DropConstantFeatures#

The DropConstantFeatures() drops constant and quasi-constant variables from a dataframe. By default, it drops only constant variables. Constant variables have a single value. Quasi-constant variables have a single value in most of its observations.

This transformer works with numerical and categorical variables, and it offers a pretty straightforward way of reducing the feature space. Be mindful though, that depending on the context, quasi-constant variables could be useful.

Example

Let’s see how to use DropConstantFeatures() in an example with the Titanic dataset. We first load the data and separate it into train and test:

from sklearn.model_selection import train_test_split
from feature_engine.datasets import load_titanic
from feature_engine.selection import DropConstantFeatures

X, y = load_titanic(
    return_X_y_frame=True,
    handle_missing=True,
)


X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=0,
)

Now, we set up the DropConstantFeatures() to remove features that show the same value in more than 70% of the observations:

# set up the transformer
transformer = DropConstantFeatures(tol=0.7)

With fit() the transformer finds the variables to drop:

# fit the transformer
transformer.fit(X_train)

The variables to drop are stored in the attribute features_to_drop_:

transformer.features_to_drop_

['parch', 'cabin', 'embarked', 'body']

We see in the following code snippets that for the variables parch and embarked, more than 70% of the observations displayed the same value:

X_train['embarked'].value_counts(normalize = True)

S          0.711790
C          0.195415
Q          0.090611
Missing    0.002183
Name: embarked, dtype: float64

71% of the passengers embarked in S.

X_train['parch'].value_counts(normalize = True)

0    0.771834
1    0.125546
2    0.086245
3    0.005459
4    0.004367
5    0.003275
6    0.002183
9    0.001092
Name: parch, dtype: float64

77% of the passengers had 0 parent or child. Because of this, these features were deemed constant and removed.

With transform(), we can go ahead and drop the variables from the data:

train_t = transformer.transform(X_train)
test_t = transformer.transform(X_test)

print(train_t.head())

We see the resulting dataframe below:

      pclass                               name     sex        age  sibsp  \
501        2  Mellinger, Miss. Madeleine Violet  female  13.000000      0
588        2                  Wells, Miss. Joan  female   4.000000      1
402        2     Duran y More, Miss. Florentina  female  30.000000      1
1193       3                 Scanlan, Mr. James    male  29.881135      0
686        3       Bradley, Miss. Bridget Delia  female  22.000000      0

             ticket     fare     boat  \
501          250644  19.5000       14
588           29103  23.0000       14
402   SC/PARIS 2148  13.8583       12
1193          36209   7.7250  Missing
686          334914   7.7250       13

                                              home.dest
501                            England / Bennington, VT
588                                Cornwall / Akron, OH
402                     Barcelona, Spain / Havana, Cuba
1193                                            Missing
686   Kingwilliamstown, Co Cork, Ireland Glens Falls...

More details#

In this Kaggle kernel we use DropConstantFeatures() together with other feature selection algorithms:

All notebooks can be found in a dedicated repository.