How can I drop rows from a Pandas DataFrame where a specific column has missing values (NaN)?

I have a Pandas dataframe in which certain rows contain missing values (NaN) in a specific column. I intend to remove these rows where the value in that column is NaN. Here’s an illustrative example of my dataframe:

df = pd.DataFrame({
       'A': [1, 2, 3, 4],
       'B': ['foo', 'bar', None, 'baz']

The current state of the dataframe is as follows:

   A    B
0  1  foo
1  2  bar
2  3  None
3  4  baz

In this instance, the value in column B for row 3 is None (equivalent to NaN). My objective is to eliminate all such rows. I would greatly appreciate any guidance on different approaches to achieve this task of removing rows with NaN values in a specified column.

Hey @sabih! I’ve got a solution for your problem using the dropna() method. This method is quite handy when it comes to handling missing data. You simply specify the column containing the NaN values, and the method will return a new dataframe with those rows removed. Here is how you can use this method:

Hello @sabih! You can employ boolean indexing to address this problem. Boolean indexing involves generating a mask according to specific conditions and subsequently using that mask to filter your DataFrame. Let’s explore how you can utilize this technique: