the following code depicts the dummy coding scheme on pokemon generation by dropping the first level binary encoded feature (gen in [ ]gen_dummy_features pd get_dummies(poke_df['generation']drop_first=truepd concat([poke_df[['name''generation']]gen_dummy_features]axis= iloc[ : out[ ]name generation gen gen gen gen gen octillery gen helioptile gen dialga gen deoxysdefense forme gen rapidash gen swanna gen if you wantyou can also choose to drop the last level binary encoded feature (gen by using the following code in [ ]gen_onehot_features pd get_dummies(poke_df['generation']gen_dummy_features gen_onehot_features iloc[:,:- pd concat([poke_df[['name''generation']]gen_dummy_features]axis= iloc[ : out[ ]name generation gen gen gen gen gen octillery gen helioptile gen dialga gen deoxysdefense forme gen rapidash gen swanna gen thus from these outputs you can see that based on the encoded level binary feature which we dropthat particular categorical value is represented by vector/encoded featureswhich all represent for example in the previous result feature setpokemon heloptile belongs to gen and is represented by all in the encoded dummy features effect coding scheme the effect coding scheme is very similar to the dummy coding scheme in most aspects howeverthe encoded features or feature vectorfor the category values that represent all in the dummy coding schemeis replaced by - in the effect coding scheme the following code depicts the effect coding scheme on the pokemon generation feature in [ ]gen_onehot_features pd get_dummies(poke_df['generation']gen_effect_features gen_onehot_features iloc[:,:- gen_effect_features loc[np all(gen_effect_features = axis= )- pd concat([poke_df[['name''generation']]gen_effect_features]axis= iloc[ : out[ ]name generation gen gen gen gen gen octillery gen helioptile gen - - - - - dialga gen