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
This means that if you pass a list of variables to impute and set
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
It is recommended to use
missing_only=True when not passing a list of variables to
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), data['SalePrice'], test_size=0.3, random_state=0)
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 missingdata_imputer.fit(X_train)
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
# Number of NA before the transformation X_train['LotFrontage'].isna().sum()
And after the imputation we should not have observations with NA:
# Number of NA after the transformation: train_t['LotFrontage'].isna().sum()
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 print(X_train.shape) print(train_t.shape)
(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.
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: