Writing Functions

Overview

Teaching: 15 min
Exercises: 10 min

Questions

• How can I create my own functions?

Objectives

• Explain and identify the difference between function definition and function call.

• Write a function that takes a small, fixed number of arguments and produces a single result.

Break programs down into functions to make them easier to understand.

• Human beings can only keep a few items in working memory at a time.
• Understand larger/more complicated ideas by understanding and combining pieces.
  • Components in a machine.
  • Lemmas when proving theorems.
• Functions serve the same purpose in programs.
  • Encapsulate complexity so that we can treat it as a single “thing”.
• Also enables re-use.
  • Write one time, use many times.

Define a function using def with a name, parameters, and a block of code.

• Begin the definition of a new function with def.
• Followed by the name of the function.
  • Must obey the same rules as variable names.
• Then parameters in parentheses.
  • Empty parentheses if the function doesn’t take any inputs.
  • We will discuss this in detail in a moment.
• Then a colon.
• Then an indented block of code.

def print_greeting():
    print('Hello!')

Defining a function does not run it.

• Defining a function does not run it.
  • Like assigning a value to a variable.
• Must call the function to execute the code it contains.
• The commands for the function are read and stored after the def block, but not actually executed until the function is called later on.
  • Imagine getting a recipe card and keeping it in your kitchen. You can cook it anytime, but you haven’t completed any of the steps until you start that cooking process.
  • This means that Python won’t complain about problems until you call the function. More specifically, just because the definition of a function runs without error doesn’t mean that there won’t be errors when it executes later.

print_greeting()

Hello!

Arguments in call are matched to parameters in definition.

• Functions are most useful when they can operate on different data.
• Specify parameters when defining a function.
  • These become variables when the function is executed.
  • Are assigned the arguments in the call (i.e., the values passed to the function).

def print_date(year, month, day):
    joined = str(year) + '/' + str(month) + '/' + str(day)
    print(joined)

print_date(1871, 3, 19)

1871/3/19

• Via Twitter: () contains the ingredients for the function while the body contains the recipe.

Functions may return a result to their caller using return.

• Use return ... to give a value back to the caller.
• May occur anywhere in the function.
• But functions are easier to understand if return occurs:
  • At the start to handle special cases.
  • At the very end, with a final result.

def average(values):
    if len(values) == 0:
        return None
    return sum(values) / len(values)

a = average([1, 3, 4])
print('average of actual values:', a)

2.6666666666666665

print('average of empty list:', average([]))

None

The scope of a variable is the part of a program that can ‘see’ that variable.

• There are only so many sensible names for variables.
• People using functions shouldn’t have to worry about what variable names the author of the function used.
• People writing functions shouldn’t have to worry about what variable names the function’s caller uses.
• The part of a program in which a variable is visible is called its scope.

pressure = 103.9

def adjust(t):
    temperature = t * 1.43 / pressure
    return temperature

• pressure is a global variable.
  • Defined outside any particular function.
  • Visible everywhere.
• t and temperature are local variables in adjust.
  • Defined in the function.
  • Not visible in the main program.
  • Remember: a function parameter is a variable that is automatically assigned a value when the function is called.

print('adjusted:', adjust(0.9))
print('temperature after call:', temperature)

adjusted: 0.01238691049085659

Traceback (most recent call last):
  File "/Users/swcarpentry/foo.py", line 8, in <module>
    print('temperature after call:', temperature)
NameError: name 'temperature' is not defined

Definition and Use

What does the following program print?

def report(pressure):
    print('pressure is', pressure)

report(22.5)

Solution

pressure is 22.5

Calling by Name

What does this short program print?

def print_date(year, month, day):
    joined = str(year) + '/' + str(month) + '/' + str(day)
    print(joined)

print_date(day=1, month=2, year=2003)

1. When have you seen a function call like this before?
2. When and why is it useful to call functions this way?

Solution

The program prints:

2003/2/1

It is useful to call a function with named arguments to ensure that the values of each argument are assigned to the intended argument in the function. This allows the order of arguments to be specified independently of how they are defined in the function itself.

Order of Operations

The example above:

result = print_date(1871, 3, 19)
print('result of call is:', result)

printed:

1871/3/19
result of call is: None

Explain why the two lines of output appeared in the order they did.

Solution

Each line of Python code is executed in order, regardless of whether that line calls out to a function, which may call out to other functions, or a variable assignment. In this case, the second line call to print will not execute until the result of print_date is complete in the first line.

Encapsulation

Fill in the blanks to create a function that takes a single filename as an argument, loads the data in the file named by the argument, and returns the minimum value in that data.

import pandas

def min_in_data(____):
    data = ____
    return ____

Solution

import pandas

def min_in_data(filename):
    data = pandas.read_csv(filename)
    return data.min()

Find the First

Fill in the blanks to create a function that takes a list of numbers as an argument and returns the first negative value in the list. What does your function do if the list is empty?

def first_negative(values):
    for v in ____:
        if ____:
            return ____

Solution

def first_negative(values):
    for v in values:
        if v < 0:
            return v

Encapsulate of If/Print Block

The code below will run on a label-printer for chicken eggs. A digital scale will report a chicken egg mass (in grams) to the computer and then the computer will print a label.

Please re-write the code so that the if-block is folded into a function.

 import random
 for i in range(10):

    # simulating the mass of a chicken egg
    # the (random) mass will be 70 +/- 20 grams
    mass=70+20.0*(2.0*random.random()-1.0)

    print(mass)
   
    #egg sizing machinery prints a label
    if(mass>=85):
       print("jumbo")
    elif(mass>=70):
       print("large")
    elif(mass<70 and mass>=55):
       print("medium")
    else:
       print("small")

The simplified program follows. What function definition will make it functional?

 # revised version
 import random
 for i in range(10):

    # simulating the mass of a chicken egg
    # the (random) mass will be 70 +/- 20 grams
    mass=70+20.0*(2.0*random.random()-1.0)

    print(mass,print_egg_label(mass))    

1. Create a function definition for print_egg_label() that will work with the revised program above. Note, the function’s return value will be significant. Sample output might be 71.23 large.
2. A dirty egg might have a mass of more than 90 grams, and a spoiled or broken egg will probably have a mass that’s less than 50 grams. Modify your print_egg_label() function to account for these error conditions. Sample output could be 25 too light, probably spoiled.

Solution

def print_egg_label(mass):
  if(mass>=90):
     print(mass, "dirty")
  elif(mass>=85):
     print(mass, "jumbo")
  elif(mass>=70):
     print(mass, "large")
  elif(mass<70 and mass>=55):
     print(mass, "medium")
  else:
     print(mass, "too light, probably spoiled")

Encapsulating Data Analysis

Assume that the following code has been executed:

import pandas

df = pandas.read_csv('gapminder_gdp_asia.csv', index_col=0)
japan = df.ix['Japan']

1. Complete the statements below to obtain the average GDP for Japan across the years reported for the 1980s.

year = 1983
gdp_decade = 'gdpPercap_' + str(year // ____)
avg = (japan.ix[gdp_decade + ___] + japan.ix[gdp_decade + ___]) / 2

1. Abstract the code above into a single function.

def avg_gdp_in_decade(country, continent, year):
    df = pd.read_csv('gapminder_gdp_'+___+'.csv',delimiter=',',index_col=0)
    ____
    ____
    ____
    return avg

1. How would you generalize this function if you did not know beforehand which specific years occurred as columns in the data? For instance, what if we also had data from years ending in 1 and 9 for each decade? (Hint: use the columns to filter out the ones that correspond to the decade, instead of enumerating them in the code.)

Solution

year = 1983
gdp_decade = 'gdpPercap_' + str(year // 10)
avg = (japan.ix[gdp_decade + '2'] + japan.ix[gdp_decade + '7']) / 2

2.

def avg_gdp_in_decade(country, continent, year):
    df = pd.read_csv('gapminder_gdp_' + continent + '.csv', index_col=0)
    c = df.ix[country]
    gdp_decade = 'gdpPercap_' + str(year // 10)
    avg = (c.ix[gdp_decade + '2'] + c.ix[gdp_decade + '7'])/2
    return avg

1. We need to loop over the reported years to obtain the average for the relevant ones in the data.

def avg_gdp_in_decade(country, continent, year):
    df = pd.read_csv('gapminder_gdp_' + continent + '.csv', index_col=0)
    c = df.ix[country] 
    gdp_decade = 'gdpPercap_' + str(year // 10)
    total = 0.0
    num_years = 0
    for yr_header in c.index: # c's index contains reported years
        if yr_header.startswith(gdp_decade):
            total = total + c.ix[yr_header]
            num_years = num_years + 1
    return total/num_years

Key Points

• Break programs down into functions to make them easier to understand.

• Define a function using def with a name, parameters, and a block of code.

• Defining a function does not run it.

• Arguments in call are matched to parameters in definition.

• Functions may return a result to their caller using return.