Given a small dataset as follow:

```
   *id *building  floor_number *floor_name
0    1         A           8.0          5F
1    2         A           4.0          4F
2    3         A           NaN          3F
3    4         A           NaN         NaN
4    5         A           NaN         NaN
5    6         B          14.0         17F
6    7         B          13.0         16F
7    8         B          20.0       world
8    9         B          13.0       hello
9   10         B          13.0         16F

```

I want to check if the containing `*` columns (`selected_col` as below), have `NaN` values, so I need to write a condition code for filtering `NaN` rows for `selected_col`.

```
selected_col = df.columns[df.columns.str.contains(pat = '^\*')]
...

```

How could I filter out rows as follows? Thanks.

```
   *id *building  floor_number *floor_name
3    4         A           NaN         NaN
4    5         A           NaN         NaN

```

**EDIT:** since I need to hightlight `NaN` cell for the columns with containing `*` (which means this column shouldn't have any `NaN`s), so I want to use `np.where` if possible.

```
np.where(conditions, None, 'contains NaNs for required columns')

```

Reference related: [Highlight dataframe cells based on multiple conditions in Python](https://stackoverflow.com/questions/64766324/highlight-dataframe-cells-based-on-multiple-conditions-in-python)