group each filename belongs
3. append the filename to that list

In the end the three lists should be:

large_files = ['inflammation-01.csv', 'inflammation-02.csv']
small_files = ['small-01.csv', 'small-02.csv']
other_files = ['myscript.py']

for file in files:
    if file.startswith('inflammation-'):
        large_files.append(file)
    elif file.startswith('small-'):
        small_files.append(file)
    else:
        other_files.append(file)

print('large_files:', large_files)
print('small_files:', small_files)
print('other_files:', other_files)

large_files: ['inflammation-01.csv', 'inflammation-02.csv']
small_files: ['small-01.csv', 'small-02.csv']
other_files: ['myscript.py']

vowels = 'aeiouAEIOU'
sentence = 'Mary had a little lamb.'
count = 0
for char in sentence:
    if char in vowels:
        count += 1

print("The number of vowels in this string is " + str(count))

The number of vowels in this string is 6

The material in this notebook is derived from the Software Carpentry lessons © Software Carpentry under the terms of the CC-BY 4.0 license.