Creating Functions
Last updated on 2026-03-31 | Edit this page
Estimated time: 90 minutes
Overview
Questions
- How can I define new functions?
- What’s the difference between defining and calling a function?
- What happens when I call a function?
- Why do I need functions?
Objectives
- Define a function that takes parameters.
- Return a value from a function.
- Test and debug a function.
- Set default values for function parameters.
- Explain why we should divide programs into small, single-purpose functions.
At this point, we’ve seen that code can have Python make decisions about what it sees in our data. What if we want to convert some of our data, like taking a temperature in Fahrenheit and converting it to Celsius. We could write something like this for converting a single number
and for a second number we could just copy the line and rename the variables
PYTHON
fahrenheit_val = 99
celsius_val = ((fahrenheit_val - 32) * (5/9))
fahrenheit_val2 = 43
celsius_val2 = ((fahrenheit_val2 - 32) * (5/9))But we would be in trouble as soon as we had to do this more than a
couple times. Cutting and pasting it is going to make our code get very
long and very repetitive, very quickly. We’d like a way to package our
code so that it is easier to reuse, a shorthand way of re-executing
longer pieces of code. In Python we can use ‘functions’. Let’s start by
defining a function fahr_to_celsius that converts
temperatures from Fahrenheit to Celsius:
PYTHON
def explicit_fahr_to_celsius(temp):
# Assign the converted value to a variable
converted = ((temp - 32) * (5/9))
# Return the value of the new variable
return converted
def fahr_to_celsius(temp):
# Return converted value more efficiently using return
# without creating a new variable. This code does
# the same thing as the previous function but in a shorter way.
return ((temp - 32) * (5/9))
The function definition opens with the keyword def
followed by the name of the function (fahr_to_celsius) and
a parenthesized list of parameter names (temp). The body of the function, the statements that
are executed when it runs, is indented below the definition line. The
body concludes with a return keyword followed by the return
value.
When we call the function, the values we pass to it are assigned to those variables so that we can use them inside the function. Inside the function, we use a return statement to send a result back to whoever asked for it.
Let’s try running our function.
This command should call our function, using “32” as the input and return the function value.
In fact, calling our own function is no different from calling any other function:
PYTHON
print('freezing point of water:', fahr_to_celsius(32), 'C')
print('boiling point of water:', fahr_to_celsius(212), 'C')OUTPUT
freezing point of water: 0.0 C
boiling point of water: 100.0 CWe’ve successfully called the function that we defined, and we have access to the value that we returned.
Composing Functions
Now that we’ve seen how to turn Fahrenheit into Celsius, we can also write the function to turn Celsius into Kelvin:
PYTHON
def celsius_to_kelvin(temp_c):
return temp_c + 273.15
print('freezing point of water in Kelvin:', celsius_to_kelvin(0.))OUTPUT
freezing point of water in Kelvin: 273.15What about converting Fahrenheit to Kelvin? We could write out the formula, but we don’t need to. Instead, we can compose the two functions we have already created:
PYTHON
def fahr_to_kelvin(temp_f):
temp_c = fahr_to_celsius(temp_f)
temp_k = celsius_to_kelvin(temp_c)
return temp_k
print('boiling point of water in Kelvin:', fahr_to_kelvin(212.0))OUTPUT
boiling point of water in Kelvin: 373.15This is our first taste of how larger programs are built: we define basic operations, then combine them in ever-larger chunks to get the effect we want. In practice, many functions are longer than the ones shown here, but it is usually a good idea to keep functions focused on a single task and not make them unnecessarily long.
Variable Scope
In composing our temperature conversion functions, we created
variables inside of those functions, temp,
temp_c, temp_f, and temp_k. We
refer to these variables as local variables because they no
longer exist once the function is done executing. If we try to access
their values outside of the function, we will encounter an error:
ERROR
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-eed2471d229b> in <module>
----> 1 print('Again, temperature in Kelvin was:', temp_k)
NameError: name 'temp_k' is not definedIf you want to reuse the temperature in Kelvin after you have
calculated it with fahr_to_kelvin, you can store the result
of the function call in a variable:
OUTPUT
temperature in Kelvin was: 373.15The variable temp_kelvin, being defined outside any
function, is said to be global.
Inside a function, one can read the value of such global variables:
PYTHON
def print_temperatures():
print('temperature in Fahrenheit was:', temp_fahr)
print('temperature in Kelvin was:', temp_kelvin)
temp_fahr = 212.0
temp_kelvin = fahr_to_kelvin(temp_fahr)
print_temperatures()OUTPUT
temperature in Fahrenheit was: 212.0
temperature in Kelvin was: 373.15Although a function can read values from global variables, relying too much on globals can make code harder to understand and test, therefore best practice is to avoid this.
Tidying up
Now that we know how to wrap bits of code up in functions, we can
make our inflammation analysis easier to read and easier to reuse.
First, let’s make a visualize function that generates our
plots:
PYTHON
def visualize(filename):
data = np.loadtxt(fname=filename, delimiter=',')
fig = plt.figure(figsize=(10.0, 3.0))
axes1 = fig.add_subplot(1, 3, 1)
axes2 = fig.add_subplot(1, 3, 2)
axes3 = fig.add_subplot(1, 3, 3)
axes1.set_ylabel('average')
axes1.plot(np.mean(data, axis=0))
axes2.set_ylabel('max')
axes2.plot(np.amax(data, axis=0))
axes3.set_ylabel('min')
axes3.plot(np.amin(data, axis=0))
fig.tight_layout()
plt.show()and another function called detect_problems that checks
for those suspicious patterns we noticed:
PYTHON
def detect_problems(filename):
data = np.loadtxt(fname=filename, delimiter=',')
if np.amax(data, axis=0)[0] == 0 and np.amax(data, axis=0)[20] == 20:
print('Suspicious looking maxima!')
elif np.sum(np.amin(data, axis=0)) == 0:
print('Minima add up to zero!')
else:
print('Seems OK!')Wait! Didn’t we forget to specify what both of these functions should
return? Well, we didn’t. In Python, functions are not required to
include a return statement and can be used for the sole
purpose of grouping together pieces of code that conceptually do one
thing. In such cases, function names usually describe what they do,
e.g. visualize, detect_problems.
Where no return is included, as a default, Python will return a
none.
Notice that rather than jumbling this code together in one giant
for loop, we can now read and reuse both ideas separately.
We can reproduce the previous analysis with a much simpler
for loop:
PYTHON
filenames = sorted(glob.glob('../data/inflammation*.csv'))
for filename in filenames[:3]:
print(filename)
visualize(filename)
detect_problems(filename)By giving our functions human-readable names, we can more easily read
and understand what is happening in the for loop. Even
better, if at some later date we want to use either of those pieces of
code again, we can do so in a single line.
Testing and Documenting
Once we start putting things in functions so that we can re-use them, it is good practice to write some documentation to remind ourselves later what it’s for and how to use it.
If the first thing in a function is a string that isn’t assigned to a variable, that string is attached to the function as its documentation:
PYTHON
def visualize(filename):
"""Load a CSV file and plot the average, maximum, and minimum values for each day."""
A string like this is called a docstring. Docstrings are usually written with triple quotes, which also lets us spread them across multiple lines if needed:
PYTHON
def visualize(filename):
"""
Load inflammation data from a CSV file and display three summary plots.
This function reads numerical data from the file given by `filename`,
where each row represents one patient and each column represents one day.
It then creates a figure with three side-by-side line plots showing:
1. The average value for each day across all patients.
2. The maximum value for each day across all patients.
3. The minimum value for each day across all patients.
Parameters
----------
filename : str
The path to the CSV file containing the inflammation data.
Returns
-------
None
"""Defining Defaults
We have passed parameters to functions in two ways: directly, as in
type(data), and by name, as in
np.loadtxt(fname='something.csv', delimiter=','). In fact,
we can pass the filename to loadtxt without the
fname=:
OUTPUT
array([[ 0., 0., 1., ..., 3., 0., 0.],
[ 0., 1., 2., ..., 1., 0., 1.],
[ 0., 1., 1., ..., 2., 1., 1.],
...,
[ 0., 1., 1., ..., 1., 1., 1.],
[ 0., 0., 0., ..., 0., 2., 0.],
[ 0., 0., 1., ..., 1., 1., 0.]])but we still need to say delimiter=:
ERROR
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/username/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py", line 1041, in loa
dtxt
dtype = np.dtype(dtype)
File "/Users/username/anaconda3/lib/python3.6/site-packages/numpy/core/_internal.py", line 199, in
_commastring
newitem = (dtype, eval(repeats))
File "<string>", line 1
,
^
SyntaxError: unexpected EOF while parsingLet’s look at the help for np.loadtxt:
OUTPUT
Help on function loadtxt in module np.lib.npyio:
loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, use
cols=None, unpack=False, ndmin=0, encoding='bytes')
Load data from a text file.
Each row in the text file must have the same number of values.
Parameters
----------
...There’s a lot of information here, but the most important part is the first couple of lines:
OUTPUT
loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, use
cols=None, unpack=False, ndmin=0, encoding='bytes')This tells us that loadtxt has one parameter called
fname that doesn’t have a default value, and eight others
that do. If we call the function like this:
then the filename is assigned to fname (which is what we
want), but the delimiter string ',' is assigned to
dtype rather than delimiter, because
dtype is the second parameter in the list. However
',' isn’t a known dtype so our code produced
an error message when we tried to run it. When we call
loadtxt we don’t have to provide fname= for
the filename because it’s the first item in the list, but if we want the
',' to be assigned to the variable delimiter,
we do have to provide delimiter= for the second
parameter since delimiter is not the second parameter in
the list.
If we usually want a function to work one way, but occasionally need it to do something else, we can allow people to pass a parameter when they need to but provide a default to make the normal case easier. The example below shows how Python matches values to parameters:
PYTHON
def display(a=1, b=2, c=3):
print('a:', a, 'b:', b, 'c:', c)
print('no parameters:')
display()
print('one parameter:')
display(55)
print('two parameters:')
display(55, 66)OUTPUT
no parameters:
a: 1 b: 2 c: 3
one parameter:
a: 55 b: 2 c: 3
two parameters:
a: 55 b: 66 c: 3As this example shows, parameters are matched up from left to right, and any that haven’t been given a value explicitly get their default value. We can override this behavior by naming the value as we pass it in:
OUTPUT
only setting the value of c
a: 1 b: 2 c: 77Readable functions
Consider these two functions:
PYTHON
def s(p):
a = 0
for v in p:
a += v
m = a / len(p)
d = 0
for v in p:
d += (v - m) * (v - m)
return np.sqrt(d / (len(p) - 1))
def std_dev(sample):
sample_sum = 0
for value in sample:
sample_sum += value
sample_mean = sample_sum / len(sample)
sum_squared_devs = 0
for value in sample:
sum_squared_devs += (value - sample_mean) * (value - sample_mean)
return np.sqrt(sum_squared_devs / (len(sample) - 1))The functions s and std_dev are
computationally equivalent (they both calculate the sample standard
deviation), but to a human reader, they look very different. You
probably found std_dev much easier to read and understand
than s.
As this example illustrates, both documentation and a programmer’s coding style combine to determine how easy it is for others to read and understand the programmer’s code. Choosing meaningful variable names and using blank spaces to break the code into logical “chunks” are helpful techniques for producing readable code. This is useful not only for sharing code with others, but also for the original programmer. If you need to revisit code that you wrote months ago and haven’t thought about since then, you will appreciate the value of readable code!
Challenges
Combining Strings
“Adding” two strings produces their concatenation:
'a' + 'b' is 'ab'. Write a function called
fence that takes two parameters called
original and wrapper and returns a new string
that has the wrapper character at the beginning and end of the original.
A call to your function should look like this:
OUTPUT
*name*OUTPUT
259.81666666666666
278.15
273.15
0k is 0 because the k inside the function
f2k doesn’t know about the k defined outside
the function. When the f2k function is called, it creates a
local variable
k. The function returns a local k but that
does not alter the k outside of its local copy. Therefore
the original value of k remains unchanged. Beware that a
local k is created because f2k internal
statements affect a new value to it. If k was only
read, it would simply retrieve the global k
value.
- The body of a function must be indented.
- The
scopeof variables defined within a function can only be seen and used within the body of the function. - Variables created outside of any function are called global variables.
- Within a function, we can access global variables.
- Variables created within a function override global variables if their names match.
- Put docstrings in functions to provide help for that function.
- Specify default values for parameters when defining a function using
name=valuein the parameter list. - Parameters can be passed by matching based on name, by position, or by omitting them (in which case the default value is used).
- Put code whose parameters change frequently in a function, then call it with different parameter values to customize its behavior.