I have a DataFrame where I would like to keep the rows when a particular variable has a `NaN` value and drop the non-missing values.

Example:

```
ticker  opinion  x1       x2  
aapl    GC       100      70  
msft    NaN      50       40  
goog    GC       40       60  
wmt     GC       45       15  
abm     NaN      80       90  

```

In the above DataFrame, I would like to drop all observations where opinion is not missing (so, I would like to drop the rows where ticker is `aapl, goog, and wmt`).

Is there anything in pandas that is the opposite to `.dropna()`?