s = pd.Series([.4, .5, .6], list('abc'))
s

a    0.4
b    0.5
c    0.6
dtype: float64

pd.Series(np.ones_like(s.values), s.index, name=s.name)

a    1.0
b    1.0
c    1.0
dtype: float64

np.random.seed(42)
df = pd.DataFrame(np.random.randn(10**6,), columns=['A'])
# Populate values with Nans
df.loc[df.sample(frac=0.5).index] = np.NaN

df.shape
# (1000000, 1)

def fill_ones_with_modify():
    ser = df['A'].copy(deep=False)     # use copy() → without modifying the original DF
    ser.values.fill(1)
    return ser

%timeit fill_ones_with_modify()
1000 loops, best of 3: 837 µs per loop

def fill_ones_without_modify():
    ser = df[['A']].copy(deep=False).squeeze()
    ser.values.fill(1)
    return ser

%timeit fill_ones_without_modify()
100 loops, best of 3: 6.4 ms per loop

>>> a = pandas.Series(np.array([0,np.nan,2,3,4]), list('abcde'))
>>> a
a    0.0
b    NaN
c    2.0
d    3.0
e    4.0
dtype: float64
>>> (a/a).fillna(1)
a    1.0
b    1.0
c    1.0
d    1.0
e    1.0
dtype: float64

pd.Series(1, s.index, name=s.name)

s = pd.Series(5, range(int(1e6)))

%timeit pd.Series(1, s.index, name=s.name)
%timeit pd.Series(np.ones(s.shape), s.index, name=s.name)
%timeit fill_ones_with_modify(s)
%timeit s.div(s).fillna(1)

413 µs ± 2.03 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
375 µs ± 2.04 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
369 µs ± 975 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
3.47 ms ± 12.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)