def str_match(arr, pat, case=True, flags=0, na=np.nan, as_indexer=False):
 if (not case):
  flags |= re.IGNORECASE
 regex = re.compile(pat, flags=flags)
 if ((not as_indexer) and (regex.groups > 0)):
  warnings.warn('In future versions of pandas, match will change to always return a bool indexer.', FutureWarning, stacklevel=3)
 if (as_indexer and (regex.groups > 0)):
  warnings.warn('This pattern has match groups. To actually get the groups, use str.extract.', UserWarning, stacklevel=3)
 if ((not as_indexer) and (regex.groups > 0)):
  dtype = object
  def f(x):
   m = regex.match(x)
   if m:
    return m.groups()
   else:
    return []
 else:
  dtype = bool
  f = (lambda x: bool(regex.match(x)))
 return _na_map(f, arr, na, dtype=dtype)

def str_match ( arr , pat , case = True , flags = 0 , na = np . nan , as_indexer = False ) : if ( not case ) : flags |= re . IGNORECASE regex = re . compile ( pat , flags = flags ) if ( ( not as_indexer ) and ( regex . groups > 0 ) ) : warnings . warn ( 'In future versions of pandas, match will change to always return a bool indexer.' , FutureWarning , stacklevel = 3 ) if ( as_indexer and ( regex . groups > 0 ) ) : warnings . warn ( 'This pattern has match groups. To actually get the groups, use str.extract.' , UserWarning , stacklevel = 3 ) if ( ( not as_indexer ) and ( regex . groups > 0 ) ) : dtype = object def f ( x ) : m = regex . match ( x ) if m : return m . groups ( ) else : return [ ] else : dtype = bool f = ( lambda x : bool ( regex . match ( x ) ) ) return _na_map ( f , arr , na , dtype = dtype )
deprecated: find groups in each string in the series/index using passed regular expression .