Making Choices

Overview

Teaching: 30 min
Exercises: 0 min

Questions

• How can my programs do different things based on data values?

Objectives

• Write conditional statements including if, elif, and else branches.

• Correctly evaluate expressions containing and and or.

One thing we noticed in some of the light curves was that there were negative flux values. Another major issue for this dataset is missing measurements. Are there objects that are completely missing observations in a given filter? 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.

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

In some of the plots we saw negative fluxes. Let’s flag the objects and the filters in which these occur. Let’s do that! However, instead of checking every single value, we can check the all columns at once:

if np.nanmin(data[:,1]) < 0:
    print('Negative fluxes!')

We also saw a lot of NaNs. We can also check for this with an elif condition:

elif np.sum(np.isnan(data[:,1])) == data.shape[0]:
    print(f + ': A NaN column')

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

else:
    print(f+': Seems OK!')

Let’s test that out:

f1 = 'data/03D3af.csv'
data = np.loadtxt(fname=f1, delimiter=',', skiprows=1)


if np.nanmin(data[:,1]) < 0.:
    print(f1 + ': a negative flux')
elif np.sum(np.isnan(data[:,1])) == data.shape[0]:
    print(f1 + ': a NaN column')
else:
    print('Seems OK!')

data/03D3af.csv: A NaN column

f2 = 'data/03D1ar.csv'
data = np.loadtxt(fname=f2, delimiter=',')

if np.nanmin(data[:,1]) < 0.:
    print(f + ': a negative flux')
elif np.sum(np.isnan(data[:,1])) == data.shape[0]:
    print(f + ': a NaN column')
else:
    print('Seems OK!')

data/03D1ar.csv: A negative flux.

Or even check both with a loop :

files = [f1, f2]

for file in files:
    if np.nanmin(data[:1]) < 0:
        print(file + ': a negative flux')
    elif np.sum(np.isnan(data[:1])) == data.shape[0]:
        print(file + ': a NaN column')
    else:
        print(file + ' seems OK!')

data/03D3af.csv: A NaN column
data/03D1ar.csv: A negative flux.

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')

Which of the following would be printed if you were to run this code? Why did you pick this answer?

1. A
2. B
3. C
4. B and C

Solution

C gets printed because the first two conditions, 4 > 5 and 4 == 5, are not true, but 4 < 5 is true.

What Is Truth?

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.

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')

That’s Not Not What I Meant

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.

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')

Close Enough

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?

Solution 1

a = 5
b = 5.1

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

Solution 2

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

This works because the Booleans True and False have string representations which can be printed.

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

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?

Solution

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.

Sorting a List Into Buckets (DO NOT USE)

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:

"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']

Solution

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)

Counting Vowels

1. Write a loop that counts the number of vowels in a character string.
2. Test it on a few individual words and full sentences.
3. Once you are done, compare your solution to your neighbor’s. Did you make the same decisions about how to handle the letter ‘y’ (which some people think is a vowel, and some do not)?

Solution

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))

Key Points

• Use if condition to start a conditional statement, elif condition to provide additional tests, and else to provide a default.

• The bodies of the branches of conditional statements must be indented.

• Use == 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.

• Zero, the empty string, and the empty list are considered false; all other numbers, strings, and lists are considered true.

• True and False represent truth values.