How to Remove Rows with NaN Values from a Specific Pandas DataFrame Column?

How to Remove NaN Values from a Specific Column in Pandas DataFrame

When working with Pandas DataFrames, it's essential to handle missing data effectively. One common task is to remove rows where a particular column contains NaN values.

Scenario:

Consider the following DataFrame:

                   STK_ID  EPS  cash
STK_ID RPT_Date                   
601166 20111231  601166  NaN   NaN
600036 20111231  600036  NaN    12
600016 20111231  600016  4.3   NaN
601009 20111231  601009  NaN   NaN
601939 20111231  601939  2.5   NaN
000001 20111231  000001  NaN   NaN

The goal is to remove all rows where the 'EPS' column contains NaN values, resulting in the following DataFrame:

                   STK_ID  EPS  cash
STK_ID RPT_Date                   
600016 20111231  600016  4.3   NaN
601939 20111231  601939  2.5   NaN

Solution:

To accomplish this task, you can use the df.dropna() method, which drops rows where any value in the specified column is NaN. However, in this case, you only want to remove rows where the 'EPS' column contains NaN. To apply this specifically to the 'EPS' column, use the following code:

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

This code checks each row in the DataFrame if the value in the 'EPS' column is not NaN, and if it is not, it keeps the row. If it is NaN, it drops the row. The resulting DataFrame will contain only the rows where the 'EPS' column has non-NaN values.

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