def predict(self, X): func = lambda x: self._eval_row(x) return eval_rows(X, func)
def transform(self, X):
X = column_or_1d(X, warn=True)
engine = _regex_engine(self.pattern)
func = lambda x: engine.sub(self.replacement, x)
Xt = eval_rows(X, func)
return _col2d(Xt)
def transform(self, X):
X = column_or_1d(X, warn=True)
func = lambda x: x[self.begin:self.end]
Xt = eval_rows(X, func)
return _col2d(Xt)
def transform(self, X):
X = column_or_1d(X, warn=True)
engine = _regex_engine(self.pattern)
func = lambda x: bool(engine.search(x))
Xt = eval_rows(X, func)
return _col2d(Xt)
def transform(self, X):
func = lambda x: self.separator.join([str(v) for v in x])
Xt = eval_rows(X, func)
return _col2d(Xt)
def transform(self, X):
func = lambda x: self._eval_row(x)
Xt = eval_rows(X, func)
if self.dtype is not None:
Xt = cast(Xt, self.dtype)
return _col2d(Xt)
def transform(self, X):
func = lambda x: self._eval_row(x)
Xt = eval_rows(X, func)
if hasattr(self, "dtype"):
Xt = cast(Xt, self.dtype)
return _col2d(Xt)
def transform(self, X):
func = lambda x: self._eval_row(x)
y = eval_rows(X, func)
if isinstance(y, Series):
y = y.values
return y.reshape(-1, 1)
