一个简单的灵魂

>>> import re
>>> s="The   fox jumped   over    the log."
>>> print re.sub('\s+',' ', s)
The fox jumped over the log.

你也可以在Pandas DataFrame中使用字符串分割技术，而不需要使用.apply(..)，如果你需要对大量字符串快速执行操作，这是非常有用的。这是一行话:

df['message'] = (df['message'].str.split()).str.join(' ')

import re

Text = " You can select below trims for removing white space!!   BR Aliakbar     "
  # trims all white spaces
print('Remove all space:',re.sub(r"\s+", "", Text), sep='') 
# trims left space
print('Remove leading space:', re.sub(r"^\s+", "", Text), sep='') 
# trims right space
print('Remove trailing spaces:', re.sub(r"\s+$", "", Text), sep='')  
# trims both
print('Remove leading and trailing spaces:', re.sub(r"^\s+|\s+$", "", Text), sep='')
# replace more than one white space in the string with one white space
print('Remove more than one space:',re.sub(' +', ' ',Text), sep='') 

结果:作为代码

"Remove all space:Youcanselectbelowtrimsforremovingwhitespace!!BRAliakbar"
"Remove leading space:You can select below trims for removing white space!!   BR Aliakbar"     
"Remove trailing spaces: You can select below trims for removing white space!!   BR Aliakbar"
"Remove leading and trailing spaces:You can select below trims for removing white space!!   BR Aliakbar"
"Remove more than one space: You can select below trims for removing white space!! BR Aliakbar" 

这样做，并将这样做::)

# python... 3.x
import operator
...
# line: line of text
return " ".join(filter(lambda a: operator.is_not(a, ""), line.strip().split(" ")))

import re
string = re.sub('[ \t\n]+', ' ', 'The     quick brown                \n\n             \t        fox')

这将删除所有的制表符，新行和多个空白与单一空白。

def unPretty(S):
   # Given a dictionary, JSON, list, float, int, or even a string...
   # return a string stripped of CR, LF replaced by space, with multiple spaces reduced to one.
   return ' '.join(str(S).replace('\n', ' ').replace('\r', '').split())