Conditionals

Learning outcomes

Using conditional 'if' statements to selectively execute code and in functions
Using compound logic (e.g. and, or, parentheses) to define conditions

Use `if` statements to control whether or not a block of code is executed

An if statement (more properly called a conditional statement) controls whether some block of code is executed or not.
Structure is similar to a for statement:
- First line opens with if and ends with a colon
- Body containing one or more statements is indented (usually by 4 spaces)


mass = 3.54
if mass > 3.0:
    print(mass, 'is large')

mass = 2.07
if mass > 3.0:
    print(mass, 'is large')


3.54 is large

Conditionals are often used inside loops

Not much point using a conditional when we know the value (as above).
But useful when we have a collection to process.


masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
    if m > 3.0:
        print(m, 'is large')


3.54 is large
9.22 is large

Use `else` to execute a block of code when an `if` condition is not true

else can be used following an if.
Allows us to specify an alternative to execute when the if branch isn't taken.


masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
    if m > 3.0:
        print(m, 'is large')
    else:
        print(m, 'is small')


3.54 is large
2.07 is small
9.22 is large
1.86 is small
1.71 is small

Use `elif` to specify additional tests

May want to provide several alternative choices, each with its own test.
Use elif (short for "else if") and a condition to specify these.
Always associated with an if.
Must come before the else (which is the "catch all").


masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
    if m > 9.0:
        print(m, 'is HUGE')
    elif m > 3.0:
        print(m, 'is large')
    else:
        print(m, 'is small')


3.54 is large
2.07 is small
9.22 is HUGE
1.86 is small
1.71 is small

Conditions are tested once, in order

Python steps through the branches of the conditional in order, testing each in turn.
So ordering matters.


grade = 85
if grade >= 70:
    print('grade is C')
elif grade >= 80:
    print('grade is B')
elif grade >= 90:
    print('grade is A')


grade is C

Does not automatically go back and re-evaluate if values change.


velocity = 10.0
if velocity > 20.0:
    print('moving too fast')
else:
    print('adjusting velocity')
    velocity = 50.0


adjusting velocity

Often use conditionals in a loop to "evolve" the values of variables.


velocity = 10.0
for i in range(5): # execute the loop 5 times
    print(i, ':', velocity)
    if velocity > 20.0:
        print('moving too fast')
        velocity = velocity - 5.0
    else:
        print('moving too slow')
        velocity = velocity + 10.0
print('final velocity:', velocity)


0 : 10.0
moving too slow
1 : 20.0
moving too slow
2 : 30.0
moving too fast
3 : 25.0
moving too fast
4 : 20.0
moving too slow
final velocity: 30.0

Compound Relations Using `and`, `or`, and Parentheses

Often, you want some combination of things to be true. You can combine relations within a conditional using and and or. Continuing the example above, suppose you have


mass = [ 3.54,  2.07,  9.22,  1.86,  1.71]
velocity = [10.00, 20.00, 30.00, 25.00, 20.00]

i = 0
for i in range(5):
    if mass[i] > 5 and velocity[i] > 20:
        print("Fast heavy object.  Duck!")
    elif mass[i] > 2 and mass[i] <= 5 and velocity[i] <= 20:
        print("Normal traffic")
    elif mass[i] <= 2 and velocity[i] <= 20:
        print("Slow light object.  Ignore it")
    else:
        print("Whoa!  Something is up with the data.  Check it")

Just like with arithmetic, you can and should use parentheses whenever there is possible ambiguity. A good general rule is to always use parentheses when mixing and and or in the same condition. That is, instead of:


if mass[i] <= 2 or mass[i] >= 5 and velocity[i] > 20:

write one of these:


if (mass[i] <= 2 or mass[i] >= 5) and velocity[i] > 20:


if mass[i] <= 2 or (mass[i] >= 5 and velocity[i] > 20):

so it is perfectly clear to a reader (and to Python) what you really mean.

Tracing Execution

What does this program print?


pressure = 71.9
if pressure > 50.0:
    pressure = 25.0
elif pressure <= 50.0:
    pressure = 0.0
print(pressure)

Trimming Values

Fill in the blanks so that this program creates a new list containing zeroes where the original list's values were negative and ones where the original list's values were positive.


original = [-1.5, 0.2, 0.4, 0.0, -1.3, 0.4]
result = ____
for value in original:
    if ____:
        result.append(0)
    else:
        ____
print(result)


[0, 1, 1, 1, 0, 1]

Processing Small Files

Modify this program so that it only processes files with fewer than 50 records.


import glob
import pandas as pd
for filename in glob.glob('data/*.csv'):
    contents = pd.read_csv(filename)
    ____:
        print(filename, len(contents))

Initializing

Modify this program so that it finds the largest and smallest values in the list no matter what the range of values originally is.


values = [...some test data...]
smallest, largest = None, None
for v in values:
    if ____:
        smallest, largest = v, v
    ____:
        smallest = min(____, v)
        largest = max(____, v)
print(smallest, largest)

What are the advantages and disadvantages of using this method to find the range of the data?


values = [-2,1,65,78,-54,-24,100]
smallest, largest = None, None
for v in values:
    if smallest is None and largest is None:
        smallest, largest = v, v
    else:
        smallest = min(smallest, v)
        largest = max(largest, v)
print(smallest, largest)

It can be argued that an advantage of using this method would be to make the code more readable. However, readability is in the eye of the beholder, so another reader may prefer this approach:


values = [-2,1,65,78,-54,-24,100]
smallest, largest = None, None
for v in values:
    if smallest is None or v < smallest:
        smallest = v
    if largest is None or v > largest:
        largest = v
print(smallest, largest)

Using Functions With Conditionals in Pandas

Functions will often contain conditionals. Here is a short example that will indicate which quartile the argument is in based on hand-coded values for the quartile cut points.


def calculate_life_quartile(exp):
    if exp < 58.41:
        # This observation is in the first quartile
        return 1
    elif exp >= 58.41 and exp < 67.05:
        # This observation is in the second quartile
        return 2
    elif exp >= 67.05 and exp < 71.70:
        # This observation is in the third quartile
        return 3
    elif exp >= 71.70:
        # This observation is in the fourth quartile
        return 4
    else:
        # This observation has bad data
        return None

calculate_life_quartile(62.5)

That function would typically be used within a for loop, but Pandas has a different, more efficient way of doing the same thing, and that is by applying a function to a dataframe or a portion of a dataframe. Here is an example, using the definition above.


data = pd.read_csv('data/gapminder_all.csv')
data['life_qrtl'] = data['lifeExp_1952'].apply(calculate_life_quartile)

There is a lot in that second line, so let's take it piece by piece. On the right side of the = we start with data['lifeExp'], which is the column in the dataframe called data labeled lifExp. We use the apply() to do what it says, apply the calculate_life_quartile to the value of this column for every row in the dataframe.