SAMUEL MIRA AQUI

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array

# Elimina todas las variables que tengan un porcentaje de valores
# iguales a 0 mayor que 0.4
class MissingValuesTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, missing_values=0, missing_percentage=0.4):
        self.missing_values = missing_values
        self.missing_percentage = missing_percentage

    def fit(self, X, y=None):
        X = check_array(X)
        self.n_features_ = X.shape[1]
        self.columns_ = np.sum(
            X == self.missing_values,
            axis=0) / X.shape[0] <= self.missing_percentage
        return self

    def transform(self, X, y=None):
        X = check_array(X)
        if self.n_features_ != X.shape[1]:
            raise ValueError("Se han recibido de entrada {} características cuando se esperaban {}.".format(X.shape[1],self.n_features_))
        return X[:, self.columns_]

pima_remover = MissingValuesTransformer()