The DropMissingData() will delete rows containing missing values. It provides similar functionality to pandas.drop_na(). The transformer has however some advantages over pandas:

  • it learns and stores the variables for which the rows with na should be deleted

  • it can be used within the Scikit-learn pipeline

It works with numerical and categorical variables. You can pass a list of variables to impute, or the transformer will select and impute all variables.

The trasformer has the option to learn the variables with missing data in the train set, and then remove observations with NA only in those variables. Or alternatively remove observations with NA in all variables. You can change the behaviour using the parameter missing_only.

This means that if you pass a list of variables to impute and set missing_only=True, and some of the variables in your list do not have missing data in the train set, missing data will not be removed during transform for those particular variables. In other words, when missing_only=True, the transformer “double checks” that the entered variables have missing data in the train set. If not, it ignores them during transform().

It is recommended to use missing_only=True when not passing a list of variables to impute.

Below a code example using the House Prices Dataset (more details about the dataset here).

First, let’s load the data and separate it into train and test:

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

from feature_engine.imputation import DropMissingData

# Load dataset
data = pd.read_csv('houseprice.csv')

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1),

Now, we set up the imputer to remove observations if they have missing data in any of the variables indicated in the list.

# set up the imputer
missingdata_imputer = DropMissingData(variables=['LotFrontage', 'MasVnrArea'])

# fit the imputer

Now, we can go ahead and add the missing indicators:

# transform the data
train_t= missingdata_imputer.transform(X_train)
test_t= missingdata_imputer.transform(X_test)

We can explore the number of observations with NA in the variable LotFrontage before the imputation:

# Number of NA before the transformation

And after the imputation we should not have observations with NA:

# Number of NA after the transformation:

We can go ahead and compare the shapes of the different dataframes, before and after the imputation, and we will see that the imputed data has less observations, because those with NA in any of the 2 variables of interest were removed.

# Number of rows before and after transformation
(1022, 79)
(829, 79)

Drop partially complete rows#

The default behaviour of DropMissingData() will drop rows in NA is present in any of the variables indicated in the list.

We have the option of dropping rows only if a certain percentage of values is missing across all variables.

For example, if we set the parameter threshold=0.5, a row will be dropped if data is missing in 50% of the variables. If we set the parameter threshold=0.01, a row will be dropped if data is missing in 1% of the variables. If we set the parameter threshold=1, a row will be dropped if data is missing in all the variables.

More details#

In the following Jupyter notebook you will find more details on the functionality of the DropMissingData(), including how to select numerical variables automatically.

For more details about this and other feature engineering methods check out these resources: