How to drop rows of Pandas DataFrame whose value in a certain column is NaN

Question

I have this DataFrame and want only the records whose EPS column is not NaN:

>>> df
                 STK_ID           


        

Answer 1

Simplest of all solutions:

filtered_df = df[df['EPS'].notnull()]

The above solution is way better than using np.isfinite()

Answer 2

Another version:

df[~df['EPS'].isna()]

Answer 3

You can use this:

df.dropna(subset=['EPS'], how='all', inplace=True)

Answer 4

It may be added at that '&' can be used to add additional conditions e.g.

df = df[(df.EPS > 2.0) & (df.EPS <4.0)]

Notice that when evaluating the statements, pandas needs parenthesis.

Answer 5

Don't drop, just take the rows where EPS is not NA:

df = df[df['EPS'].notna()]

Answer 6

I know this has already been answered, but just for the sake of a purely pandas solution to this specific question as opposed to the general description from Aman (which was wonderful) and in case anyone else happens upon this:

import pandas as pd
df = df[pd.notnull(df['EPS'])]