I have a Pandas dataframe where some rows have missing values (`NaN`

) in a certain column. I want to drop all the rows where the value in that column is `NaN`

. How can I do this?

Here’s an example of my dataframe:

This is the output of this code:

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

As you can see, the value in column `B`

of row 2 is `None`

(which is equivalent to `NaN`

). I want to drop that row.

Here’s the code I’ve tried so far:

```
df.dropna(subset=['A'], inplace=True)
print(df)
```

However, this doesn’t seem to work. Can anyone help me figure out what I’m doing wrong here? And are there any alternative ways to drop rows with NaN values in a certain column? Thanks for any help you can provide.