AMD wrote:
For reading delimited fields in Python, you can use .split string method.
Yes, that is what I use right now, but I still have to do the conversion
to integers, floats, dates as several separate steps. What is nice about
the scanf function is that it is all done on the same step. Exactly like
when you use % to format a string and you pass it a dictionary, it does
all the conversions to string for you.
You're confusing surface syntax with processing steps. If you want to
do things on one line, just add a suitable helper to take care of the
processing. E.g. for whitespace-separated data:
>>> def scan(s, *types):
... return tuple(f(v) for (f, v) in zip(types, s.split()))
...
>>> scan("1 2 3", int, int, float)
(1, 2, 3.0)
This has the additional advantage that it works with any data type that
provides a way to convert from string to that type, not just a small
number of built-in types. And you can even pass in your own local
helper, of course:
>>> def myfactory(n):
... return int(n) * "!"
...
>>> scan("1 2 3", int, float, myfactory)
(1, 2.0, '!!!')
If you're reading multiple columns of the same type, you might as well
inline the whole thing:
data = map(int, line.split())
For other formats, replace the split with slicing or a regexp. Or use a
ready-made module; there's hardly every any reason to read standard CSV
files by hand when you can just do "import csv", for example.
Also note that function *creation* is relatively cheap in Python, and
since "def" is an executable statement, you can create them pretty much
anywhere; if you find that need a helper somewhere in your code, just
put it there. The following is a perfectly valid pattern:
def myfunc(...):
def myhelper(...):
...
myhelper(...)
myhelper(...)
for line in open(file):
myhelper(...)
(I'd say knowing when and how to abstract things away into a local
helper is an important step towards full Python fluency -- that is, the
point where you're able to pack "a lot of action in a small amount of
clear code" most of the time.)
</F>
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