A flake8 plugin that helps you simplify your code.
Install with pip
:
pip install flake8-simplify
Python 3.6 to 3.8 are supported.
Just call flake8 .
in your package or flake your.py
:
$ flake8 .
./foo/__init__.py:690:12: SIM101 Multiple isinstance-calls which can be merged into a single call for variable 'other'
Python-specific rules:
SIM101
: Multiple isinstance-calls which can be merged into a single call by using a tuple as a second argument (example)SIM104
: Use 'yield from iterable' (introduced in Python 3.3, see PEP 380) (example)SIM105
: Use 'contextlib.suppress(...)' instead of try-except-pass (example)SIM107
: Don't usereturn
in try/except and finally (example)SIM108
: Use the ternary operator if it's reasonable (example)SIM109
: Use a list to compare a value against multiple values (example)SIM110
: Use any(...) (example)SIM111
: Use all(...) (example)SIM113
: Use enumerate instead of manually incrementing a counter (example)SIM114
: Combine conditions via a logical or to prevent duplicating code (example)SIM115
: Use context handler for opening files (example)SIM116
: Use a dictionary instead of many if/else equality checks (example)SIM117
: Merge with-statements that use the same scope (example)SIM119
: Use dataclasses for data containers (example)SIM120
: Use 'class FooBar:' instead of 'class FooBar(object):' (example)
Simplifying Comparations:
SIM201
: Use 'a != b' instead of 'not a == b' (example)SIM202
: Use 'a == b' instead of 'not a != b' (example)SIM203
: Use 'a not in b' instead of 'not (a in b)' (example)SIM204
: Use 'a >= b' instead of 'not (a < b)' (example)SIM205
: Use 'a > b' instead of 'not (a <= b)' (example)SIM206
: Use 'a <= b' instead of 'not (a > b)' (example)SIM207
: Use 'a < b' instead of 'not (a <= b)' (example)SIM208
: Use 'a' instead of 'not (not a)' (example)SIM210
: Use 'bool(a)' instead of 'True if a else False' (example)SIM211
: Use 'not a' instead of 'False if a else True' (example)SIM212
: Use 'a if a else b' instead of 'b if not a else a' (example)SIM220
: Use 'False' instead of 'a and not a' (example)SIM221
: Use 'True' instead of 'a or not a' (example)SIM222
: Use 'True' instead of '... or True' (example)SIM223
: Use 'False' instead of '... and False' (example)SIM300
: Use 'age == 42' instead of '42 == age' (example)
Simplifying usage of dictionaries:
SIM401
: Use 'a_dict.get(key, "default_value")' instead of an if-block (example)SIM118
: Use 'key in dict' instead of 'key in dict.keys()' (example)
General Code Style:
SIM102
: Use a single if-statement instead of nested if-statements (example)SIM103
: Return the boolean condition directly (example)SIM106
: Handle error-cases first (example)SIM112
: Use CAPITAL environment variables (example)
You might have good reasons to ignore some rules. To do that, use the standard Flake8 configuration. For example, within the setup.cfg
file:
[flake8]
ignore = SIM106, SIM113, SIM119
# Bad
isinstance(a, int) or isinstance(a, float)
# Good
isinstance(a, (int, float))
# Bad
if a:
if b:
c
# Good
if a and b:
c
# Bad: This example returns 3!
def foo():
try:
1 / 0
return "1"
except:
return "2"
finally:
return "3"
# Good
def foo():
return_value = None
try:
1 / 0
return_value = "1"
except:
return_value = "2"
finally:
return_value = "3" # you might also want to check if "except" happened
return return_value
# Bad
if a:
b = c
else:
b = d
# Good
b = c if a else d
# Bad
if a == b or a == c:
d
# Good
if a in [b, c]:
d
# Bad
for x in iterable:
if check(x):
return True
return False
# Good
return any(check(x) for x in iterable)
# Bad
for x in iterable:
if check(x):
return False
return True
# Good
return all(not check(x) for x in iterable)
# Bad
os.environ["foo"]
os.environ.get("bar")
# Good
os.environ["FOO"]
os.environ.get("BAR")
Using enumerate
in simple cases like the loops below is a tiny bit easier to read as the reader does not have to figure out where / when the loop variable is incremented (or if it is incremented in multiple places).
# Bad
idx = 0
for el in iterable:
...
idx += 1
# Good
for idx, el in enumerate(iterable):
...
# Bad
if a:
b
elif c:
b
# Good
if a or c:
b
# Bad
f = open(...)
... # (do something with f)
f.close()
# Good
with open(...) as f:
... # (do something with f)
# Bad
if a == "foo":
return "bar"
elif a == "bar":
return "baz"
elif a == "boo":
return "ooh"
else:
return 42
# Good
mapping = {"foo": "bar", "bar": "baz", "boo": "ooh"}
return mapping.get(a, 42)
# Bad
with A() as a:
with B() as b:
print("hello")
# Good
with A() as a, B() as b:
print("hello")
Thank you for pointing this one out, Aaron Gokaslan!
# Bad
key in a_dict.keys()
# Good
key in a_dict
Thank you for pointing this one out, Aaron Gokaslan!
Dataclasses were introduced with PEP 557 in Python 3.7. The main reason not to use dataclasses is to support legacy Python versions.
Dataclasses create a lot of the boilerplate code for you:
__init__
__eq__
__hash__
__str__
__repr__
A lot of projects use them:
# Bad
class FooBar(object):
...
# Good
class FooBar:
...
Both notations are equivalent in Python 3, but the second one is shorter.
# Bad
not a == b
# Good
a != b
# Bad
not a != b
# Good
a == b
# Bad
not a in b
# Good
a not in b
# Bad
not a < b
# Good
a >= b
# Bad
not a <= b
# Good
a > b
# Bad
not a > b
# Good
a <= b
# Bad
not a >= b
# Good
a < b
# Bad
not (not a)
# Good
a
# Bad
True if a else False
# Good
bool(a)
# Bad
False if a else True
# Good
not a
# Bad
b if not a else a
# Good
a if a else b
# Bad
a and not a
# Good
False
# Bad
a or not a
# Good
True
# Bad
... or True
# Good
True
# Bad
... and False
# Good
False
# Bad; This is called a "Yoda-Condition"
42 == age
# Good
age == 42
# Bad
if key in a_dict:
value = a_dict[key]
else:
value = "default_value"
# Good
thing = a_dict.get(key, "default_value")