# Overview

It's very common to need to extract text or text lines from within program source. The way Python likes to have its text indented, however, means that there will often be extra spaces appended to the beginning of each line, as well as possibly extra lines at the start and end of the text that are present for inclusion in the program source, but not useful in the resulting data.

Python string methods give easy ways to clean this text up, but it's no joy reinventing that particular text-cleanup wheel every time you need it--especially since many of the details are nitsy, dropping the code down into low-level constructs rather than just "give me the text!"

This module helps clean up included text (or text lines) in a simple, reusable way that won't muck up your programs with extra code, or require constant wheel-reinvention.

## Usage

data = lines("""
There was an old woman who lived in a shoe.
She had so many children, she didn't know what to do;
She gave them some broth without any bread;
Then whipped them all soundly and put them to bed.
""")


will result in:

['There was an old woman who lived in a shoe.',
"She had so many children, she didn't know what to do;",
'She gave them some broth without any bread;',
'Then whipped them all soundly and put them to bed.']


If instead you used textlines(), the result is the same, but joined by newlines into into a single string:

"There was an old woman who lived in a shoe.\nShe ... to bed."
# where the ... abbreviates exactly the characters you'd expect


textlines is an optional entry point, as lines has a join kwarg that, if set, joins the lines with that string.

Both routines provide typically-desired cleanups:

• remove blank lines default), but at least first and last blanks (which usually appear due to Python formatting)
• remove common line prefix (default)
• (optionally) join the lines together with your choice of separator string

## The API

lines(text, noblanks=True, dedent=True, lstrip=False, rstrip=True, join=False)

Returns text as a series of cleaned-up lines.

• text is the text to be processed.
• noblanks => all blank lines are eliminated, not just starting and ending ones. (default True).
• dedent => strip a common prefix (usually whitespace) from each line (default True).
• lstrip => strip all left (leading) space from each line (default False). Note that lstrip and dedent are mutually exclusive ways of handling leading space.
• rstrip => strip all right (trailing) space from each line (default True)
• join => either False (do nothing), True (concatenate lines), or a string that will be used to join the resulting lines (default False)

textlines(text, noblanks=True, dedent=True, lstrip=False, rstrip=True, join=False)

Does the same helpful cleanups as lines(), but returns result as a single string, with lines separated by newlines (by default) and without a trailing newline.

## Notes

• Automated multi-version testing accomplished with pytest and tox. Latest version successfully tested against Python 2.6, 2.7, 3.2, 3.3, 3.4, and PyPy 2.4.0 (based on 2.7.8). It should also work on Python 2.5, though it's no longer officially supported; time to upgrade!
• Common line prefix is now computed without considering blank lines, so blank lines need not have any indentation on them just to "make things work."
• The tricky case where all lines have a common prefix, but it's not entirely composed of whitespace, now properly handled. This is useful for lines that are already "quoted" such as with leading "|" or ">" symbols (common in Markdown and old-school email usage styles)/
• textlines() is now somewhat superfluous, now that lines() has a join kwarg. But you may prefer it for the implicit indication that it's turning lines into text.
• It's tempting to define a constant such as Dedent that might be the default for the lstrip parameter, instead of having separate dedent and lstrip Booleans. The more I use singleton classes in Python as designated special values, the more useful they seem.
pip install -U textdata

python3.3 -m easy_install --upgrade textdata