Slides from my free functional Python webinar, given on October 22nd, 2014. Discussion included functional programming as a perspective, passing functions as data, and writing programs that take functions as parameters. Includes (at the end) a coupon for my new ebook, Practice Makes Python.

1. Functional Programming
in Python
Reuven M. Lerner, PhD • reuven@lerner.co.il
October 22nd, 2014

2. Who am I?
• Programmer, consultant, developer, trainer
• Long-time Python (+Ruby) user
• Linux Journal columnist
• Author Practice Makes Python!
• PhD in Learning Sciences from Northwestern
• Read (lots) more at http://lerner.co.il/
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4. Functional programming
• A different way of thinking about programming
• A different way of writing programs
• A different way of approaching problems
• Often provides new, clean, elegant solutions…
• … but again, it requires that you think about things
very differently.
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5. 5
Input
Function
Output
No external state
changes in this function…
… and if you can avoid
internal state, even better!

6. Why work this way?
• Simpler functions — easier to write and maintain
• Easer to test functions, and predict their results
• Easier to combine functions in new, different ways
• You can split work across threads, processors, or
even machines without any ill effects
• Certain things can be expressed easily, elegantly
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7. The FP perspective
Input f1 f2 f3 f4 f5 Output
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8. OO or functional?
• Python supports both styles
• It’s common to mix them in one program
• My current approach: Use objects in general, but
functional techniques inside of methods
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9. Sequences and iterables
• There are three built-in sequences in Python:
strings, tuples, and lists
• Many other objects are "iterable," meaning that you
can stick them into a "for" loop and get one item at
a time
• Examples: files and dicts
• This discussion mainly has to do with iterables
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10. Where are iterables?
• Strings, lists, and tuples (obviously)
• Dicts, sets, and files
• Complex data structures: Lists of lists, lists of dicts,
dicts of dicts (and others)
• Results from database queries
• Information from a network feed
• Many, many objects return iterables
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11. Transformation
• You often want to turn one iterable into another
• For example, you might want to turn the list
[0,1,2,3,4] # range(5)
• into the list
[0,1,4,9,16]
• We can transform the first list into the other by
applying a Python function.
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12. Each list
item
def square(x):
return x*x
New list
item
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13. The usual solution
>>>> input = range(5)
>>>> def square(x):
return x*x
>>> output = [ ]
>>> for x in input:
output.append(square(x))
>>> output
[0, 1, 4, 9, 16]
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14. What's wrong with this?
• Nothing is wrong.
• But functional programming looks at this, and says:
• Why create a new variable ("output")?
• Why are you building it, one step at a time?
• Why are you modifying the state of "output"?
• Why do it in such a clumsy way?
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15. The elegant way
"I want to apply my function, square, to each and
every element of the input list, and get a list back."
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16. Input list
def square(x):
return x*x
Output list
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17. List comprehensions
• In Python, we do this with a "list comprehension":
[square(x) for x in range(5)]
• Or if you prefer:
[x*x for x in range(5)]
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18. Ints to strings
• I can't say
', '.join(range(5))
• because str.join's input must contain strings.
• Solution:
', '.join([str(x) for x in range(5)])
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19. Lowercase all words
• I can transform a sentence into all lowercase:
words = 'This is a sentence for my Python
class'.split()
[word.lower() for word in words]
• Or even:
' '.join([word.lower() for word in words])
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20. Filenames to files
• I can get a list of filenames from os.listdir:
os.listdir('/etc')
• I can get a file object for each of these:
[open('/etc/' + filename)
for filename in os.listdir('/etc')]
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21. File contents
• If I want to get the names of users on my Unix
system, I can say
[ line.split(":")[0]
for line in open('/etc/passwd') ]
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22. Filtering
• If I want to get the names of users on my Unix
system, I can say
[ line.split(":")[0]
for line in open('/etc/passwd')
if line[0] != '#' ]
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23. Pig Latin!
def plword(word):
vowels = 'aeiou'
if word[0] in vowels:
return word + 'way'
else:
return word[1:] + word[0] + 'ay'
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24. Translation
' '.join([plword(word)
for word in open('column-215') ])
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25. Bottom line
• Comprehensions are Python's answer to the
traditional functions "map" and "filter".
• Once you start to think in terms of "mapping" one
sequence to another, you'll see this pattern
everywhere.
• You'll reduce the number of assignments you make.
Your programs will be shorter and easier to
understand. And you'll be able to stack these
manipulations, without worrying about side effects.
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26. Functions
• Where are the functions in functional programming?
• Let's write some functions! Let's use some
functions!
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27. Calling functions
• We call functions in Python with ()
• No parentheses — no function call!
x = len('abc')
type(x)
int
x = len
type(x)
builtin_function_or_method
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28. Parentheses
• In Python, something that can be executed with
parentheses is known as "callable".
• 'a'() will result in an error, that a string isn't
"callable."
• Normally, we say that functions and classes are
callable. How do we know? They have a __call__
attribute!
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29. Dispatch table
def foo(): return "foo"
def bar(): return "bar"
d = {'foo':foo, 'bar':bar}
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30. Sorting
• We know that we can use list.sort to sort a list:
words= 'This is a sentence for my online
Python class'.split()
words.sort()
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31. Changing sort's behavior
• The optional "key" parameter lets you pass a
function that changes sort's behavior
• Define a function, and pass it to "key".
• Or use an existing function:
words.sort(key=str.lower)
words.sort(key=len)
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32. The real lesson:
You can do it, too!
• Take a function as a parameter
• Invoke the function within your function
• Now you have a more generic function, whose
behavior can be modified!
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33. Function
Use
input 2
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Input 1
Output
input 2

34. Example
• Let's implement transform_values!
>>> d = {'a':1, 'b':2, 'c':3}
>>> transform_values(lambda x: x*x, d)
{'a': 1, 'b': 4, 'c': 9}
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35. Summary
• Functions are data, too! Treating them as such
makes your programs more flexible, generic, and
easy to understand
• Python's comprehensions are a modern version of
classic "map" and "filter" functions.
• Composing functions isn't the only solution, but it's
a really flexible and useful one
• It becomes easier with practice!
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36. Questions? Comments?
And remember…
• Early-bird discount on my book!
• http://lerner.co.il/practice-makes-python
• Use the WEBINAR coupon code for 10% off!
• Courses are at http://lerner.co.il/courses
• Contact me: reuven@lerner.co.il
• Follow me: @reuvenmlerner
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