Suppose I have a function that is hard-coded to make a substring lowercase, when instances of that substring are found in a larger string, e.g.:
def process_record(header, needle, pattern):
sequence = needle
for idx in [m.start() for m in re.finditer(pattern, needle)]:
offset = idx + len(pattern)
sequence = sequence[:idx] + needle[idx:offset].lower() + sequence[offset:]
sys.stdout.write('%s\n%s\n' % (header, sequence))
This works fine, e.g.:
>>> process_record('>foo', 'ABCDEF', 'BCD')
>foo
AbcdEF
What I'd like to do is generalize this, to pass in a string function (lower, in this case, but it could be any function of a primitive type or class) as a parameter. Something like:
def process_record(header, needle, pattern, fn):
sequence = needle
for idx in [m.start() for m in re.finditer(pattern, needle)]:
offset = idx + len(pattern)
sequence = sequence[:idx] + needle[idx:offset].fn() + sequence[offset:]
sys.stdout.write('%s\n%s\n' % (header, sequence))
This doesn't work (which is why I'm asking the question), but hopefully this demonstrates the idea, to try to generalize what the function does in a way that is readable.
One option I suppose is to write a helper function that wraps stringInstance.lower() and passes copies of strings around, which is inefficient and clumsy. I'm hoping there's a more elegant approach that Python experts know about.
With C, for instance, I'd pass a pointer to the function I want to run as a parameter to process_record(), and run the function pointer directly on the variable of interest.
What is the syntax for doing the same when using string primitive functions (or similar on primitive or other classes) in Python?
In general, use this approach:
def call_fn(arg, fn):
return fn(arg)
call_fn('FOO', str.lower) # 'foo'
The definition of a method in Python always starts with self as it's first argument. By calling the method as an attribute of the class you can force the value of that argument.
Your example is a little complex, so I would break this into two different questions:
1) How can you provide functions as arguments?
Functions are objects like everything else, and can be passed around as expected, e.g.:
def apply(val, func):
# e.g. ("X", string.lower) -> "x"
# ("X", lambda x: x * 2) -> "XX"
return func(val)
In your example, you might do
def process_record(..., func):
...
sequence = ... func(needle[idx:offset]) ...
...
An alternative method that I wouldn't recommend would be something like
def apply_by_name(val, method_name):
# e.g. ("X", "lower") -> "x"
return getattr(val, method_name)()
2) How can I apply an effect to each match of a regular expression in a string?
For this I would recommend the built-in 'sub' function, which takes strings as well as functions.
>>> re.sub('[aeiou]', '!', 'the quick brown fox')
'th! q!!ck br!wn f!x'
def foo(match):
v = match.group()
if v == 'i': return '!!!!!!!'
elif v in 'eo': return v * 2
else: return v.upper()
>>> re.sub('[aeiou]', foo, 'the quick brown fox')
'thee qU!!!!!!!ck broown foox'
Hope this helps!
Here is my function which is supposed to apply another function to every element within a given iterable.
def transform(iterable,f):
all=(i for i in iterable)
return (e.f() for e in all)
for i in transform('abCdeFg','upper'):
print(i,end='')
What it should do is capitalizing all the letters, but instead I get an error. What am I doing wrong? I'm using Python 3.3.
e.f is literally e.f. It has no relation to your f variable. To get an attribute by name, you use getattr:
def transform(iterable,f):
all=(i for i in iterable)
return (getattr(e, f)() for e in all)
for i in transform('abCdeFg','upper'):
print(i,end='')
Also, you may find the builtin map function useful:
def function(l):
return l.upper()
for i in map(function, 'abCdeFg'):
print(i, end='')
You made 2 errors:
To call a function f on argument e, you do f(e), not e.f()
To give a function as a parameter, gives its name, not a string with its name
So a corrected version would be:
def transform(iterable, f):
return (f(i) for i in iterable)
for i in transform('abCdeFg', str.upper):
print(i, end='')
The error is on this line:
return (e.f() for e in all)
The problem is that the str variable e doesn't have a method f(). You should instead:
def transform(iterable,f):
all=(i for i in iterable)
return (f(e) for e in all)
for i in transform('abCdeFg',str.upper):
print(i,end='')
If you want to do this just as you have written it, you need to do this:
def transform(s,f):
return getattr(type(s), f)(s)
for i in transform('abCdeFg','upper'):
print(i,end='')
Prints:
ABCDEFG
As others have said, this is more direct:
print('abCdeFg'.upper())
You should also not use Python built-ins as names. (i.e., Avoid calling a genex all because you will overwrite the built-in function all.)
With getattr you can also return a default of the type does not have the given method:
>>> def transform(s,f):
... return getattr(type(s), f, lambda s: 'no bueno')(s)
>>> transform(1,'upper')
'no bueno'
Or use try / except:
>>> def transform(s,f):
... try:
... return getattr(type(s), f)(s)
... except AttributeError as e:
... return e
...
>>> transform(1,'upper')
AttributeError("type object 'int' has no attribute 'upper'",)
You can also use join with map:
>>> ''.join(map(lambda c: str.upper(c), 'abCdeFg'))
'ABCDEFG'
Or join with a comprehension:
>>> ''.join(c.upper() for c in 'abCdeFg')
'ABCDEFG'
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What are the lesser-known but useful features of the Python programming language?
Try to limit answers to Python core.
One feature per answer.
Give an example and short description of the feature, not just a link to documentation.
Label the feature using a title as the first line.
Quick links to answers:
Argument Unpacking
Braces
Chaining Comparison Operators
Decorators
Default Argument Gotchas / Dangers of Mutable Default arguments
Descriptors
Dictionary default .get value
Docstring Tests
Ellipsis Slicing Syntax
Enumeration
For/else
Function as iter() argument
Generator expressions
import this
In Place Value Swapping
List stepping
__missing__ items
Multi-line Regex
Named string formatting
Nested list/generator comprehensions
New types at runtime
.pth files
ROT13 Encoding
Regex Debugging
Sending to Generators
Tab Completion in Interactive Interpreter
Ternary Expression
try/except/else
Unpacking+print() function
with statement
Chaining comparison operators:
>>> x = 5
>>> 1 < x < 10
True
>>> 10 < x < 20
False
>>> x < 10 < x*10 < 100
True
>>> 10 > x <= 9
True
>>> 5 == x > 4
True
In case you're thinking it's doing 1 < x, which comes out as True, and then comparing True < 10, which is also True, then no, that's really not what happens (see the last example.) It's really translating into 1 < x and x < 10, and x < 10 and 10 < x * 10 and x*10 < 100, but with less typing and each term is only evaluated once.
Get the python regex parse tree to debug your regex.
Regular expressions are a great feature of python, but debugging them can be a pain, and it's all too easy to get a regex wrong.
Fortunately, python can print the regex parse tree, by passing the undocumented, experimental, hidden flag re.DEBUG (actually, 128) to re.compile.
>>> re.compile("^\[font(?:=(?P<size>[-+][0-9]{1,2}))?\](.*?)[/font]",
re.DEBUG)
at at_beginning
literal 91
literal 102
literal 111
literal 110
literal 116
max_repeat 0 1
subpattern None
literal 61
subpattern 1
in
literal 45
literal 43
max_repeat 1 2
in
range (48, 57)
literal 93
subpattern 2
min_repeat 0 65535
any None
in
literal 47
literal 102
literal 111
literal 110
literal 116
Once you understand the syntax, you can spot your errors. There we can see that I forgot to escape the [] in [/font].
Of course you can combine it with whatever flags you want, like commented regexes:
>>> re.compile("""
^ # start of a line
\[font # the font tag
(?:=(?P<size> # optional [font=+size]
[-+][0-9]{1,2} # size specification
))?
\] # end of tag
(.*?) # text between the tags
\[/font\] # end of the tag
""", re.DEBUG|re.VERBOSE|re.DOTALL)
enumerate
Wrap an iterable with enumerate and it will yield the item along with its index.
For example:
>>> a = ['a', 'b', 'c', 'd', 'e']
>>> for index, item in enumerate(a): print index, item
...
0 a
1 b
2 c
3 d
4 e
>>>
References:
Python tutorial—looping techniques
Python docs—built-in functions—enumerate
PEP 279
Creating generators objects
If you write
x=(n for n in foo if bar(n))
you can get out the generator and assign it to x. Now it means you can do
for n in x:
The advantage of this is that you don't need intermediate storage, which you would need if you did
x = [n for n in foo if bar(n)]
In some cases this can lead to significant speed up.
You can append many if statements to the end of the generator, basically replicating nested for loops:
>>> n = ((a,b) for a in range(0,2) for b in range(4,6))
>>> for i in n:
... print i
(0, 4)
(0, 5)
(1, 4)
(1, 5)
iter() can take a callable argument
For instance:
def seek_next_line(f):
for c in iter(lambda: f.read(1),'\n'):
pass
The iter(callable, until_value) function repeatedly calls callable and yields its result until until_value is returned.
Be careful with mutable default arguments
>>> def foo(x=[]):
... x.append(1)
... print x
...
>>> foo()
[1]
>>> foo()
[1, 1]
>>> foo()
[1, 1, 1]
Instead, you should use a sentinel value denoting "not given" and replace with the mutable you'd like as default:
>>> def foo(x=None):
... if x is None:
... x = []
... x.append(1)
... print x
>>> foo()
[1]
>>> foo()
[1]
Sending values into generator functions. For example having this function:
def mygen():
"""Yield 5 until something else is passed back via send()"""
a = 5
while True:
f = (yield a) #yield a and possibly get f in return
if f is not None:
a = f #store the new value
You can:
>>> g = mygen()
>>> g.next()
5
>>> g.next()
5
>>> g.send(7) #we send this back to the generator
7
>>> g.next() #now it will yield 7 until we send something else
7
If you don't like using whitespace to denote scopes, you can use the C-style {} by issuing:
from __future__ import braces
The step argument in slice operators. For example:
a = [1,2,3,4,5]
>>> a[::2] # iterate over the whole list in 2-increments
[1,3,5]
The special case x[::-1] is a useful idiom for 'x reversed'.
>>> a[::-1]
[5,4,3,2,1]
Decorators
Decorators allow to wrap a function or method in another function that can add functionality, modify arguments or results, etc. You write decorators one line above the function definition, beginning with an "at" sign (#).
Example shows a print_args decorator that prints the decorated function's arguments before calling it:
>>> def print_args(function):
>>> def wrapper(*args, **kwargs):
>>> print 'Arguments:', args, kwargs
>>> return function(*args, **kwargs)
>>> return wrapper
>>> #print_args
>>> def write(text):
>>> print text
>>> write('foo')
Arguments: ('foo',) {}
foo
The for...else syntax (see http://docs.python.org/ref/for.html )
for i in foo:
if i == 0:
break
else:
print("i was never 0")
The "else" block will be normally executed at the end of the for loop, unless the break is called.
The above code could be emulated as follows:
found = False
for i in foo:
if i == 0:
found = True
break
if not found:
print("i was never 0")
From 2.5 onwards dicts have a special method __missing__ that is invoked for missing items:
>>> class MyDict(dict):
... def __missing__(self, key):
... self[key] = rv = []
... return rv
...
>>> m = MyDict()
>>> m["foo"].append(1)
>>> m["foo"].append(2)
>>> dict(m)
{'foo': [1, 2]}
There is also a dict subclass in collections called defaultdict that does pretty much the same but calls a function without arguments for not existing items:
>>> from collections import defaultdict
>>> m = defaultdict(list)
>>> m["foo"].append(1)
>>> m["foo"].append(2)
>>> dict(m)
{'foo': [1, 2]}
I recommend converting such dicts to regular dicts before passing them to functions that don't expect such subclasses. A lot of code uses d[a_key] and catches KeyErrors to check if an item exists which would add a new item to the dict.
In-place value swapping
>>> a = 10
>>> b = 5
>>> a, b
(10, 5)
>>> a, b = b, a
>>> a, b
(5, 10)
The right-hand side of the assignment is an expression that creates a new tuple. The left-hand side of the assignment immediately unpacks that (unreferenced) tuple to the names a and b.
After the assignment, the new tuple is unreferenced and marked for garbage collection, and the values bound to a and b have been swapped.
As noted in the Python tutorial section on data structures,
Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.
Readable regular expressions
In Python you can split a regular expression over multiple lines, name your matches and insert comments.
Example verbose syntax (from Dive into Python):
>>> pattern = """
... ^ # beginning of string
... M{0,4} # thousands - 0 to 4 M's
... (CM|CD|D?C{0,3}) # hundreds - 900 (CM), 400 (CD), 0-300 (0 to 3 C's),
... # or 500-800 (D, followed by 0 to 3 C's)
... (XC|XL|L?X{0,3}) # tens - 90 (XC), 40 (XL), 0-30 (0 to 3 X's),
... # or 50-80 (L, followed by 0 to 3 X's)
... (IX|IV|V?I{0,3}) # ones - 9 (IX), 4 (IV), 0-3 (0 to 3 I's),
... # or 5-8 (V, followed by 0 to 3 I's)
... $ # end of string
... """
>>> re.search(pattern, 'M', re.VERBOSE)
Example naming matches (from Regular Expression HOWTO)
>>> p = re.compile(r'(?P<word>\b\w+\b)')
>>> m = p.search( '(((( Lots of punctuation )))' )
>>> m.group('word')
'Lots'
You can also verbosely write a regex without using re.VERBOSE thanks to string literal concatenation.
>>> pattern = (
... "^" # beginning of string
... "M{0,4}" # thousands - 0 to 4 M's
... "(CM|CD|D?C{0,3})" # hundreds - 900 (CM), 400 (CD), 0-300 (0 to 3 C's),
... # or 500-800 (D, followed by 0 to 3 C's)
... "(XC|XL|L?X{0,3})" # tens - 90 (XC), 40 (XL), 0-30 (0 to 3 X's),
... # or 50-80 (L, followed by 0 to 3 X's)
... "(IX|IV|V?I{0,3})" # ones - 9 (IX), 4 (IV), 0-3 (0 to 3 I's),
... # or 5-8 (V, followed by 0 to 3 I's)
... "$" # end of string
... )
>>> print pattern
"^M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$"
Function argument unpacking
You can unpack a list or a dictionary as function arguments using * and **.
For example:
def draw_point(x, y):
# do some magic
point_foo = (3, 4)
point_bar = {'y': 3, 'x': 2}
draw_point(*point_foo)
draw_point(**point_bar)
Very useful shortcut since lists, tuples and dicts are widely used as containers.
ROT13 is a valid encoding for source code, when you use the right coding declaration at the top of the code file:
#!/usr/bin/env python
# -*- coding: rot13 -*-
cevag "Uryyb fgnpxbiresybj!".rapbqr("rot13")
Creating new types in a fully dynamic manner
>>> NewType = type("NewType", (object,), {"x": "hello"})
>>> n = NewType()
>>> n.x
"hello"
which is exactly the same as
>>> class NewType(object):
>>> x = "hello"
>>> n = NewType()
>>> n.x
"hello"
Probably not the most useful thing, but nice to know.
Edit: Fixed name of new type, should be NewType to be the exact same thing as with class statement.
Edit: Adjusted the title to more accurately describe the feature.
Context managers and the "with" Statement
Introduced in PEP 343, a context manager is an object that acts as a run-time context for a suite of statements.
Since the feature makes use of new keywords, it is introduced gradually: it is available in Python 2.5 via the __future__ directive. Python 2.6 and above (including Python 3) has it available by default.
I have used the "with" statement a lot because I think it's a very useful construct, here is a quick demo:
from __future__ import with_statement
with open('foo.txt', 'w') as f:
f.write('hello!')
What's happening here behind the scenes, is that the "with" statement calls the special __enter__ and __exit__ methods on the file object. Exception details are also passed to __exit__ if any exception was raised from the with statement body, allowing for exception handling to happen there.
What this does for you in this particular case is that it guarantees that the file is closed when execution falls out of scope of the with suite, regardless if that occurs normally or whether an exception was thrown. It is basically a way of abstracting away common exception-handling code.
Other common use cases for this include locking with threads and database transactions.
Dictionaries have a get() method
Dictionaries have a 'get()' method. If you do d['key'] and key isn't there, you get an exception. If you do d.get('key'), you get back None if 'key' isn't there. You can add a second argument to get that item back instead of None, eg: d.get('key', 0).
It's great for things like adding up numbers:
sum[value] = sum.get(value, 0) + 1
Descriptors
They're the magic behind a whole bunch of core Python features.
When you use dotted access to look up a member (eg, x.y), Python first looks for the member in the instance dictionary. If it's not found, it looks for it in the class dictionary. If it finds it in the class dictionary, and the object implements the descriptor protocol, instead of just returning it, Python executes it. A descriptor is any class that implements the __get__, __set__, or __delete__ methods.
Here's how you'd implement your own (read-only) version of property using descriptors:
class Property(object):
def __init__(self, fget):
self.fget = fget
def __get__(self, obj, type):
if obj is None:
return self
return self.fget(obj)
and you'd use it just like the built-in property():
class MyClass(object):
#Property
def foo(self):
return "Foo!"
Descriptors are used in Python to implement properties, bound methods, static methods, class methods and slots, amongst other things. Understanding them makes it easy to see why a lot of things that previously looked like Python 'quirks' are the way they are.
Raymond Hettinger has an excellent tutorial that does a much better job of describing them than I do.
Conditional Assignment
x = 3 if (y == 1) else 2
It does exactly what it sounds like: "assign 3 to x if y is 1, otherwise assign 2 to x". Note that the parens are not necessary, but I like them for readability. You can also chain it if you have something more complicated:
x = 3 if (y == 1) else 2 if (y == -1) else 1
Though at a certain point, it goes a little too far.
Note that you can use if ... else in any expression. For example:
(func1 if y == 1 else func2)(arg1, arg2)
Here func1 will be called if y is 1 and func2, otherwise. In both cases the corresponding function will be called with arguments arg1 and arg2.
Analogously, the following is also valid:
x = (class1 if y == 1 else class2)(arg1, arg2)
where class1 and class2 are two classes.
Doctest: documentation and unit-testing at the same time.
Example extracted from the Python documentation:
def factorial(n):
"""Return the factorial of n, an exact integer >= 0.
If the result is small enough to fit in an int, return an int.
Else return a long.
>>> [factorial(n) for n in range(6)]
[1, 1, 2, 6, 24, 120]
>>> factorial(-1)
Traceback (most recent call last):
...
ValueError: n must be >= 0
Factorials of floats are OK, but the float must be an exact integer:
"""
import math
if not n >= 0:
raise ValueError("n must be >= 0")
if math.floor(n) != n:
raise ValueError("n must be exact integer")
if n+1 == n: # catch a value like 1e300
raise OverflowError("n too large")
result = 1
factor = 2
while factor <= n:
result *= factor
factor += 1
return result
def _test():
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
Named formatting
% -formatting takes a dictionary (also applies %i/%s etc. validation).
>>> print "The %(foo)s is %(bar)i." % {'foo': 'answer', 'bar':42}
The answer is 42.
>>> foo, bar = 'question', 123
>>> print "The %(foo)s is %(bar)i." % locals()
The question is 123.
And since locals() is also a dictionary, you can simply pass that as a dict and have % -substitions from your local variables. I think this is frowned upon, but simplifies things..
New Style Formatting
>>> print("The {foo} is {bar}".format(foo='answer', bar=42))
To add more python modules (espcially 3rd party ones), most people seem to use PYTHONPATH environment variables or they add symlinks or directories in their site-packages directories. Another way, is to use *.pth files. Here's the official python doc's explanation:
"The most convenient way [to modify
python's search path] is to add a path
configuration file to a directory
that's already on Python's path,
usually to the .../site-packages/
directory. Path configuration files
have an extension of .pth, and each
line must contain a single path that
will be appended to sys.path. (Because
the new paths are appended to
sys.path, modules in the added
directories will not override standard
modules. This means you can't use this
mechanism for installing fixed
versions of standard modules.)"
Exception else clause:
try:
put_4000000000_volts_through_it(parrot)
except Voom:
print "'E's pining!"
else:
print "This parrot is no more!"
finally:
end_sketch()
The use of the else clause is better than adding additional code to the try clause because it avoids accidentally catching an exception that wasn’t raised by the code being protected by the try ... except statement.
See http://docs.python.org/tut/node10.html
Re-raising exceptions:
# Python 2 syntax
try:
some_operation()
except SomeError, e:
if is_fatal(e):
raise
handle_nonfatal(e)
# Python 3 syntax
try:
some_operation()
except SomeError as e:
if is_fatal(e):
raise
handle_nonfatal(e)
The 'raise' statement with no arguments inside an error handler tells Python to re-raise the exception with the original traceback intact, allowing you to say "oh, sorry, sorry, I didn't mean to catch that, sorry, sorry."
If you wish to print, store or fiddle with the original traceback, you can get it with sys.exc_info(), and printing it like Python would is done with the 'traceback' module.
Main messages :)
import this
# btw look at this module's source :)
De-cyphered:
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
Interactive Interpreter Tab Completion
try:
import readline
except ImportError:
print "Unable to load readline module."
else:
import rlcompleter
readline.parse_and_bind("tab: complete")
>>> class myclass:
... def function(self):
... print "my function"
...
>>> class_instance = myclass()
>>> class_instance.<TAB>
class_instance.__class__ class_instance.__module__
class_instance.__doc__ class_instance.function
>>> class_instance.f<TAB>unction()
You will also have to set a PYTHONSTARTUP environment variable.
Nested list comprehensions and generator expressions:
[(i,j) for i in range(3) for j in range(i) ]
((i,j) for i in range(4) for j in range(i) )
These can replace huge chunks of nested-loop code.
Operator overloading for the set builtin:
>>> a = set([1,2,3,4])
>>> b = set([3,4,5,6])
>>> a | b # Union
{1, 2, 3, 4, 5, 6}
>>> a & b # Intersection
{3, 4}
>>> a < b # Subset
False
>>> a - b # Difference
{1, 2}
>>> a ^ b # Symmetric Difference
{1, 2, 5, 6}
More detail from the standard library reference: Set Types