Pre-processing steps with different features

I would like to include multiple features in a classifier for better improving the performance. I have a dataset similar to this one

Text                             is_it_capital?     is_it_upper?      contains_num?   Label
an example of text                      0                  0               0            0
ANOTHER example of text                 1                  1               0            1
What's happening?Let's talk at 5        1                  0               1            1

I am applying different pre-processing algorithms to Text (BoW, TF-IDF,...). It was 'easy' to use only Text column in my classifier by selecting X['Text'] and applying the algorithm of pre-processing. However, I would like to include now also is_it_capital? and the other variables (except Label) as features as I found them potentially useful for my classifier. What I tried was the following:

X=df[['Text','is_it_capital?', 'is_it_upper?', 'contains_num?']]
y=df['Label']

# Need to use DenseTransformer to properly concatenate results
# from CountVectorizer and other transformer steps
from sklearn.base import TransformerMixin
class DenseTransformer(TransformerMixin):
    def fit(self, X, y=None, **fit_params):
        return self
    def transform(self, X, y=None, **fit_params):
        return X.todense()

from sklearn.pipeline import Pipeline
pipeline = Pipeline([
     ('vectorizer', CountVectorizer()), 
     ('to_dense', DenseTransformer()), 
])

transformer = ColumnTransformer([('text', pipeline, 'Text')], remainder='passthrough')

X_train, X_test, y_train, y_test  = train_test_split(X, y, test_size=0.25, random_state=40)

X_train = transformer.fit_transform(X_train)
X_test = transformer.transform(X_test)

df_train = pd.concat([X_train, y_train], axis=1)
df_test = pd.concat([X_test, y_test], axis=1)

#Logistic regression logR_pipeline = Pipeline([ ('LogRCV',countV), ('LogR_clf',LogisticRegression()) ])

logR_pipeline.fit(df_train['Text'], df_train['Label'])
predicted_LogR = logR_pipeline.predict(df_test['Text'])
np.mean(predicted_LogR == df_test['Label'])
However I got the error: 

TypeError: cannot concatenate object of type '<class 'scipy.sparse.csr.csr_matrix'>'; only Series and DataFrame objs are valid

Is there anyone that handled with a similar problem? How could I fix it? My goal is to include all the features in my classifiers.

Read more here: https://stackoverflow.com/questions/66269321/pre-processing-steps-with-different-features