class feature_engine.preprocessing.MatchVariables(fill_value=nan, missing_values='raise', verbose=True)[source]#

MatchVariables() ensures that the same variables observed in the train set are present in the test set. If the dataset to transform contains variables that were not present in the train set, they are dropped. If the dataset to transform lacks variables that were present in the train set, these variables are added to the dataframe with a value determined by the user (np.nan by default).

train = pd.DataFrame({
    "Name": ["tom", "nick", "krish", "jack"],
    "City": ["London", "Manchester", "Liverpool", "Bristol"],
    "Age": [20, 21, 19, 18],
    "Marks": [0.9, 0.8, 0.7, 0.6],

test = pd.DataFrame({
    "Name": ["tom", "sam", "nick"],
    "Age": [20, 22, 23],
    "Marks": [0.9, 0.7, 0.6],
    "Hobbies": ["tennis", "rugby", "football"]

match_columns = MatchVariables()

df_transformed = match_columns.transform(test)

Note that in the returned dataframe, the variable “Hobbies” was removed and the variable “City” was added with np.nan:


    Name    City  Age  Marks
0    tom  np.nan   20    0.9
1    sam  np.nan   22    0.7
2   nick  np.nan   23    0.6

The order of the variables in the transformed dataset is also adjusted to match that observed in the train set.

More details in the User Guide.

fill_value: integer, float or string. Default=np.nan

The values for the variables that will be added to the transformed dataset.

missing_values: string, default=’ignore’

Indicates if missing values should be ignored or raised. If ‘ignore’, the transformer will ignore missing data when transforming the data. If ‘raise’ the transformer will return an error if the training or the datasets to transform contain missing values.

verbose: bool, default=True

If True, the transformer will print out the names of the variables that are added and / or removed from the dataset.


The variables present in the train set, in the order observed during fit.


The number of features in the train set used in fit.



Identify the variable names in the train set.


Add or delete variables to match those observed in the train set.


Fit to the data. Then transform it.

fit(X, y=None)[source]#

Learns and stores the names of the variables in the training dataset.

X: pandas dataframe of shape = [n_samples, n_features]

The input dataframe.

y: None

y is not needed for this transformer. You can pass y or None.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.


Estimator parameters.

selfestimator instance

Estimator instance.


Drops variables that were not seen in the train set and adds variables that were in the train set but not in the data to transform. In other words, it returns a dataframe with matching columns.

X: pandas dataframe of shape = [n_samples, n_features]

The data to transform.

X_new: Pandas dataframe, shape = [n_samples, n_features]

The dataframe with variables that match those observed in the train set.