[SOLVED] Keeping NaN values and dropping nonmissing values

Keeping NaN values and dropping nonmissing values

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()?

Solution

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()]