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Matt Layman @mblayman

mattlayman.com

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You, yeah, you reading the presenter notes. You can run this presentation by running:

$ python -m SimpleHTTPServer

from this directory in a terminal.

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??? Topics we'll cover

• specific style guidance (list comprehension, with statement, property decorator)
• general guidance (built-ins, standard library, pep8)

Key question to answer. But first...

• Who are you?
• What's a bit of your background?
• Where are people at with their Python experience?
• What are you hoping to learn?

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Let's talk about lists.

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Python has a few core data types: tuple, list, dictionary

empty = []
numbers = [1, 2, 3, 4, 5]
numbers.append(6)  # Add to a list
len(numbers)  # How many are in the list?
# Lists can mix types.
mixed = [1, 2, 'foo', 'bar']
three = numbers[2]

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Describe the operations.

How would you created a doubled list of numbers?

int size = 5;
int numbers[size] = {1, 2, 3, 4, 5};
int doubled[size];
for (int i = 0; i < size; i++) {
    doubled[i] = 2 * numbers[i];
}

numbers = [1, 2, 3, 4, 5]
doubled = []
size = len(numbers)
i = 0
while i < size:
    doubled.append(2 * numbers[i])
    i += 1

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numbers = [1, 2, 3, 4, 5]
doubled = []
for number in numbers:
    doubled.append(2 * number)

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This is a reasonable solution.

numbers = [1, 2, 3, 4, 5]
doubled = [2 * number for number in numbers]

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7 lines down to 2. Theme of Pythonic code being short code too.

--

??? doubled is created with a list comprehension.

result_list = [
    expression for value in original_list]

--

• List constructor - square brackets

--

• An expression - lambda function that receives a value

--

• An iterator over a list

numbers = [1, 2, 3, 4, 5]
odds = [number for number in numbers
               if number % 2 != 0]

xs = [1, 2, 3, 4, 5]
ys = [1, 2, 3, 4, 5]
pairs = [(x, y) for x in xs
                for y in ys]

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• Of course, we're going to look at... cat pics.

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f = open('kitteh.jpg', 'rb')
cat_pic = f.read()
# Do other stuff with the cat picture.

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• Missing the critical close
• IOError "too many files open"

f = open('kitteh.jpg', 'rb')
cat_pic = f.read()
# Do other stuff with the cat picture.
f.close()

try:
    f = open('kitteh.jpg', 'rb')
    cat_pic = f.read()
    # Do other stuff with the cat picture.
    f.close()
except:
    print('oops, something went wrong.')

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• Who can spot the bug?
• Yuck, please don't catch all exceptions like this.

try:
    f = open('kitteh.jpg', 'rb')
    cat_pic = f.read()
    # Do other stuff with the cat picture.
except:
    print('oops, something went wrong.')
finally:
    f.close()

with open('kitteh.jpg', 'rb') as f:
    cat_pic = f.read()
try:
    # Do other stuff with the cat picture.
except:
    print('oops, something went wrong.')

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Notice that something is absent, where is close?

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--

• When using with, open automatically closes files. --

• Work is passed to a "context manager." --

• A context manager defines __enter__ and __exit__. --

• Examples: files, locks, timers --

• Write your own with contextlib.

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Do your classes look like this?

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class OhMyJava:
    def __init__(self):
        self._foo = ''
    def get_foo(self):
        return self._foo
    def set_foo(self, foo):
        self._foo = foo

oh_my = OhMyJava()
oh_my.set_foo('This is painful.')

class AwwYeah:
    def __init__(self):
        self.foo = ''

--

If you aren't doing anything special, it is safe to set the attribute directly.

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• Setting attributes directly seems to violate encapsulation.
• Encapsulation is supposed to be a core benefit of object oriented design.

class AwwYeah:
    def __init__(self):
        self._bar = ''
    @property
    def foo(self):
        return 'Wow: {}'.format(self._bar)
    @foo.setter
    def foo(self, value):
        self._bar = '{}!'.format(value)

>>> a = AwwYeah()
>>> a.foo = 'Python'
>>> a.foo
'Wow: Python!'

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• Fewer parenthesis means less to comprehend.

• Excluding a setter makes ready only properties. Handy.

• Properties are super useful for computed/derived values. --

• @property transforms obj.foo() to obj.foo --

• Use of property make another decorator available: @foo.setter --

• Computed/derived values.

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--

• Google search: python built in functions --

• Always globally available functions --

• Some favorites:

  • any
  • enumerate
  • print
  • range
  • sorted

>>> checks = [False, False, True, False]
>>> status = any(checks)
>>> status
True

# Bad variable name. Short for slides.
>>> f = ['apple', 'orange', 'kiwi', 'pear']
>>> for idx, fruit in enumerate(f, start=1):
...     print(idx, fruit)
...
(1, 'apple')
(2, 'orange')
(3, 'kiwi')
(4, 'pear')

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• Default start is 0.

>>> print('Hello World')
Hello World
>>> import sys
>>> print('An error!', file=sys.stderr)
An error!

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• This is one of the big difference between Python 2 and 3.
• In Python 2, print was a keyword.
• Now, it's much easier to use keyword arguments and pass to different streams.

>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> range(5, 10)
[5, 6, 7, 8, 9]
>>> range(0, 10, 2)
[0, 2, 4, 6, 8]

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• Step is very infrequently used.

>>> numbers = [5, 2, 3, 1, 4]
>>> ordered = sorted(numbers)
>>> ordered
[1, 2, 3, 4, 5]
>>> numbers
[5, 2, 3, 1, 4]
>>> numbers.sort()
>>> numbers
[1, 2, 3, 4, 5]

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• Sorted in new list.
• In place sorting

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• The language wants to enable you by including the things you'll probably need anyway. --

• Python has a huge standard library. --

• There are tools for:

  • Manipulating files (os.path) --

  • Matching patterns (re) --

  • Handling dates and times (datetime) --

  • Creating CLIs (argparse) --

  • Working with CSV (csv) --

  • and tons more!

• list comprehensions
• the with statement
• the property decorator
• built-ins
• the standard library

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Pythonic code is code that takes advantage of the features of the Python language and feels more natural than implementing language X in Python.

• https://docs.python.org/3/tutorial/datastructures.html
• https://docs.python.org/3/library/functions.html
• https://docs.python.org/3/library/index.html

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Matt Layman @mblayman

mattlayman.com