nan + nan = 0 when  skipna=True or when groupby().sum()
**Disclaimer**: Not sure whether this is expected behavior. I would have not expected it though and it is always bad in my use cases (survey data).

Example:
```python
data = pd.DataFrame(
    data = {
        "var1": [np.nan, 1000, 2000],
        "var2": [np.nan, 100, np.nan],
        "hh_id" : [1, 2, 2] 
    }
)
```

**Normal sum**
```
data[["var1", "var2"]].sum(axis=1)
```

Expected output:
```
0      np.nan
1    1100.0
2    2000.0
dtype: float64
```

Output:

```
0       0.0
1    1100.0
2    2000.0
dtype: float64
```

**Groupby sum**
```python
data.groupby("hh_id")["var2"].sum()
```

Expected output:
```

hh_id
1      np.nan
2    100.0
Name: var2, dtype: float64


```

Output
```

hh_id
1      0.0
2    100.0
Name: var2, dtype: float64


```
