I want to transform my dataframe so that the new DataFrame is of the same shape where each entry represents the number of consecutive NaNs counted after its position as follows:

IN:

```
A B
0 0.1880 0.345
1 0.2510 0.585
2 NaN NaN
3 NaN NaN
4 NaN 1.150
5 0.2300 1.210
6 0.1670 1.290
7 0.0835 1.400
8 0.0418 NaN
9 0.0209 NaN
10 NaN NaN
11 NaN NaN
12 NaN NaN
```

OUT:

```
A B
0 0 0
1 0 0
2 3 2
3 2 1
4 1 0
5 0 0
6 0 0
7 0 0
8 0 5
9 0 4
10 3 3
11 2 2
12 1 1
```

Similar question that I was trying to modify – Fast way to get the number of NaNs in a column counted from the last valid value in a DataFrame

### >Solution :

Inspired from this answer https://stackoverflow.com/a/52718619/3275464

```
from io import StringIO
import pandas as pd
s = """ A B
0 0.1880 0.345
1 0.2510 0.585
2 NaN NaN
3 NaN NaN
4 NaN 1.150
5 0.2300 1.210
6 0.1670 1.290
7 0.0835 1.400
8 0.0418 NaN
9 0.0209 NaN
10 NaN NaN
11 NaN NaN
12 NaN NaN """
df = pd.read_csv(StringIO(s), engine='python', sep='\s+')
_df = df.isna().iloc[::-1]
b = _df.cumsum()
c = b.sub(b.mask(_df).ffill().fillna(0)).astype(int).iloc[::-1]
c #gives the output you seem to want
```