Pandas categorical variables encoding for regression (one-hot encoding vs dummy encoding)
One advantage of get_dummies is that it can operate on values other than integers (so you don't need the LabelEncoder) and returns a DataFrame with the categories as column names. Also, you can conveniently drop one redundant category using drop_first=True. 

One advantage of scikit-learn's OneHoteEncoder lies in the scikit-learn API. OHE gives you a transformer which you can apply to your training and test set separately if you specify the total number of categories. This doesn't work with get_dummies ,for example, if the training set misses categories present in the test set.

You can still delete categories by simply deleting columns from the resulting numpy array (e.g. using n_values_ or feature_indices_ to see which columns correspond to the same feature). Some models work regardless, for example tree-based models. Also, L1 regularization can often set redundant features to zero (see Lasso regression).