Fill forward a DataFrame with matching values - pandas

Question

I have a DataFrame of booleans. I would like to replace the 2 False values that are directly positioned after a True value. I thought the .replace() method would do it since the 5th example seems to be what I am looking for.
Here is what I do:
dataIn = pd.DataFrame([False, False, False, True, False, False, False, False])
dataOut = dataIn.replace(to_replace=False, method='ffill', limit=2)
>>> TypeError: No matching signature found
Here is the output I am looking for:
dataOut = pd.DataFrame([False, False, False, True, True, True, False, False])

Answer 1

# create a series not a dateframe
# if you have a dataframe then assign to a new variable as a series
# s = df['bool_col']
s = pd.Series([False, True, False, True, False, False, False, False])
# create a mask based on the logic using shift
mask = (s == False) & (((s.shift(1) == True) & (s.shift(-1) == False))\
| ((s.shift(2) == True) & (s.shift(1) == False)))
# numpy.where to create the new output
np.where(mask, True, s)
# array([False, True, False, True, True, True, False, False])
# assign to a new column in the frame (if you want)
# df['new_col'] = np.where(mask, True, s)

Answer 2

Define a function which conditionally replaces 2 first elements with True:
def condRepl(grp):
rv = grp.copy()
if grp.size >= 2 and grp.eq(False).all():
rv.iloc[0:2] = [True] * 2
return rv
The condition triggering this replace is:
group has 2 elements or more,
the group is composed solely of False values.
Then, using this function, transform each group of "new" values
(each change in the value starts a new group):
dataIn[0] = dataIn[0].groupby(s.ne(s.shift()).cumsum()).transform(condRepl)

Answer 3

Thanks for both answers above. But actually, it seems the .replace() can be used, but it does not entirely handle booleans.
By replacing them temporarily by int, it is possible to use it:
dataIn = pd.DataFrame([False, False, False, True, False, False, False, False])
dataOut = dataIn.astype(int).replace(to_replace=False, method='ffill', limit=2).astype(bool)