You can use multiple boolean conditions to test if the current value and previous value are NaN
:
In [3]:
df = pd.DataFrame({'a':[1,3,np.NaN, np.NaN, 4, np.NaN, 6,7,8]})
df
Out[3]:
a
0 1
1 3
2 NaN
3 NaN
4 4
5 NaN
6 6
7 7
8 8
In [6]:
df[(df.a.isnull()) & (df.a.shift().isnull())]
Out[6]:
a
3 NaN
If you wanted to find where consecutive NaNs
occur where you are looking for more than 2 you could do the following:
In [38]:
df = pd.DataFrame({'a':[1,2,np.NaN, np.NaN, np.NaN, 6,7,8,9,10,np.NaN,np.NaN,13,14]})
df
Out[38]:
a
0 1
1 2
2 NaN
3 NaN
4 NaN
5 6
6 7
7 8
8 9
9 10
10 NaN
11 NaN
12 13
13 14
In [41]:
df.a.isnull().astype(int).groupby(df.a.notnull().astype(int).cumsum()).sum()
Out[41]:
a
1 0
2 3
3 0
4 0
5 0
6 0
7 2
8 0
9 0
Name: a, dtype: int32