How to Filter a Pandas DataFrame to Keep Only Rows with Non-NaN Values in a Specific Column?

How to Isolate Non-NaN Values in a Column of a Pandas DataFrame

Question:

Consider a DataFrame like this:

STK_ID RPT_Date                   <br>601166 20111231  601166  NaN   NaN<br>600036 20111231  600036  NaN    12<br>600016 20111231  600016  4.3   NaN<br>601009 20111231  601009  NaN   NaN<br>601939 20111231  601939  2.5   NaN<br>000001 20111231  000001  NaN   NaN<br>

Goal:

Isolate the records where the "EPS" column is not NaN, resulting in this DataFrame:

STK_ID RPT_Date                   <br>600016 20111231  600016  4.3   NaN<br>601939 20111231  601939  2.5   NaN<br>

Solution:

Instead of dropping rows, you can filter the DataFrame using the notna() method to select only the rows where the "EPS" column is not NaN:

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

This will create a new DataFrame with the desired result.

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