Identify leading and trailing NAs in pandas DataFrame

Question

Is there a way to identify leading and trailing NAs in a pandas.DataFrame

Currently I do the following but it seems not straightforward:

import pandas as pd
df = pd.DataFrame(dict(a=[0.1, 0.2, 0.2],
                       b=[None, 0.1, None],
                       c=[0.1, None, 0.1]) 
lead_na = (df.isnull() == False).cumsum() == 0
trail_na = (df.iloc[::-1].isnull() == False).cumsum().iloc[::-1] == 0
trail_lead_nas = top_na | trail_na

Any ideas how this could be expressed more efficiently?

Answer:

%timeit df.ffill().isna() | df.bfill().isna()
The slowest run took 29.24 times longer than the fastest. This could mean that 
an intermediate result is being cached.
31 ms ± 25.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ((df.isnull() == False).cumsum() == 0) | ((df.iloc[::-1].isnull() ==False).cumsum().iloc[::-1] == 0)
255 ms ± 66.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Answer 1

How about this

df.ffill().isna() | df.bfill().isna()

Out[769]:
       a      b      c
0  False   True  False
1  False  False  False
2  False   True  False

---

df = pd.concat([df] * 1000, ignore_index=True)

In [134]: %%timeit
     ...: lead_na = (df.isnull() == False).cumsum() == 0
     ...: trail_na = (df.iloc[::-1].isnull() == False).cumsum().iloc[::-1] == 0
     ...: trail_lead_nas = lead_na | trail_na
     ...: 
11.8 ms ± 105 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [135]: %%timeit
     ...: df.ffill().isna() | df.bfill().isna()
     ...: 
2.1 ms ± 50 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)