sklearn.impute
.SimpleImputer¶
- class sklearn.impute.SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None, verbose='deprecated', copy=True, add_indicator=False)[source]¶
Univariate imputer for completing missing values with simple strategies.
Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a constant value.
Read more in the User Guide.
New in version 0.20:
SimpleImputer
replaces the previoussklearn.preprocessing.Imputer
estimator which is now removed.- Parameters:
- missing_valuesint, float, str, np.nan, None or pandas.NA, default=np.nan
The placeholder for the missing values. All occurrences of
missing_values
will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values,missing_values
can be set to eithernp.nan
orpd.NA
.- strategystr, default=’mean’
The imputation strategy.
If “mean”, then replace missing values using the mean along each column. Can only be used with numeric data.
If “median”, then replace missing values using the median along each column. Can only be used with numeric data.
If “most_frequent”, then replace missing using the most frequent value along each column. Can be used with strings or numeric data. If there is more than one such value, only the smallest is returned.
If “constant”, then replace missing values with fill_value. Can be used with strings or numeric data.
New in version 0.20: strategy=”constant” for fixed value imputation.
- fill_valuestr or numerical value, default=None
When strategy == “constant”, fill_value is used to replace all occurrences of missing_values. If left to the default, fill_value will be 0 when imputing numerical data and “missing_value” for strings or object data types.
- verboseint, default=0
Controls the verbosity of the imputer.
Deprecated since version 1.1: The ‘verbose’ parameter was deprecated in version 1.1 and will be removed in 1.3. A warning will always be raised upon the removal of empty columns in the future version.
- copybool, default=True
If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if
copy=False
:If
X
is not an array of floating values;If
X
is encoded as a CSR matrix;If
add_indicator=True
.
- add_indicatorbool, default=False
If True, a
MissingIndicator
transform will stack onto output of the imputer’s transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won’t appear on the missing indicator even if there are missing values at transform/test time.
- Attributes:
- statistics_array of shape (n_features,)
The imputation fill value for each feature. Computing statistics can result in
np.nan
values. Duringtransform
, features corresponding tonp.nan
statistics will be discarded.- indicator_
MissingIndicator
Indicator used to add binary indicators for missing values.
None
ifadd_indicator=False
.- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
IterativeImputer
Multivariate imputer that estimates values to impute for each feature with missing values from all the others.
KNNImputer
Multivariate imputer that estimates missing features using nearest samples.
Notes
Columns which only contained missing values at
fit
are discarded upontransform
if strategy is not"constant"
.In a prediction context, simple imputation usually performs poorly when associated with a weak learner. However, with a powerful learner, it can lead to as good or better performance than complex imputation such as
IterativeImputer
orKNNImputer
.Examples
>>> import numpy as np >>> from sklearn.impute import SimpleImputer >>> imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean') >>> imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]]) SimpleImputer() >>> X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]] >>> print(imp_mean.transform(X)) [[ 7. 2. 3. ] [ 4. 3.5 6. ] [10. 3.5 9. ]]
Methods
fit
(X[, y])Fit the imputer on
X
.fit_transform
(X[, y])Fit to data, then transform it.
get_feature_names_out
([input_features])Get output feature names for transformation.
get_params
([deep])Get parameters for this estimator.
Convert the data back to the original representation.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Impute all missing values in
X
.- fit(X, y=None)[source]¶
Fit the imputer on
X
.- Parameters:
- X{array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where
n_samples
is the number of samples andn_features
is the number of features.- yIgnored
Not used, present here for API consistency by convention.
- Returns:
- selfobject
Fitted estimator.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters:
- 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).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters:
- input_featuresarray-like of str or None, default=None
Input features.
If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_names_in_
is not defined, then the following input feature names are generated:["x0", "x1", ..., "x(n_features_in_ - 1)"]
.If
input_features
is an array-like, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
- Returns:
- feature_names_outndarray of str objects
Transformed feature names.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- inverse_transform(X)[source]¶
Convert the data back to the original representation.
Inverts the
transform
operation performed on an array. This operation can only be performed afterSimpleImputer
is instantiated withadd_indicator=True
.Note that
inverse_transform
can only invert the transform in features that have binary indicators for missing values. If a feature has no missing values atfit
time, the feature won’t have a binary indicator, and the imputation done attransform
time won’t be inverted.New in version 0.24.
- Parameters:
- Xarray-like of shape (n_samples, n_features + n_features_missing_indicator)
The imputed data to be reverted to original data. It has to be an augmented array of imputed data and the missing indicator mask.
- Returns:
- X_originalndarray of shape (n_samples, n_features)
The original
X
with missing values as it was prior to imputation.
- set_params(**params)[source]¶
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.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
Examples using sklearn.impute.SimpleImputer
¶
Release Highlights for scikit-learn 1.1
Release Highlights for scikit-learn 0.23
Combine predictors using stacking
Permutation Importance vs Random Forest Feature Importance (MDI)
Imputing missing values before building an estimator
Imputing missing values with variants of IterativeImputer
Column Transformer with Mixed Types