I have dataframe

```
    site1   time1   site2   time2   site3   time3   site4   time4   site5   time5   ... time6   site7   time7   site8   time8   site9   time9   site10  time10  target
 session_id                                                                                 

21669   56  2013-01-12 08:05:57 55.0    2013-01-12 08:05:57 NaN NaT NaN NaT NaN NaT ... NaT NaN NaT NaN NaT NaN NaT NaN NaT 0
54843   56  2013-01-12 08:37:23 55.0    2013-01-12 08:37:23 56.0    2013-01-12 09:07:07 55.0    2013-01-12 09:07:09 NaN NaT ... NaT NaN NaT NaN NaT NaN NaT NaN NaT 0
77292   946 2013-01-12 08:50:13 946.0   2013-01-12 08:50:14 951.0   2013-01-12 08:50:15 946.0   2013-01-12 08:50:15 946.0   2013-01-12 08:50:16 ... 2013-01-12 08:50:16 948.0   2013-01-12 08:50:16 784.0   2013-01-12 08:50:16 949.0   2013-01-12 08:50:17 946.0   2013-01-12 08:50:17 0
114021  945 2013-01-12 08:50:17 948.0   2013-01-12 08:50:17 949.0   2013-01-12 08:50:18 948.0   2013-01-12 08:50:18 945.0   2013-01-12 08:50:18 ... 2013-01-12 08:50:18 947.0   2013-01-12 08:50:19 945.0   2013-01-12 08:50:19 946.0   2013-01-12 08:50:19 946.0   2013-01-12 08:50:20 0

```

I need to count N of columns, where site != NaN.
I try to use

```
df[['site%s' % i for i in range(1, 11)]].count(axis=1)

```

but it returns me 10 to every id

Also I have tried

```
train_df[sites].notnull().count(axis=1)

```

and it also didn't help.

Desire output

```
21669    2
54843    4
77292    10
114021   10

```