Use DataFrame.where
for forward filling by mask - testing only non missing values by back filling them:
df = df.where(df.bfill().isna(), df.ffill())
print (df)
a b c
2020-01-01 NaN 11.0 NaN
2020-02-01 1.0 NaN NaN
2020-03-01 1.0 NaN 14.0
2020-04-01 2.0 NaN 14.0
2020-05-01 2.0 NaN 14.0
2020-06-01 2.0 NaN 15.0
2020-07-01 3.0 NaN NaN
2020-08-01 NaN NaN NaN
Your solution should be used too if compare Series converted to numpy array with broadcasting:
mask = df.notna()[::-1].idxmax().to_numpy() < df.index.to_numpy()[:, None]
df = df.where(mask, df.ffill())
print (df)
a b c
2020-01-01 NaN 11.0 NaN
2020-02-01 1.0 NaN NaN
2020-03-01 1.0 NaN 14.0
2020-04-01 2.0 NaN 14.0
2020-05-01 2.0 NaN 14.0
2020-06-01 2.0 NaN 15.0
2020-07-01 3.0 NaN NaN
2020-08-01 NaN NaN NaN
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