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.