Just like any other form of art, Programming can always be improved. In daily grind to deliver working software we might forget details and "JUST DO IT". In our quest to improve our python coding we've come across couple of good Python videos that will help you take you code to next level. Following is summary of two talks Designing Poetic APIs by Erik Rose and Transforming Code into Beautiful, Idiomatic Python by Raymond Hettinger. On twitter you can find them on @erikrose and @raymondh

Designing Poetic APIs by Erik Rose

Eric Rose's Github has more fascinating stuff. His talk is a good investment of 37 minutes of your time. Following is summary we've compiled from the video:

Don't Be An Architecture Astronaut

  • Best libraries are extracted and not invented


  • User's spend 90% of time calling other people's APIs
  • Sticking to commonly used conventions can help
  • E.g. Macintosh Human Interface Design is a good example of how consistency in terms of Keyboards and Menus were extracted
  • API Design is similar to UI Design and principles from UI Design can be applied to API Design
  • get(key, default) is better than fetch(default, key) because it sticks with current conventions of Python
  • Layout all options and select the best one
  • If you're going to be wiered, be self consistent
  • Red flags for Consistency:
    • Frequent references to your own documentation
    • Feel syntactically correct {Make sure novely pays off than shows off}


  • Try to make common things shorts
  • Red flags for Brevity:
    • Copy and Pasting same piece of code frequently
    • Typing something irrelevant
    • Long arg list, suggest lack of sane defaults


  • Being able to reuse code and use in different setting
  • Aka Flexibility, Loose Coupling
  • Red flags for Composability
    • Classes with lot of state
    • Deep inheritance heirarchy
    • Violations of Law of Dementer
    • Mocking in test {too many dependencies as means of Mocking is not a good idea}
    • Options {a lot of them}

Plain Data

  • Avoid using complex/custom data structures. Use as many as standards data structures
  • Try to use as many build-in data structures
  • Red flags for Plain Data
    • If users take your data and convert it to "something", output should be that "something"
    • Instantiating objects to pass to another one
    • Rewriteing language-provided things


  • Allow users to spend more time in groviness
  • Avoid nonsense representations
  • Fail shallowly
  • Resource acquisition in initialization - {Required explicitness, error if no required argument are provided}
  • Compelling Examples
  • Red falgs for Groviness
    • Representable nonsense
    • Invariants that aren't
    • Lack of clear starting point
    • Long, complicated documentation


  • Walls are there for preveting hurting yourself and others
  • Red flags for Safety
    • Docs that say "remember to"... or "make sure you..."
    • Surprisingly people will blame themselves {Which points to bad design}

Transforming Code into Beautiful, Idiomatic Python by Raymond Hettinger

RDH Talks is interesting list. His talk is another interesting one. Here is the summary

When you see this, do that instead

  • Replace traditional index manipulation with Python's core looping idioms
  • Learn advanced techniques with for-else clause and two arguments of iter()
  • Improve your craftsmanship and aim for clean, fast, idiomatic Python code
  • Looping over a range of numbers
    for i in [0, 1, 2, 3, 4, 5]:
    print i**2

    Better way

    for i in range(6):
    print i**2

    Faster way

    Ugly way, in Python 3K this is renamed to xrange

    for i in xrange(6):
    print i**2
    - Looping over a collectionpython
    colors = ['red', 'green', 'blue', 'yellow']

    for i in range(len(colors)):
    print colors[i]

    Better way

    for color in colors:
    print color
    - Looping backwardspython
    colors = ['red', 'green', 'blue', 'yellow']

    for i in range(len(colors)-1, -1, -1):
    print colors[i]

    This is faster and beautiful

    for color in reversed(colors):
    print color
    - Looping over collection and indiciespython
    colors = ['red', 'green', 'blue', 'yellow']

    for i in range(len(colors)):
    print i, colors[i]

    Better way

    for i, color in enumerate(color):
    print i, color
    - Looping over two collections at oncepython
    names = ['raymond', 'rachel', 'matthew']
    colors = ['red', 'green', 'blue', 'yellow']

    n = min(len(names), len(colors))
    for i in range(n):
    print names[i], "->", colors[i]

    Better way

    But takes more memory

    for name, color in zip(names, colors):
    print name, "->", color

    Idiomatic way

    Consumes less memory

    for name, color in izip(names, colors):
    print name, "->", color
    - Looping over sorted orderpython
    colors = ['red', 'green', 'blue', 'yellow']

    for color in sorted(colors):
    print color

    for color in sorted(colors, reverse=True):
    print color
    - Custom sort orderpython
    colors = ['red', 'green', 'blue', 'yellow']

    def compare_length(c1, c2):
    if len(c1) < len(c2): return -1
    if len(c1) > len(c2): return 1
    return 0

    print sorted(colors, cmp=compare_length)

    Better way

    print sorted(colors, key=len)
    - Call a function until a sentinal value. Note: [Partial](https://docs.python.org/2/library/functools.html) take a function with one or more arguments and returns a function with fewer argument.python
    blocks = []
    while True:
    block = f.read(32)
    if block == '':

    Better way

    blocks = []

    iter function takes two arguments

    1. Function you call over and over again

    2. Sentinel value

    for block in iter(partial(f.read, 32), ''):
    - Distinguish multiple exit point in loopspython
    def find(seq, target):
    found = False
    for i, value in enumerate(seq):
    if value == target:
    found = True
    if not found:
    return -1
    return i

    Better way

    def find(seq, target):
    for i, value in enumerate(seq):
    if value == target:
    found = True
    return -1
    return i

Dictionary Skills

  • Mastering dictionaries is fundamental Python skill
  • They are fundamental tool for expressing relationships, linking, counting and grouping
  • Looping over dictionary keys
    d = {'matthew': 'blue', 'rachel': 'green', 'raymond': 'red'}

    for k in d:
    print k
    - **DONT mutate while iterating over things** - Looping over dictionary keys and valuespython

    for k, v in d.items():
    print k, "->", v

    Better way

    for k,v in d.iteritems():
    print k, "->", v
    - Construct a dictionary in pairspython
    names = ['matthew', 'rachel', 'raymond']
    colors = ['blue', 'green', 'raymond']

    d = dict(izip(names, colors))

    d = dict(enumerate(names))
    - Counting with dictionariespython
    colors = ['red', 'green', 'red', 'blue' 'green', 'red']

    d = {}
    for color in colors:
    if color not in d:
    d[color] = 0
    d[color] += 1

    Better way

    d = {}
    for color in colors:
    d[color] = d.get(color, 0) + 1

    Idiomatic way

    from collections import defaultdict
    d = defaultdict(int)

    for color in colors:
    d[color] += 1
    - Grouping with dictionariespython
    names = ['raymond', 'rachel', 'matthew', 'roger', 'betty', 'melissa', 'judith', 'charlie']
    d = {}
    for name in names:
    key = len(name)
    if key not in d:
    d[key] = []

    Better way

    d = {}
    for name in names:
    key = len(name)
    d.setdefault(key, []).append(name)

    Better way

    d = defaultdict(list)
    for name in names:
    key = len(name)
    - Is a dictionary popitem() atomic?python
    d = {'matthew': 'blue', 'rachel': 'green', 'raymond': 'red'}

    Pop item is atomic so it can be used between threads

    while d:
    key, value = d.popitem()
    print key, "->", value
    - Linking dictionaires (Or chaining them)python

    Some dictionary

    default = {}
    d = defaults.copy()

    d.ChainMap(command_line_args, os.environ, defaults)

Improving Clarity

  • Positional arguments and indicies are nice
  • Keywords and names are better
  • The first way is convinient for the computers
  • The second corresponds to how human's think

  • Clarify function calls with keyword arguments (Good activity for someone who doesn't know the codebase)
    twitter_search('@obama', False, 20, True)

    More readable

    twitter_search('@obama', retweets=False, numtweets=20, popular=True)
    - Clarify multiple return values with named tuplespython
    (0, 4)

    This is more readable and result of NamedTuple

    TestResults(failed=0, attempted=4)

    How named tuples are used

    TestResults = namedtuple('TestResult', ['failed', 'attempted'])
    - Unpacking Sequencespython
    p = 'a', 'b', 'c', d
    fname = p[0]
    lname = p[1]
    age = p[2]
    email = p[3]

    fname, lname, age, email = p

    - Update multiple state variablespython
    def fibonacci(n):
    x = 0
    y = 1
    for i in range(n):
    print x
    t = y
    y = x + y
    x = t

    Better version

    def fibonacci(n):
    x, y = 0, 1
    for i in range(n):
    print x
    x, y = y, x+y

Tuple packing and unpacking

  • Don't under-estimate the advantages of updating state variables at the same time
  • It eliminates an entire class of errors due to out-of-order updates
  • It allows high level thinking: "chunking"

Decorators and Context Managers

  • Helps separate business logic and administrative logic
  • Clean, beautiful tools for factoring code and imprving code reuse
  • Good naming is essential
  • Remember the Spiderman rule: With great power comes great responsibility
  • Using decorators to factor-out administrative logic
    def web_lookup(url, saved={}):
    if url in saved:
    return saved[url]
    page = urllib.urlopen(url).read()
    saved[url] = page
    return page

    Better way

    def web_lookup(url):
    return urllib.urlopen(url).read()

    Logic for decorator

    def cache(func):
    saved = {}
    def newfunc(args):
    if args in saved:
    return newfunc(
    result = func(args)
    args] = result
    return result
    return newfunc
    - How to open and close filespython
    f = open('data.txt')
    data = f.read()

    Better way, closes file automatically

    with open('data.txt') as f:
    data = f.read()
    - Factor-out temporary contextpython
    except OSError:

    Better wat

    with ignored(OSError):

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