How to efficiently parse fixed width files?

Using the Python standard library's struct module would be fairly easy as well as fairly fast since it's written in C. The code below how it use it. It also allows columns of characters to be skipped by specifying negative values for the number of characters in the field.

import struct

fieldwidths = (2, -10, 24)
fmtstring = ' '.join('{}{}'.format(abs(fw), 'x' if fw < 0 else 's') for fw in fieldwidths)

# Convert Unicode input to bytes and the result back to Unicode string.
unpack = struct.Struct(fmtstring).unpack_from  # Alias.
parse = lambda line: tuple(s.decode() for s in unpack(line.encode()))

print('fmtstring: {!r}, record size: {} chars'.format(fmtstring, struct.calcsize(fmtstring)))

line = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\n'
fields = parse(line)
print('fields: {}'.format(fields))

Output:

fmtstring: '2s 10x 24s', recsize: 36 chars
fields: ('AB', 'MNOPQRSTUVWXYZ0123456789')

Here's a way to do it with string slices, as you were considering but were concerned that it might get too ugly. It is kind of complicated and speedwise it's about the same as the version based the struct module — although I have an idea about how it could be sped up (which might make the extra complexity worthwhile). See update below on that topic.

from itertools import zip_longest
from itertools import accumulate

def make_parser(fieldwidths):
    cuts = tuple(cut for cut in accumulate(abs(fw) for fw in fieldwidths))
    pads = tuple(fw < 0 for fw in fieldwidths) # bool values for padding fields
    flds = tuple(zip_longest(pads, (0,)+cuts, cuts))[:-1]  # ignore final one
    parse = lambda line: tuple(line[i:j] for pad, i, j in flds if not pad)
    # Optional informational function attributes.
    parse.size = sum(abs(fw) for fw in fieldwidths)
    parse.fmtstring = ' '.join('{}{}'.format(abs(fw), 'x' if fw < 0 else 's')
                                                for fw in fieldwidths)
    return parse

line = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\n'
fieldwidths = (2, -10, 24)  # negative widths represent ignored padding fields
parse = make_parser(fieldwidths)
fields = parse(line)
print('format: {!r}, rec size: {} chars'.format(parse.fmtstring, parse.size))
print('fields: {}'.format(fields))

Output:

format: '2s 10x 24s', rec size: 36 chars
fields: ('AB', 'MNOPQRSTUVWXYZ0123456789')

Update

As I suspected, there is a way of making the string-slicing version of the code faster — which in Python 2.7 make it about the same speed as the version using struct, but in Python 3.x make it 233% faster (as well as the un-optimized version of itself which is about the same speed as the struct version).

What the version presented above does is define a lambda function that's primarily a comprehension that generates the limits of a bunch of slices at runtime.

parse = lambda line: tuple(line[i:j] for pad, i, j in flds if not pad)

Which is equivalent to a statement like the following, depending on the values of i and j in the for loop, to something looking like this:

parse = lambda line: tuple(line[0:2], line[12:36], line[36:51], ...)

However the latter executes more than twice as fast since the slice boundaries are all constants.

Fortunately it relatively easy to convert and "compile" the former into the latter using the built-in eval() function:

def make_parser(fieldwidths):
    cuts = tuple(cut for cut in accumulate(abs(fw) for fw in fieldwidths))
    pads = tuple(fw < 0 for fw in fieldwidths) # bool flags for padding fields
    flds = tuple(zip_longest(pads, (0,)+cuts, cuts))[:-1]  # ignore final one
    slcs = ', '.join('line[{}:{}]'.format(i, j) for pad, i, j in flds if not pad)
    parse = eval('lambda line: ({})\n'.format(slcs))  # Create and compile source code.
    # Optional informational function attributes.
    parse.size = sum(abs(fw) for fw in fieldwidths)
    parse.fmtstring = ' '.join('{}{}'.format(abs(fw), 'x' if fw < 0 else 's')
                                                for fw in fieldwidths)
    return parse

I'm not really sure if this is efficient, but it should be readable (as opposed to do the slicing manually). I defined a function slices that gets a string and column lengths, and returns the substrings. I made it a generator, so for really long lines, it doesn't build a temporary list of substrings.

def slices(s, *args):
    position = 0
    for length in args:
        yield s[position:position + length]
        position += length

Example

In [32]: list(slices('abcdefghijklmnopqrstuvwxyz0123456789', 2))
Out[32]: ['ab']

In [33]: list(slices('abcdefghijklmnopqrstuvwxyz0123456789', 2, 10, 50))
Out[33]: ['ab', 'cdefghijkl', 'mnopqrstuvwxyz0123456789']

In [51]: d,c,h = slices('dogcathouse', 3, 3, 5)
In [52]: d,c,h
Out[52]: ('dog', 'cat', 'house')

But I think the advantage of a generator is lost if you need all columns at once. Where one could benefit from is when you want to process columns one by one, say in a loop.