How to Remove Rows with Missing Values in a Specific Pandas DataFrame Column?

Dropping Pandas DataFrame Rows with Missing Values in a Specific Column

In data analysis, it's often necessary to deal with missing values. One common task is to remove rows with missing values in a particular column. For example, 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

To obtain a DataFrame with only rows where the "EPS" column is not null, we can use the following method:

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

This expression selects all the rows where the "EPS" column is not null and assigns the result to the new DataFrame df. The result is as follows:

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

By using the notna() method, we can effectively filter out missing values in the specified column and create a DataFrame that contains only the rows of interest.

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