How to handle missing values (NaN) in categorical data when using scikit-learn OneHotEncoder?

I have recently started learning python to develop a predictive model for a research project using machine learning methods. I have a large dataset comprised of both numerical and categorical data. The dataset has lots of missing values. I am currently trying to encode the categorical features using OneHotEncoder. When I read about OneHotEncoder, my understanding was that for a missing value (NaN), OneHotEncoder would assign 0s to all the feature's categories, as such:

0     Male 1     Female2     NaN

After applying OneHotEncoder:

0     10 1     012     00

However, when running the following code:

    # Encoding categorical data    from sklearn.compose import ColumnTransformer    from sklearn.preprocessing import OneHotEncoder    ct = ColumnTransformer([('encoder', OneHotEncoder(handle_unknown='ignore'), [1])],                           remainder='passthrough')    obj_df = np.array(ct.fit_transform(obj_df))    print(obj_df)

I am getting the error ValueError: Input contains NaN

So I am guessing my previous understanding of how OneHotEncoder handles missing values is wrong. Is there a way for me to get the functionality described above? I know imputing the missing values before encoding will resolve this issue, but I am reluctant to do this as I am dealing with medical data and fear that imputation may decrease the predictive accuracy of my model.

I found this question that is similar but the answer doesn't offer a detailed enough solution on how to deal with the NaN values.

Let me know what your thoughts are, thanks.