Most of us programmers go through technical interviews every once in a while. At other times, many of us sit on the opposite side of the table running these interviews. Stakes are high, emotions run strong, intellectual pressure builds up. I have found that an unfortunate code review may turn into something similar to a harsh job interview.
While it is theoretically in the best interest of the whole team to end up with high quality code, variations in individual’s technical background, cultural differences, preconceptions built up on previous experience, personality quirks, and even temper may lure people into a fierce fight over relatively unimportant matters.
Consider an imaginary pull request. There we typically have two actors: the author and code reviewers. Sometimes authors tend to overestimate the quality of their code which provokes them to be overly defensive and possibly even hostile to any argument. People reviewing the code may find themselves being in a position of power to judge author’s work. Once the actors collide over a matter where they take orthogonal and sufficiently strong sides, all is fair in love and war.
Another interesting phenomena I encountered while reviewing Python code can probably be attributed to Python’s low barrier to entry for newcomers. Programmers switching over from other languages bring along customs and idioms they are used to in their “mother tongue”. I can usually figure out from their Python code whether the author is a former Java, Perl, or Bash programmer. As much as I admire other technologies and expertise, I believe it is most efficient and enjoyable to code in harmony with the language rather than stretching it beyond its intended design.
In this article I’ll focus on my personal experience in authoring and reviewing Python code from both psychological and technical perspectives. And I’ll do so keeping in mind the ultimate goal of striking a balance between code reviews being enjoyable and technically fruitful.
Why we review code
The most immediate purpose of a code review is to make code better by focusing more eyeballs on it. But code review seems to be a lot more than that!
It is also a way for the engineers to communicate, learn and even socialize over a meaningful and mutually interesting topic. In a team where both senior and junior engineers work together, a code review provides the opportunity for the junior engineers to observe masters at work and learn from them.
Seniors, in turn, get a chance to coach fellow engineers, be challenged and thus prove their authority (which is healthy). Everyone can see the problem from different perspectives, which ultimately contributes to a better outcome.
For mature Pythonistas coding with newbies, code review is a way to teach them how we do things in Python with the goal of creating idiomatic Python coders out of them.
For the greater good
The best we can do on the psychological side is to, as an author, relinquish our emotional attachment to our code and, as a reviewer, consciously restrain ourselves from attacking other authors’ ideas, focusing instead on mentoring them.
For a review to be a productive and relatively comfortable experience, a reviewer should stay positive, thankful, honestly praise the author’s work and talent in a genuine matter. Suggested changes should be justified by solid technical grounds, and never by the reviewer’s personal taste.
For authors it may help to keep reminding themselves how much time and effort it might have taken for reviewers to work with the author’s code — their feedback is precious!
As idealistic as it sounds, my approach aims at downplaying my ego by optimizing for a healthier team and an enjoyable job. I suspect that it may come at the cost of compromised quality of the code we collectively produce, but to me that is worth it. My hope here is that even if we do merge sub-optimal code at times, we will eventually learn from that and re-factor later. That’s a much cheaper cost compared to ending up with a stressed, despaired and demotivated colleague.
When I’m an author
As an author, I’m not taking Pull Requests (PRs) lightly. My day’s worth of code is likely to keep fellow reviewers busy for a good couple of hours. I know that it’s a hard and expensive endeavor. Proper code review may require my team mates to reverse engineer business logic behind a change, trace code execution, conduct thought experiments, and search for edge cases. I do keep that in mind and feel grateful for their time.
I try to keep my changes small. The larger the change is, the more effort it would take for reviewers to finish the review. That gives smaller, isolated changes a better chance for quality treatment, while huge and messy blobs of diffs risk turning a blind eye on my PR.
Well-debugged code accompanied with tests, properly documented changes complying with team policies — those PR qualities are signs of respect and care towards reviewers. I feel myself more confident once I run a quick self code review against my prospective PR prior to submitting it to fellow engineers.
When I’m a reviewer
To me, the most important qualities of code is to be clean and as Pythonic as circumstances permit.
Clean code tends to be well-structured, where logically distinct parts are isolated from each other via clearly visible boundaries. Ideally, each part is specialized on solving a single problem. As Zen of Python puts it: “If the implementation is easy to explain, it may be a good idea”.
Signs of clear code include self-documented functions and variables describing problem entities, not implementation details.
Readability counts, indeed, though I would not sweat full PEP8 compliance where it becomes nitpicking and bikesheding.
I praise the author’s coding on the shoulders of giants — abstracting problems into canonical data structures/algorithms and working from there. That gives me the warm feeling of the author belonging to the same trade guild as myself, and confidence that we both know what to expect from the code.
When I’m a Pythonista
The definition of code being Pythonic tends to be somewhat vague and subjective. It seems to be influenced by one’s habits, taste, picked up idioms and language evolution. I keep that in mind and restrain myself from evangelizing my personal perception of what’s Pythonic towards fellow Pythonistas.
Speaking from my experience in the field, let me offer the reader a handful of code observations I have encountered, along with suggestions leveraging features that are native to Python of which people with different backgrounds may be unaware.
Justified programming model
People coming from Java tend to turn everything into a class. That’s probably because Java heavily enforces the OOP paradigm. Python programmers enjoy a freedom of picking a programming model that is best suited for the task.
The choice of object-based implementations look reasonable to me when there is a clear abstraction for the task being solved. Statefulness and duck-typed objects are another strong reason for going the OOP way.
If the author’s priority is to keep related functions together, pushing them to a class is an option to consider. Such classes may never need instantiation, though.
Free-standing functions are easy to grasp, concise and light. When a function does not cause side-effects, it’s also good for functional programming.
Pythonic loops
Folks coming from C might feel at home with index-based while loops:
# Non-Pythonic
choices = ['red', 'green', 'blue']
i = 0
while i < len(choices):
print('{}) {}'.format(i, choices[i]))
i += 1
or with for loops like this:
# Non-Pythonic
choices = ['red', 'green', 'blue']
for i in range(len(choices)):
print('{}) {}'.format(i, choices[i]))
When the index is not required, a more Pythonic way would be to run a for loop over a collection:
# Pythonic!
choices = ['red', 'green', 'blue']
for choice in choices:
print(choice)
Otherwise enumerate the collection and run a for loop over the enumeration:
# Pythonic!
choices = ['red', 'green', 'blue']
for idx, choice in enumerate(choices):
print('{}) {}'.format(idx, choice))
As a side note, Python’s for loop is quite different from what we have in C, Java or JavaScript. Technically, it’s a foreach loop.
What if we have many collections to loop over? As naive as it could get:
# Non-Pythonic
preferences = ['first', 'second', 'third']
choices = ['red', 'green', 'blue']
for i in range(len(choices)):
print('{}) {}'.format(preferences[i], choices[i]))
But there is a better way — use zip:
# Pythonic!
preferences = ['first', 'second', 'third']
choices = ['red', 'green', 'blue']
for preference, choice in zip(preferences, choices):
print('{}) {}'.format(preference, choice))
Comprehensions, no tiny loops
Even a perfectly Pythonic loop can be further improved by turning it into a list or dictionary comprehension. Consider quite a mundane for-loop building a sub-list on a condition:
# Non-Pythonic
oyster_months = []
for month in months:
if 'r' in month:
oyster_months.append(month)
List comprehension reduces the whole loop into a one-liner!
# Pythonic!
oyster_months = [month for month in months if 'r' in month]
Dictionary comprehension works similarly, but for mapping types.
Readable signatures
Differing from many languages, in Python, names of function parameters are always part of function signature:
>>> def count_fruits(apples, oranges):
... return apples + oranges
...
>>> count_fruits(apples=12, oranges=21)
>>> count_fruits(garlic=14, carrots=12)
TypeError: count_fruits() got an unexpected keyword argument 'garlic'
The outcome is twofold: a caller can explicitly refer to a parameter name to improve code readability. The function’s author should be aware of callers possibly binding to a once-announced name and restrain from changing names in public APIs.
At any rate, passing named parameters to the functions we call adds to code readability.
Named tuples for readability
Wrapping structured stuff into a tuple is a recipe for communicating multiple items with a function. Trouble is that it quickly becomes messy:
# Non-Pythonic
>>> team = ('Jan', 'Viliam', 'Ilya')
>>> team
('Jan', 'Viliam', 'Ilya')
>>> lead = team[0]
Named tuples simply add names to tuple elements so that we can enjoy object notation for getting hold of them:
# Pythonic!
>>> Team = collections.namedtuple('Team', ['lead', 'eng_1', 'eng_2'])
>>> team = Team('Jan', 'Viliam', 'Ilya')
>>> team
Team(lead='Jan', eng_1='Viliam', eng_2='Ilya')
>>> lead = team.lead
Using named tuples improves readability at the cost of creating an extra class. Keep in mind, though, that namedtuple factory functions create a new class by exec‘ing a template — that may slow things down in a tight loop.
Exceptions, no checks
Raising an exception is a primary vehicle for communicating errors in a Python program. It’s easier to ask for forgiveness than permission, right?
# Non-Pythonic
if resource_exists():
use_resource()
# Pythonic!
try:
use_resource()
except ResourceDoesNotExist:
...
Beware likely failing exceptions in tight loops, though — those may slow down your code.
It is generally advisable to subclass built-in exception classes. That helps to clearly communicate errors that are specific to our problem and differentiate errors that bubble up to our code from other, less expected failures.
Ad-hoc namespaces
When we encounter colliding variables, we might want to isolate them from each other. The most obvious way is to wrap at least one of them into a class:
# Non-Pythonic
class NS:
pass
ns = NS()
ns.fruits = ['apple', 'orange']
But there is a handy Pythonic shortcut:
# Pythonic!
ns = types.SimpleNamespace(fruits=['apple', 'orange'])
The SimpleNamespace object acts like any class instance — we can add, change or remove attributes at any moment.
Dictionary goodies
Python dict is a well-understood canonical data type much like a Perl hash or a Java HashMap. In Python, however, we have a few more built-in features like returning a value for a missing key:
>>> dict().get('missing key', 'failover value')
'failover value'
Conditionally setting a key if it’s not present:
>>> dict().setdefault('key', 'new value')
'new value'
>>> d = {'key': 'old value'}
>>> d.setdefault('key', 'new value')
'old value'
Or automatically generate an initial value for missing keys:
>>> d = collections.defaultdict(int)
>>> d['missing key'] += 1
>>> d['missing key']
1
A dictionary that maintains key insertion order:
>>> d = collections.OrderedDict()
>>> d['x'] = 1
>>> d['y'] = 1
>>> list(d)
['x', 'y']
>>> del d['x']
>>> d['x'] = 1
>>> list(d)
['y', 'x']
Newcomers may not be aware of these nifty little tools — let’s tell them!
Go for iterables
When it comes to collections, especially large or expensive ones to compute, the concept of iterability kicks right in. To start with, a for loop implicitly operates over Iterable objects:
for x in [1, 2, 3]:
print(x)
for line in open('myfile.txt', 'r'):
print(line)
Many built-in types are already iterable. User objects can become iterable by supporting the iterator protocol:
class Team:
def __init__(self, *members):
self.members = members
self.index = 0
def __iter__(self):
return self
def __next__(self):
try:
return self.members[self.index]
except IndexError:
raise StopIteration
finally:
self.index += 1
so they can be iterated over a loop:
>>> team = Team('Jan', 'Viliam', 'Ilya')
>>> for member in team:
... print(member)
...
Jan
Viliam
Ilya
as well as in many other contexts where an iterable is expected:
>>> team = Team('Jan', 'Viliam', 'Ilya')
>>> reversed(team)
['Ilya', 'Viliam', 'Jan']
Iterable user functions are known as generators:
def team(*members):
for member in members:
yield member
>>> for member in team('Jan', 'Viliam', 'Ilya'):
... print(member)
...
...
Jan
Viliam
Ilya
The concept of an iterable type is firmly built into the Python infrastructure and it is considered Pythonic to leverage iterability features.
Besides being handled by built-in operators, there is a collection of functions in the itertools module that are designed to consume and/or produce iterables.
Properties for gradual encapsulation
Java and C++ are particularly famous for promoting object state protection by operating via “accessor” methods (also known as getters/setters). A Pythonic alternative to them is based on the property feature. Unlike Java programmers, Pythonistas do not begin with planting getters and setters into their code. They start out with simple, unprotected attributes:
class Team:
members = ['Jan', 'Viliam', 'Ilya']
team = Team()
print(team.members)
Once a need for protection arises, we turn an attribute into a property by adding access controls into the setter:
class Team:
_members = ['Jan', 'Viliam', 'Ilya']
@property
def members(self):
return list(self._members)
@members.setter
def members(self, value):
raise AttributeError('This team is too precious to touch!')
>>> team = Team()
>>> print(team.members)
['Jan', 'Viliam', 'Ilya']
>>> team.members = []
AttributeError('This team is too precious to touch!',)
Python properties are implemented on top of descriptors which is a lower-level but more universal mechanism to get hold on attribute access.
Context managers to guard resources
It is common for programs to acquire resources, use them, and clean up afterwards. A simplistic implementation might look like this:
# Non-Pythonic
resource = allocate()
try:
resource.use()
finally:
resource.deallocate()
In Python, we could re-factor this into something more succinct, leveraging the context manager protocol:
# Pythonic!
with allocate_resource() as resource:
resource.use()
The expression following a with must support the context manager protocol. Its __enter__ and __exit__ magic methods will be called respectively before and after the statements inside the with block.
Context managers are idiomatic in Python for all sorts of resource control situations: working with files, connections, locks, processes. To give a few examples, this code will ensure that a connection to a web server is closed once the execution runs out of with block:
with contextlib.closing(urllib.urlopen('https://redhat.com')) as conn:
conn.readlines()
The suppress context manager silently ignores the specified exception if it occurs within the body of the with statement:
with contextlib.suppress(IOError):
os.unlink('non-existing-file.txt')
Decorators to add functionality
Python decorators work by wrapping a function with another function. Use cases include memoization, locking, pre/post-processing, access control, timing, and many more.
Consider a straightforward memoization implementation:
# Non-Pythonic
cache = {}
def compute(arg):
if arg not in cache:
cache[arg] = do_heavy_computation(arg)
return cache[arg]
This is where Python decorators come in handy:
def universal_memoization_decorator(computing_func):
cache = {}
def wrapped(arg):
if arg not in cache:
cache[arg] = computing_func(arg)
return cache[arg]
return wrapped
# Pythonic!
@universal_memoization_decorator
def compute(arg)
return do_heavy_computation(arg)
The power of decorators comes from their ability to modify function behavior in a non-invasive and universal way. That opens up possibilities to offload business logic into a specialized decorator and reuse it across the whole codebase.
The Python standard library offers many ready-made decorators. For example, the above memorization tool is readily available in the standard library:
# Pythonic!
@functools.lru_cache()
def compute(arg):
return do_heavy_computation(arg)
Decorators have made their way into public APIs in large projects like Django or PyTest.
Duck typing
Duck typing is highly encouraged in Python for being more productive and flexible. A frequent use-case involves emulating built-in Python types such as containers:
# Pythonic!
class DictLikeType:
def __init__(self, *args, **kwargs):
self.store = dict(*args, **kwargs)
def __getitem__(self, key):
return self.store[key]
...
Full container protocol emulation requires many magic methods to be present and properly implemented. This can become laborious and error prone. A better way is to base user containers on top of a respective abstract base class:
# Extra Pythonic!
class DictLikeType(collections.abc.MutableMapping):
def __init__(self, *args, **kwargs):
self.store = dict(*args, **kwargs)
def __getitem__(self, key):
return self.store[key]
...
Not only would we have to implement less magic methods, the ABC harness ensures that all mandatory protocol methods are in place. This partly mitigates the inherent fragility of dynamic typing.
Goose typing
Type checking based on types hierarchy is a popular pattern in Python programs. People with a background in statically-typed languages tend to introduce ad-hoc type checks like this:
# Non-Pythonic
if not isinstance(x, list):
raise ApplicationError('Python list type is expected')
While not discouraged in Python, type checks could be made more general and reliable by testing against abstract base types:
# Pythonic!
if not isinstance(x, collections.abc.MutableSequence):
raise ApplicationError('A sequence type is expected')
This is known as “Goose typing” in Python parlance. It immediately makes a type check compatible with both built-in and user types that inherit from abstract base classes. Additionally, checking against an ABC empowers interface-based, as opposed to hierarchy-based, types comparison :
class X:
def __len__(self):
return 0
>>> x = X()
>>> isinstance(x, collections.abc.Sized)
True
Alternative to ad-hoc type checks planted into the code is the gradual typing technique which is fully supported since Python 3.6. It is based on the idea of annotating important variables with type information, then running a static analyzer over the annotated code like this:
def filter_by_key(d: typing.Mapping, s: str) -> dict:
return {k: d[k] for k in d if k == s}
d: dict = filter_by_key('x': 1, 'y': 2}, 'x')
Static typing tends to make programs more reliable by leveraging explicit type information, computing types compatibility and failing gracefully when a type error is discovered. When type hinting is adopted by a project, type annotations can fully replace ad-hoc type checks throughout the code.
Pythonista’s power tools
It may occur to a reviewer, that a more efficient solution is possibly viable here and there. To establish solid technical grounds by backing their re-factoring proposal with hard numbers rather than intuition or personal preference, a quick analysis may come in handy.
Among the tools I use when researching for a better solution are dis (for bytecode analysis), timeit (for code snippets running time measurement) and profile (for finding hot spots in a running Python program).
Happy reviewing!