Select missing values pandas dataframe

I have a dataset where apparently there are no null values:

dataset.isnull().sum()
Patient           0
City              0
DOB               0
Gender            0
Gender_isspace    0
dtype: int64

However, if a I do for example:

sns.countplot(data=Patients, x='Gender')

There are three columns, M, F and another column with no name and at least 25% of the values.

How can I select this missing values? And delete them?

Answer

Create a boolean mask to get ‘M’, ‘F’ rows and reverse the mask:

df[~dataset['Gender'].isin(['M', 'F'])]