How can I one hot encode in Python?

I have a machine learning classification problem with 80% categorical variables. Must I use one hot encoding if I want to use some classifier for the classification? Can i pass the data to a classifier without the encoding?

I am trying to do the following for feature selection:

1. I read the train file:num_rows_to_read = 10000 train_small = pd.read_csv("../../dataset/train.csv", nrows=num_rows_to_read)
2. I change the type of the categorical features to ‘category’:non_categorial_features = ['orig_destination_distance', 'srch_adults_cnt', 'srch_children_cnt', 'srch_rm_cnt', 'cnt'] for categorical_feature in list(train_small.columns): if categorical_feature not in non_categorial_features: train_small[categorical_feature] = train_small[categorical_feature].astype('category')
3. I use one hot encoding:train_small_with_dummies = pd.get_dummies(train_small, sparse=True)

The problem is that the 3’rd part often get stuck, although I am using a strong machine.

Thus, without the one hot encoding I can’t do any feature selection, for determining the importance of the features.

What do you recommend?