Got some troubles with using OneHotEncoder to multiple categories

I'm trying to get the final pipeline on the titanic dataset(Example was taken from the 'Hands-on ML' book).

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelBinarizer

num_pipeline = Pipeline([
    ('selector', DataFrameSelector(list(df_num))),
    ('imputer',SimpleImputer(strategy='median', fill_value='num',missing_values=np.nan)),
    ('std_scaler',StandardScaler())
])
cat_pipeline = Pipeline([
    ('selector', DataFrameSelector(list(df_cat))),
    ('imputer',SimpleImputer(strategy='most_frequent', fill_value='categorical',missing_values=np.nan)),
    ('cat_encoder', OneHotEncoder(sparse=False)),
])

from sklearn.pipeline import FeatureUnion


full_pipeline = FeatureUnion(transformer_list=[
        (num_pipeline, num_pipeline),
        (cat_pipeline, cat_pipeline),
    ])

df_prepared = full_pipeline.fit_transform(df)
df_prepared.shape
df_total = pd.DataFrame(df_prepared, columns=df.columns)
df_total

Where

df_num = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
df_cat = ['Sex', 'Embarked']

The problem is that I got the ValueError:

ValueError: Shape of passed values is (668, 10), indices imply (668, 7)

I tried dropping 'Embarked' from the df_cat and it created the DF somehow(but it was inaccurate). I assume it didn't work because of 'Embarked' had 3 categories(10-3 = 7 - that's the number of columns I wanted.)

If I drop 'Sex' category, I get ValueError: Shape of passed values is (668, 8), indices imply (668, 7) (10-2=8)

How to encode all two categories using Pipeline() and which wrong steps I've made?