Select rows from a DataFrame based on presence of null value in specific column or columns
<p>I have an imported xls file as pandas dataframe, there are two columns containing coordinates which i will use to merge the dataframe with others which have geolocation data. df.info() shows 8859 records, the coordinatess columns have '8835 non-null float64' records. </p>

<p>I want to eyeball the 24 rows (that i assume are null) with all columns records to see if one of the other columns (street address town) can't be used to manually add back the coordinates for those 24 records. Ie. return dataframe for column in df.['Easting'] where isnull or NaN</p>

<p>I have adapted the method given <a href="https://stackoverflow.com/questions/17071871/select-rows-from-a-dataframe-based-on-values-in-a-column-in-pandas">here</a> as below;</p>

<pre><code>df.loc[df['Easting'] == NaN]
</code></pre>

<p>But get back an empty dataframe (0 rows × 24 columns), which makes no sense (to me). Attempting to use Null or Non null doesn't work as these values aren't defined. What am i missing?</p>