- How can I define new functions?
- What's the difference between defining and calling a function?
- What happens when I call a function?
- 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 written code to draw some interesting features in our inflammation data, loop over all our data files to quickly draw these plots for each of them, and have Python make decisions based on what it sees in our data. But, our code is getting pretty long and complicated; what if we had thousands of datasets, and didn't want to generate a figure for every single one? Commenting out the figure-drawing code is a nuisance. Also, what if we want to use that code again, on a different dataset or at a different point in our program? 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, and Python provides for this by letting us define things called 'functions' - a shorthand way of re-executing longer pieces of code.
Let's start by defining a function fahr_to_kelvin that converts temperatures from Fahrenheit to Kelvin:
def fahr_to_kelvin(temp):
return ((temp - 32) * (5/9)) + 273.15
The function definition opens with the keyword def followed by the
name of the function and a parenthesized list of parameter names. The
body of the function --- the
statements that are executed when it runs --- is indented below the
definition line.
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.
fahr_to_kelvin(32)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:
print('freezing point of water:', fahr_to_kelvin(32))
print('boiling point of water:', fahr_to_kelvin(212))We've successfully called the function that we defined, and we have access to the value that we returned.
We are using Python 3, where division always returns a floating point number:
$ python3 -c "print(5/9)"0.5555555555555556Unfortunately, this wasn't the case in Python 2:
5/90If you are using Python 2 and want to keep the fractional part of division you need to convert one or the other number to floating point:
float(5)/90.5555555555565/float(9)0.5555555555565.0/90.5555555555565/9.00.555555555556And if you want an integer result from division in Python 3, use a double-slash:
4//223//21
Now that we've seen how to turn Fahrenheit into Kelvin, it's easy to turn Kelvin into Celsius:
def kelvin_to_celsius(temp_k):
return temp_k - 273.15
print('absolute zero in Celsius:', kelvin_to_celsius(0.0))What about converting Fahrenheit to Celsius? We could write out the formula, but we don't need to. Instead, we can compose the two functions we have already created:
def fahr_to_celsius(temp_f):
temp_k = fahr_to_kelvin(temp_f)
result = kelvin_to_celsius(temp_k)
return result
print('freezing point of water in Celsius:', fahr_to_celsius(32.0))This is our first taste of how larger programs are built: we define basic operations, then combine them in ever-large chunks to get the effect we want. Real-life functions will usually be larger than the ones shown here --- typically half a dozen to a few dozen lines --- but they shouldn't ever be much longer than that, or the next person who reads it won't be able to understand what's going on.
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 an analyze function that generates our plots:
def analyze(filename):
data = numpy.loadtxt(fname=filename, delimiter=',')
fig = matplotlib.pyplot.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(numpy.mean(data, axis=0))
axes2.set_ylabel('max')
axes2.plot(numpy.max(data, axis=0))
axes3.set_ylabel('min')
axes3.plot(numpy.min(data, axis=0))
fig.tight_layout()
matplotlib.pyplot.show()and another function called detect_problems that checks for those systematics
we noticed:
def detect_problems(filename):
data = numpy.loadtxt(fname=filename, delimiter=',')
if numpy.max(data, axis=0)[0] == 0 and numpy.max(data, axis=0)[20] == 20:
print('Suspicious looking maxima!')
elif numpy.sum(numpy.min(data, axis=0)) == 0:
print('Minima add up to zero!')
else:
print('Seems OK!')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:
for f in filenames[:3]:
print(f)
analyze(f)
detect_problems(f)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.
Once we start putting things in functions so that we can re-use them, we need to start testing that those functions are working correctly. To see how to do this, let's write a function to center a dataset around a particular value:
def center(data, desired):
return (data - numpy.mean(data)) + desiredWe could test this on our actual data, but since we don't know what the values ought to be, it will be hard to tell if the result was correct. Instead, let's use NumPy to create a matrix of 0's and then center that around 3:
z = numpy.zeros((2,2))
print(center(z, 3))That looks right,
so let's try center on our real data:
data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
print(center(data, 0))It's hard to tell from the default output whether the result is correct, but there are a few simple tests that will reassure us:
print('original min, mean, and max are:', numpy.min(data), numpy.mean(data), numpy.max(data))
centered = center(data, 0)
print('min, mean, and max of centered data are:', numpy.min(centered), numpy.mean(centered), numpy.max(centered))That seems almost right: the original mean was about 6.1, so the lower bound from zero is now about -6.1. The mean of the centered data isn't quite zero --- we'll explore why not in the challenges --- but it's pretty close. We can even go further and check that the standard deviation hasn't changed:
print('std dev before and after:', numpy.std(data), numpy.std(centered))Those values look the same, but we probably wouldn't notice if they were different in the sixth decimal place. Let's do this instead:
print('difference in standard deviations before and after:', numpy.std(data) - numpy.std(centered))Again, the difference is very small. It's still possible that our function is wrong, but it seems unlikely enough that we should probably get back to doing our analysis. We have one more task first, though: we should write some documentation for our function to remind ourselves later what it's for and how to use it.
The usual way to put documentation in software is to add comments like this:
# center(data, desired): return a new array containing the original data centered around the desired value.
def center(data, desired):
return (data - numpy.mean(data)) + desiredThere's a better way, though. 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:
def center(data, desired):
'''Return a new array containing the original data centered around the desired value.'''
return (data - numpy.mean(data)) + desiredThis is better because we can now ask Python's built-in help system to show us the documentation for the function:
help(center)A string like this is called a docstring. We don't need to use triple quotes when we write one, but if we do, we can break the string across multiple lines:
def center(data, desired):
'''Return a new array containing the original data centered around the desired value.
Example: center([1, 2, 3], 0) => [-1, 0, 1]'''
return (data - numpy.mean(data)) + desired
help(center)We have passed parameters to functions in two ways:
directly, as in type(data),
and by name, as in numpy.loadtxt(fname='something.csv', delimiter=',').
In fact, we can pass the filename to loadtxt without the fname=:
numpy.loadtxt('inflammation-01.csv', delimiter=',')but we still need to say delimiter=:
numpy.loadtxt('inflammation-01.csv', ',')To understand what's going on,
and make our own functions easier to use,
let's re-define our center function like this:
def center(data, desired=0.0):
'''Return a new array containing the original data centered around the desired value (0 by default).
Example: center([1, 2, 3], 0) => [-1, 0, 1]'''
return (data - numpy.mean(data)) + desiredThe key change is that the second parameter is now written desired=0.0 instead of just desired.
If we call the function with two arguments,
it works as it did before:
test_data = numpy.zeros((2, 2))
print(center(test_data, 3))But we can also now call it with just one parameter,
in which case desired is automatically assigned the default value of 0.0:
more_data = 5 + numpy.zeros((2, 2))
print('data before centering:')
print(more_data)
print('centered data:')
print(center(more_data))This is handy: 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:
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)As 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:
print('only setting the value of c')
display(c=77)With that in hand,
let's look at the help for numpy.loadtxt:
help(numpy.loadtxt)There's a lot of information here, but the most important part is the first couple of lines:
loadtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None,
unpack=False, ndmin=0)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:
numpy.loadtxt('inflammation-01.csv', ',')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.
Consider these two functions:
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 numpy.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 numpy.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!
"Adding" two strings produces their concatenation:
'a' + 'b'is'ab'. Write a function calledfencethat takes two parameters calledoriginalandwrapperand 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:print(fence('name', '*'))*name*def fence(original, wrapper): return wrapper + original + wrapper{: .solution}
If the variable
srefers to a string, thens[0]is the string's first character ands[-1]is its last. Write a function calledouterthat returns a string made up of just the first and last characters of its input. A call to your function should look like this:print(outer('helium'))hmdef outer(input_string): return input_string[0] + input_string[-1]{: .solution}
Write a function
rescalethat takes an array as input and returns a corresponding array of values scaled to lie in the range 0.0 to 1.0. (Hint: If$L$ and$H$ are the lowest and highest values in the original array, then the replacement for a value$v$ should be$(v-L) / (H-L)$ .)def rescale(input_array): L = numpy.min(input_array) H = numpy.max(input_array) output_array = (input_array - L) / (H - L) return output_array{: .solution}
Run the commands
help(numpy.arange)andhelp(numpy.linspace)to see how to use these functions to generate regularly-spaced values, then use those values to test yourrescalefunction. Once you've successfully tested your function, add a docstring that explains what it does.'''Takes an array as input, and returns a corresponding array scaled so that 0 corresponds to the minimum and 1 to the maximum value of the input array. Examples: >>> rescale(numpy.arange(10.0)) array([ 0. , 0.11111111, 0.22222222, 0.33333333, 0.44444444, 0.55555556, 0.66666667, 0.77777778, 0.88888889, 1. ]) >>> rescale(numpy.linspace(0, 100, 5)) array([ 0. , 0.25, 0.5 , 0.75, 1. ]) '''{: .solution}
Rewrite the
rescalefunction so that it scales data to lie between 0.0 and 1.0 by default, but will allow the caller to specify lower and upper bounds if they want. Compare your implementation to your neighbor's: do the two functions always behave the same way?def rescale(input_array, low_val=0.0, high_val=1.0): '''rescales input array values to lie between low_val and high_val''' L = numpy.min(input_array) H = numpy.max(input_array) intermed_array = (input_array - L) / (H - L) output_array = intermed_array * (high_val - low_val) + low_val return output_array{: .solution}
What does the following piece of code display when run - and why?
f = 0 k = 0 def f2k(f): k = ((f-32)*(5.0/9.0)) + 273.15 return k f2k(8) f2k(41) f2k(32) print(k)259.81666666666666 287.15 273.15 0
kis 0 because thekinside the functionf2kdoesn't know about thekdefined outside the function. {: .solution}
Given the following code:
def numbers(one, two=2, three, four=4): n = str(one) + str(two) + str(three) + str(four) return n print(numbers(1, three=3))what do you expect will be printed? What is actually printed? What rule do you think Python is following?
1234one2three41239SyntaxErrorGiven that, what does the following piece of code display when run?
def func(a, b=3, c=6): print('a: ', a, 'b: ', b, 'c:', c) func(-1, 2)
a: b: 3 c: 6a: -1 b: 3 c: 6a: -1 b: 2 c: 6a: b: -1 c: 2Attempting to define the
numbersfunction results in4. SyntaxError. The defined parameterstwoandfourare given default values. Becauseoneandthreeare not given default values, they are required to be included as arguments when the function is called and must be placed before any parameters that have default values in the function definition.The given call to
funcdisplaysa: -1 b: 2 c: 6. -1 is assigned to the first parametera, 2 is assigned to the next parameterb, andcis not passed a value, so it uses its default value 6. {: .solution}
Which of the following would be printed if you were to run this code? Why did you pick this answer?
7 33 73 37 7a = 3 b = 7 def swap(a, b): temp = a a = b b = temp swap(a, b) print(a, b)
3, 7is correct. Initiallyahas a value of 3 andbhas a value of 7. When the swap function is called, it creates local variables (also calledaandbin this case) and trades their values. The function does not return any values and does not alteraorboutside of its local copy. Therefore the original values ofaandbremain unchanged. {: .solution}
Revise a function you wrote for one of the previous exercises to try to make the code more readable. Then, collaborate with one of your neighbors to critique each other's functions and discuss how your function implementations could be further improved to make them more readable.
- Define a function using
def name(...params...). - The body of a function must be indented.
- Call a function using
name(...values...). - Numbers are stored as integers or floating-point numbers.
- Integer division produces the whole part of the answer (not the fractional part).
- Each time a function is called, a new stack frame is created on the call stack to hold its parameters and local variables.
- Python looks for variables in the current stack frame before looking for them at the top level.
- Use
help(thing)to view help for something. - 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).
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