Rename MeanMedianImputer to MeanImputer

docs/api_doc/imputation/MeanImputer.rst

@@ -0,0 +1,6 @@
+MeanImputer
+===========
+
+.. autoclass:: feature_engine.imputation.MeanImputer
+    :members:
+

docs/api_doc/imputation/index.rst

@@ -12,7 +12,7 @@ The following table summarises each imputer's functionality:
 ================================== ===================== ======================= ====================================================================================
     Transformer                     Numerical variables	  Categorical variables	    Description
 ================================== ===================== ======================= ====================================================================================
-:class:`MeanMedianImputer()`	        √	                 ×	                    Replaces missing values with the mean or median
+:class:`MeanImputer()`	        √	                 ×	                    Replaces missing values with the mean or median
 :class:`ArbitraryNumberImputer()`	    √	                 ×	                    Replaces missing values with an arbitrary value
 :class:`EndTailImputer()`	            √	                 ×	                    Replaces missing values with a value at the end of the distribution
 :class:`CategoricalImputer()`           ×	                 √	                    Replaces missing values with the most frequent category or an arbitrary string
@@ -27,7 +27,7 @@ Imputers
 .. toctree::
    :maxdepth: 1
 
-   MeanMedianImputer
+   MeanImputer
    ArbitraryNumberImputer
    EndTailImputer
    CategoricalImputer

docs/index.rst

@@ -179,7 +179,7 @@ Missing Data Imputation: Imputers
 Missing data imputation consists in replacing missing values in categorical data and numerical variables with estimates
 of those nan values or arbitrary data points. Feature-engine supports the following missing data imputation methods:
 
-- :doc:`api_doc/imputation/MeanMedianImputer`: replaces missing data in numerical variables with the mean or median
+- :doc:`api_doc/imputation/MeanImputer`: replaces missing data in numerical variables with the mean or median
 - :doc:`api_doc/imputation/ArbitraryNumberImputer`: replaces missing data in numerical variables with an arbitrary number
 - :doc:`api_doc/imputation/EndTailImputer`: replaces missing data in numerical variables with numbers at the distribution tails
 - :doc:`api_doc/imputation/CategoricalImputer`: replaces missing data with an arbitrary string or with the most frequent category

docs/quickstart/index.rst

@@ -57,7 +57,7 @@ imputation.
     from sklearn.datasets import fetch_openml
     from sklearn.model_selection import train_test_split
 
-    from feature_engine.imputation import MeanMedianImputer
+    from feature_engine.imputation import MeanImputer
 
     # Load dataset
     X, y = fetch_openml(
@@ -76,7 +76,7 @@ imputation.
     )
 
     # set up the imputer
-    median_imputer = MeanMedianImputer(
+    median_imputer = MeanImputer(
         imputation_method='median',
         variables=['LotFrontage', 'MasVnrArea']
     )
@@ -127,7 +127,7 @@ pickle (.pkl). Here is an example of how to do it:
     from feature_engine.discretisation import DecisionTreeDiscretiser
     from feature_engine.imputation import (
         AddMissingIndicator,
-        MeanMedianImputer,
+        MeanImputer,
         CategoricalImputer,
     )
 
@@ -172,7 +172,7 @@ pickle (.pkl). Here is an example of how to do it:
         ('continuous_var_imputer', AddMissingIndicator(variables=['LotFrontage'])),
 
         # Replace NA with the median in 2 variables
-        ('continuous_var_median_imputer', MeanMedianImputer(
+        ('continuous_var_median_imputer', MeanImputer(
             imputation_method='median', variables=['LotFrontage', 'MasVnrArea']
         )),
 

docs/user_guide/imputation/MeanMedianImputer.rst → docs/user_guide/imputation/MeanImputer.rst

@@ -1,9 +1,9 @@
-.. _mean_median_imputer:
+.. _mean_imputer:
 
 .. currentmodule:: feature_engine.imputation
 
-MeanMedianImputer
-=================
+MeanImputer
+===========
 
 Mean imputation and median imputation consist of replacing missing data in numerical 
 variables with the variable's mean or median. These simple univariate missing data 
@@ -74,18 +74,18 @@ used with decision tree-based algorithms. When the relationship among the variab
 crucial, you might want to consider better ways to estimate the missing data, such as 
 multiple imputation (aka, multivariate imputation). 
 
-MeanMedianImputer
------------------
+MeanImputer
+-----------
 
-Feature-engine's :class:`MeanMedianImputer()` replaces missing data with the variable's 
+Feature-engine's :class:`MeanImputer()` replaces missing data with the variable's 
 mean or median value, determined over the observed values. Hence, it can only impute
 numerical variables. You can pass the 
-list of variables you want to impute, or alternatively, :class:`MeanMedianImputer()` 
+list of variables you want to impute, or alternatively, :class:`MeanImputer()` 
 will automatically impute all numerical variables in the training set.
 
 .. attention::
 
-    **New in version 2.0:** When `variables` is `None`, :class:`MeanMedianImputer()` used to
+    **New in version 2.0:** When `variables` is `None`, :class:`MeanImputer()` used to
     raise an error if the dataframe contained no numerical variables. You can now
     set the new parameter `return_empty` to `True` to make the transformer return an
     empty list of variables and skip the imputation instead, leaving the dataframe
@@ -97,7 +97,7 @@ will automatically impute all numerical variables in the training set.
 Python implementation
 ---------------------
 
-In this section, we will explore :class:`MeanMedianImputer()`'s functionality. Let's start by 
+In this section, we will explore :class:`MeanImputer()`'s functionality. Let's start by 
 importing the required libraries:
 
 .. code:: python
@@ -107,7 +107,7 @@ importing the required libraries:
 	from sklearn.datasets import fetch_openml
 	from sklearn.pipeline import make_pipeline
 	from sklearn.model_selection import train_test_split
-	from feature_engine.imputation import MeanMedianImputer
+	from feature_engine.imputation import MeanImputer
 	from feature_engine.imputation import AddMissingIndicator
 
 
@@ -156,13 +156,13 @@ either **LotFrontage** or **MasVnrArea**:
 	1018      Gilbert          NaN        76.0
 
 
-Let's now set up and fit :class:`MeanMedianImputer()` with the strategy set to mean,
+Let's now set up and fit :class:`MeanImputer()` with the strategy set to mean,
 so we can impute the variables `LotFrontage` and `MasVnrArea`:
 
 .. code:: python
 
 	# Set up the imputer
-	mmi = MeanMedianImputer(
+	mmi = MeanImputer(
 		imputation_method='mean',
 		variables=['LotFrontage', 'MasVnrArea']
 	)
@@ -173,10 +173,10 @@ so we can impute the variables `LotFrontage` and `MasVnrArea`:
 .. note::
 
     It's worth noting that we have the flexibility to omit the `variables` parameter,
-    in which case, :class:`MeanMedianImputer()` will automatically find and impute all
+    in which case, :class:`MeanImputer()` will automatically find and impute all
     numeric features.
 
-After fitting :class:`MeanMedianImputer()`, we can check out the statistics 
+After fitting :class:`MeanImputer()`, we can check out the statistics 
 (either mean or median; mean in this case) for each of the variables to impute:
 
 .. code:: python
@@ -217,7 +217,7 @@ Imputing missing values alongside missing indicators
 Mean or median imputation are commonly done alongside adding missing indicators.
 We can add missing indicators with :class:`AddMissingIndicator()` from feature-engine.
 
-We can chain :class:`AddMissingIndicator()` with :class:`MeanMedianImputer()` using a 
+We can chain :class:`AddMissingIndicator()` with :class:`MeanImputer()` using a 
 `scikit-learn pipeline <https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html>`_.
 
 For example, let's create an imputation pipeline to add missing indicators and then
@@ -228,7 +228,7 @@ impute the missing values:
 	# Create imputation pipeline
 	imputer = make_pipeline(
 		AddMissingIndicator(),
-		MeanMedianImputer()
+		MeanImputer()
 	)
 
 	# Fit the pipeline

docs/user_guide/imputation/index.rst

@@ -101,7 +101,7 @@ Feature-engine's imputers' main characteristics
 ================================== ===================== ======================= ====================================================================================
     Transformer                     Numerical variables	  Categorical variables	    Description
 ================================== ===================== ======================= ====================================================================================
-:class:`MeanMedianImputer()`	        √	                 ×	                    Replaces missing values with the mean or median
+:class:`MeanImputer()`	        √	                 ×	                    Replaces missing values with the mean or median
 :class:`ArbitraryNumberImputer()`	    √	                 x	                    Replaces missing values with an arbitrary value
 :class:`EndTailImputer()`	            √	                 ×	                    Replaces missing values with a value at the end of the distribution
 :class:`CategoricalImputer()`           ×	                 √	                    Replaces missing values with the most frequent category or an arbitrary string
@@ -132,7 +132,7 @@ assume that the missing values are close in value to the majority, that is, to t
 
 Data imputed with the mean or median is commonly used to train linear regression and logistic regression models.
 
-The :class:`MeanMedianImputer()` implements mean-median imputation.
+The :class:`MeanImputer()` implements mean-median imputation.
 
 Arbitrary Number Imputation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -340,7 +340,7 @@ Imputers
 .. toctree::
    :maxdepth: 1
 
-   MeanMedianImputer
+   MeanImputer
    ArbitraryNumberImputer
    EndTailImputer
    CategoricalImputer

docs/user_guide/outliers/Winsoriser.rst

@@ -207,7 +207,7 @@ using 1.5 of the IQR to find those limits (param `fold`).
 
 .. code:: python
 
-    from feature_engine.imputation import MeanMedianImputer
+    from feature_engine.imputation import MeanImputer
     from feature_engine.pipeline import Pipeline
     from feature_engine.outliers import Winsoriser
 
@@ -218,7 +218,7 @@ using 1.5 of the IQR to find those limits (param `fold`).
     )
 
     pipe = Pipeline([
-        ("imputer", MeanMedianImputer()),
+        ("imputer", MeanImputer()),
         ("outlier", w),
     ])
 
@@ -286,7 +286,7 @@ As an alternative, let's cap the variables at their 10th percentile on the right
     )
 
     pipe = Pipeline([
-        ("imputer", MeanMedianImputer()),
+        ("imputer", MeanImputer()),
         ("outlier", w),
     ])
 

docs/user_guide/timeseries/forecasting/ExpandingWindowFeatures.rst

@@ -289,20 +289,20 @@ Imputing rows with nan
 
 If instead of removing the row with nan in the expanding window features, we want to impute those
 values, we can do so with any of feature-engine's imputers. Here, we will replace nan with
-the median value of the resulting window features, using the `MeanMedianImputer` within
+the median value of the resulting window features, using the `MeanImputer` within
 a pipeline:
 
 
 .. code:: python
 
-    from feature_engine.imputation import MeanMedianImputer
+    from feature_engine.imputation import MeanImputer
     from feature_engine.pipeline import Pipeline
 
     win_f = ExpandingWindowFeatures(functions=["mean", "std"])
 
     pipe = Pipeline([
         ("windows", win_f),
-        ("imputer", MeanMedianImputer(imputation_method="median"))
+        ("imputer", MeanImputer(imputation_method="median"))
     ])
 
     X_tr = pipe.fit_transform(X, y)

docs/user_guide/wrappers/Wrapper.rst

@@ -268,7 +268,7 @@ scikit-learn's PolynomialFeatures. We start with the imports:
     from sklearn.preprocessing import PolynomialFeatures
     from sklearn.pipeline import Pipeline
     from feature_engine.datasets import load_titanic
-    from feature_engine.imputation import CategoricalImputer, MeanMedianImputer
+    from feature_engine.imputation import CategoricalImputer, MeanImputer
     from feature_engine.encoding import OrdinalEncoder
     from feature_engine.wrappers import SklearnTransformerWrapper
 
@@ -292,7 +292,7 @@ Now, we assemble the pipeline. The last step wraps scikit-learn's PolynomialFeat
 
     pipeline = Pipeline(steps = [
         ('ci', CategoricalImputer(imputation_method='frequent')),
-        ('mmi', MeanMedianImputer(imputation_method='mean')),
+        ('mmi', MeanImputer(imputation_method='mean')),
         ('od', OrdinalEncoder(encoding_method='arbitrary')),
         ('pl', SklearnTransformerWrapper(
             PolynomialFeatures(interaction_only = True, include_bias=False),

feature_engine/datasets/titanic.py

@@ -2,7 +2,7 @@
 import pandas as pd
 from sklearn.pipeline import Pipeline
 
-from feature_engine.imputation import CategoricalImputer, MeanMedianImputer
+from feature_engine.imputation import CategoricalImputer, MeanImputer
 
 
 # TODO: loading the dataset from the internet is not the best, we need to store it
@@ -95,7 +95,7 @@ def load_titanic(
                     "categorical_imputer",
                     CategoricalImputer(imputation_method="missing"),
                 ),
-                ("mean_median_imputer", MeanMedianImputer(imputation_method="mean")),
+                ("mean_imputer", MeanImputer(imputation_method="mean")),
             ]
         )
 

feature_engine/imputation/__init__.py

@@ -6,11 +6,12 @@
 from .categorical import CategoricalImputer
 from .drop_missing_data import DropMissingData
 from .end_tail import EndTailImputer
-from .mean_median import MeanMedianImputer
+from .mean_median import MeanImputer, MeanMedianImputer
 from .missing_indicator import AddMissingIndicator
 from .random_sample import RandomSampleImputer
 
 __all__ = [
+    "MeanImputer",
     "MeanMedianImputer",
     "ArbitraryNumberImputer",
     "CategoricalImputer",

feature_engine/imputation/mean_median.py

@@ -1,6 +1,7 @@
 # Authors: Soledad Galli <solegalli@protonmail.com>
 # License: BSD 3 clause
 
+import warnings
 from typing import List, Optional, Union
 
 import pandas as pd
@@ -43,16 +44,16 @@
     transform=_transform_imputers_docstring,
     fit_transform=_fit_transform_docstring,
 )
-class MeanMedianImputer(BaseImputer):
+class MeanImputer(BaseImputer):
     """
-    The MeanMedianImputer() replaces missing data by the mean or median value of the
+    The MeanImputer() replaces missing data by the mean or median value of the
     variable. It works only with numerical variables.
 
     You can pass a list of variables to impute. Alternatively, the
-    MeanMedianImputer() will automatically select all variables of type numeric in the
+    MeanImputer() will automatically select all variables of type numeric in the
     training set.
 
-    More details in the :ref:`User Guide <mean_median_imputer>`.
+    More details in the :ref:`User Guide <mean_imputer>`.
 
     Parameters
     ----------
@@ -87,12 +88,12 @@ class MeanMedianImputer(BaseImputer):
 
     >>> import pandas as pd
     >>> import numpy as np
-    >>> from feature_engine.imputation import MeanMedianImputer
+    >>> from feature_engine.imputation import MeanImputer
     >>> X = pd.DataFrame(dict(
     >>>        x1 = [np.nan,1,1,0,np.nan],
     >>>        x2 = ["a", np.nan, "b", np.nan, "a"],
     >>>        ))
-    >>> mmi = MeanMedianImputer(imputation_method='median')
+    >>> mmi = MeanImputer(imputation_method='median')
     >>> mmi.fit(X)
     >>> mmi.transform(X)
         x1   x2
@@ -151,3 +152,25 @@ def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
         self._get_feature_names_in(X)
 
         return self
+
+
+# TODO: remove in version 2.1.0
+class MeanMedianImputer(MeanImputer):
+    def __init__(
+        self,
+        imputation_method: str = "median",
+        variables: Union[None, int, str, List[Union[str, int]]] = None,
+        return_empty: bool = False,
+    ) -> None:
+        warnings.warn(
+            "MeanMedianImputer was deprecated in favour of MeanImputer in version "
+            "2.0.0 and will be removed in version 2.1.0. To silence this warning, "
+            "use MeanImputer instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        super().__init__(
+            imputation_method=imputation_method,
+            variables=variables,
+            return_empty=return_empty,
+        )

tests/check_estimators_with_parametrize_tests.py

@@ -30,7 +30,7 @@
     CategoricalImputer,
     DropMissingData,
     EndTailImputer,
-    MeanMedianImputer,
+    MeanImputer,
     RandomSampleImputer,
 )
 from feature_engine.outliers import ArbitraryOutlierCapper, OutlierTrimmer, Winsoriser
@@ -87,7 +87,7 @@ def test_sklearn_compatible_creator(estimator, check):
 # imputation
 @parametrize_with_checks(
     [
-        MeanMedianImputer(),
+        MeanImputer(),
         ArbitraryNumberImputer(),
         CategoricalImputer(fill_value=0, ignore_format=True),
         EndTailImputer(),