Keeping NaN values and dropping nonmissing values

Question

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()?

Answer 1

Use pandas.Series.isnull on the column to find the missing values and index with the result.

import pandas as pd  data = pd.DataFrame({'ticker': ['aapl', 'msft', 'goog'],                      'opinion': ['GC', nan, 'GC'],                      'x1': [100, 50, 40]})  data = data[data['opinion'].isnull()]