The scope of index variables in Python's for loops(eli.thegreenplace.net)
eli.thegreenplace.net
The scope of index variables in Python's for loops
http://eli.thegreenplace.net/2015/the-scope-of-index-variables-in-pythons-for-loops/
6 comments
> It's not a very good proposal; some of the type info goes in comments because it didn't fit the syntax.
Actually, the reason why it is put in the comments is that it is implemented as an experimental feature so that its usefulness can be evaluated without having to (possibly only temporarily) change the syntax.
Actually, the reason why it is put in the comments is that it is implemented as an experimental feature so that its usefulness can be evaluated without having to (possibly only temporarily) change the syntax.
Yes another super useful trick you can use with this scope trick is to get the length of a file (or its last line) just do:
for i,v in enumerate(open(f)): pass
now 'i' is the number of lines in your file and 'v' is the last line. This is also blazingly fast, especially useful when you have ~several GB large log files to parse.
Great article!
for i,v in enumerate(open(f)): pass
now 'i' is the number of lines in your file and 'v' is the last line. This is also blazingly fast, especially useful when you have ~several GB large log files to parse.
Great article!
Note that your code still forces python to read the entire file! If all you want is the last line of a file, use the UNIX tail:
EDIT: I haven't tested it but you might be interested in this implementation of tail in python: http://stackoverflow.com/a/136368
tail -n 1 $f
EDIT: I haven't tested it but you might be interested in this implementation of tail in python: http://stackoverflow.com/a/136368
I've done timing tests and it is typically on par with tail & wc. The bulk of the time is wasted reading your file into ram, the time it takes to count the lines is essentially 0.
Edit: Of course I misspoke, yes tail is much faster for getting the last line of the file! I meant for getting the line count the loop methods is typically just ~5% slower than wc on sufficiently large files.
Edit: Of course I misspoke, yes tail is much faster for getting the last line of the file! I meant for getting the line count the loop methods is typically just ~5% slower than wc on sufficiently large files.
It's a nice trick, but it's not too "blazingly" fast.
(the long output of the profiler is a little more interesting, I've put it in a pastebin http://pastebin.com/WXX93sS8 )
EDIT/ADD: There's an awful lot time spent in decoding the characters to unicode strings!
In [73]: cProfile.run('for i,v in enumerate(open(fn)): pass\nprint(i)')
15496799
406215 function calls in 10.998 seconds
def file_block_count_lf(f) :
while True :
k = f.read(1<<20)
if k == '' :
break
yield k.count('\n')
In [77]: cProfile.run('print(sum(file_block_count_lf(open(fn))))')
15496800
10977 function calls in 6.479 seconds
In [70]: cProfile.run('sum(1 for l in open(fn))')
15903016 function calls in 14.409 seconds
(the long output of the profiler is a little more interesting, I've put it in a pastebin http://pastebin.com/WXX93sS8 )
EDIT/ADD: There's an awful lot time spent in decoding the characters to unicode strings!
def file_block_count_lf(f) :
while True :
k = f.read(1<<20)
if not k :
break
yield k.count(b'\n')
In [48]: cProfile.run("sum(file_block_count_lf(open('more_logs.txt','rb')))")
4768 function calls in 2.969 secondsWhen you do your timing tests make sure to do it on different files to make sure you aren't taking advantage of caching your system may be doing. That may explain some of your timing discrepancies. And when I say blazingly fast I mean that these pyscripts are nearly as fast as wc/tail.
Edit; I just tested your code and my own and against a set of files of uniform size.
time wc -l GIANTFILE1 -> 16.7 seconds
cProfile.run("for i,v in enumerate(open("GIANTFILE2")):pass") -> 18.1 seconds
cProfile.run("sum(file_block_count_lf(open('GIANTFILE3','rb')))") -> 22.9 seconds
Edit; I just tested your code and my own and against a set of files of uniform size.
time wc -l GIANTFILE1 -> 16.7 seconds
cProfile.run("for i,v in enumerate(open("GIANTFILE2")):pass") -> 18.1 seconds
cProfile.run("sum(file_block_count_lf(open('GIANTFILE3','rb')))") -> 22.9 seconds
Obviously, when comparing different methods of computation with negligible cache/memory footprint, I'd run it on hot caches to maximize the visible effect on the implementation. File here was about 1.5 GB on a 4GB RAM machine, and I repeated each test to have a stable duration for each.
Would I have tested, say, different patterns of data access (reading files sequentially, parallel read from multiple disks/partitions), of course I would have made sure that either caches are cleared (/proc/sys/vm/drop_caches), datasets will exceed memory, and actual CPU load of the algorithm is according to the real-world-usage scenario (pure read() throughput is meaningless when CPU is bogged, and L1-3 caches swamped by actual algorithmic work).
Re: your additional tests: Interesting, what python-version and size of files are you running it on?
Would I have tested, say, different patterns of data access (reading files sequentially, parallel read from multiple disks/partitions), of course I would have made sure that either caches are cleared (/proc/sys/vm/drop_caches), datasets will exceed memory, and actual CPU load of the algorithm is according to the real-world-usage scenario (pure read() throughput is meaningless when CPU is bogged, and L1-3 caches swamped by actual algorithmic work).
Re: your additional tests: Interesting, what python-version and size of files are you running it on?
I'm trapped using 2.7! On this machine it looks like I'm running 2.7.8. Files are roughly 1GB each. They're on a relatively slow file system, just a single HD on this machine... no raid, no ssd.
Somewhat tangentially related, the for-else-statement of Python is something that I occasionally miss in every other language that I seriously use.
And sometimes I really wish that the scope of variables inside a do-while-loop in C-ish languages would extend into the loop condition of the final while...
And sometimes I really wish that the scope of variables inside a do-while-loop in C-ish languages would extend into the loop condition of the final while...
The Python 2 list comprehension behaviour could be used to assign local variables within lambda expressions. Disgusting abuse, but fun: http://stackoverflow.com/a/14617232/1114
I found this example most surprising:
It appears that the lambda creates a closure for i, but i is defined in the outer scope and we capture the reference for i, rather than i's value.
So we can get around this:
--------Edit 2--------
Out of curiosity, I looked at this in Clojure and the way it creates lambdas is similar to Python (captures reference as opposed to value).
>>> def foo():
... lst = []
... for i in range(4):
... lst.append(lambda: i)
... print([f() for f in lst])
...
>>> foo()
[3, 3, 3, 3]
It appears that the lambda creates a closure for i, but i is defined in the outer scope and we capture the reference for i, rather than i's value.
So we can get around this:
>>> def foo():
... lst = []
... for i in range(4):
... lst.append((lambda a: lambda: a)(i))
... print([f() for f in lst])
...
>>> foo()
[0, 1, 2, 3]
--------Edit 2--------
Out of curiosity, I looked at this in Clojure and the way it creates lambdas is similar to Python (captures reference as opposed to value).
user=> (def a 1)
#'user/a
user=> (def f (fn [] a))
#'user/f
user=> (f)
1
user=> (def a 2)
#'user/a
user=> (f)
2
user=> (def f ((fn [x] (fn [] x)) a))
#'user/f
user=> (def a 1)
#'user/a
user=> (def f ((fn [x] (fn [] x)) a))
#'user/f
user=> (f)
1
user=> (def a 10)
#'user/a
user=> (f)
1
You can capture the index with a keyword argument default value:
def foo():
lst = []
for i in range(4):
lst.append(lambda i=i: i)
print([f() for f in lst])
Common Lisp:
(setf *a* nil)
(dotimes (x 3)
(push (lambda () x) *a*))
(mapcar #'funcall *a*)
Probably this stems from the typical C-way of error-reporting out of a for-loop, which is to terminate earlier than the "regular loop condition", and hence a second test for the loop-condition can easily be used to check for abnormal termination...
int i;
for (i=0; i<length; i++) {
if (is_broken(element[i]))
break;
process(element[i]);
}
/* if everything went fine, i == length, else we broke out early */
if (i < length) {
fprintf(stderr,"Something was broken.");
return -1;
}
for i in [1,2,3] :
do_something(i)
# ..now i is 3, not 4... obviously
Go has worse problems. The ":=" operator in Go indicates the creation of a new variable. Except when there are multiple variables on the left side of the :=.
can declare a, b, or a and b, but not neither of a and b. (See https://play.golang.org/p/zE7AIMhleo). This can create some hard to find shadowing bugs.
Declaration-free language are hard. It's impressive how far Python got without declarations. Now there's an advisory typing proposal for Python. It's not a very good proposal; some of the type info goes in comments because it didn't fit the syntax.