Identifying consecutive NaN's with pandas

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

If you wish to map this back to the original index, or have a consective count of NaNs use Ed's answer with cumsum instead of sum. This is particularly useful for visualising NaN groups in time series:

df = pd.DataFrame({'a':[
    1,2,np.NaN, np.NaN, np.NaN, 6,7,8,9,10,np.NaN,np.NaN,13,14
]})

df.a.isnull().astype(int).groupby(df.a.notnull().astype(int).cumsum()).cumsum()


0     0
1     0
2     1
3     2
4     3
5     0
6     0
7     0
8     0
9     0
10    1
11    2
12    0
13    0
Name: a, dtype: int64

for example,

pd.concat([
        df,
        (
            df.a.isnull().astype(int)
            .groupby(df.a.notnull().astype(int).cumsum())
            .cumsum().to_frame('consec_count')
        )
    ],
    axis=1
)

    a       consec_count
0   1.0     0
1   2.0     0
2   NaN     1
3   NaN     2
4   NaN     3
5   6.0     0
6   7.0     0
7   8.0     0
8   9.0     0
9   10.0    0
10  NaN     1
11  NaN     2
12  13.0    0
13  14.0    0