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Lstrip, rstrip and strip remove characters from the left, right and both ends of a string respectively The with statement saves you from having to call close manually. By default they remove whitespace characters (space, tabs, linebreaks, etc)
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I want to eliminate all the whitespace from a string, on both ends, and in between words So instead you can read the whole thing then split on spaces I have this python code
Sentence = ' hello apple ' sentence.strip() but that
I was told it deletes whitespace but s = ss asdas vsadsafas asfasasgas print(s.strip()) prints out ss asdas vsadsafas asfasasgas shouldn't it be ssasdasvsadsafasasfasasgas? The string.strip (), string.stripleading (), and string.striptrailing () methods trim white space [as determined by character.iswhitespace ()] off either the front, back, or both front and back of the targeted string /** * returns a string whose value is this string, with any leading and trailing * whitespace removed. Map(str.strip, my_list) is the fastest way, it's just a little bit faster than comperhensions
Use map or itertools.imap if there's a single function that you want to apply (like str.split) I know.strip() returns a copy of the string in which all chars have been stripped from the beginning and the end of the string But i wonder why / if it is necessary. The method strip () returns a copy of the string in which all chars have been stripped from the beginning and the end of the string (default whitespace characters)
So, it trims whitespace from begining and end of a string if no input char is specified
At this point, it just controls whether string x is empty or not without considering spaces because an empty string is interpreted as false in. I'm trying to recreate the strip () function of python using regex It's the last practice problem from automate the boring stuff with python Import re stripchar = input ('enter
Strip returns a new string, so you need to assign that to something (better yet, just use a list comprehension) iterating over a file object gives you lines, not words
