DictVectorizer#

class sklearn.feature_extraction.DictVectorizer(*, dtype=<class 'numpy.float64'>, separator='=', sparse=True, sort=True)[source]#

Transforms lists of feature-value mappings to vectors.

This transformer turns lists of mappings (dict-like objects) of feature names to feature values into Numpy arrays or scipy.sparse matrices for use with scikit-learn estimators.

When feature values are strings, this transformer will do a binary one-hot (aka one-of-K) coding: one boolean-valued feature is constructed for each of the possible string values that the feature can take on. For instance, a feature “f” that can take on the values “ham” and “spam” will become two features in the output, one signifying “f=ham”, the other “f=spam”.

If a feature value is a sequence or set of strings, this transformer will iterate over the values and will count the occurrences of each string value.

However, note that this transformer will only do a binary one-hot encoding when feature values are of type string. If categorical features are represented as numeric values such as int or iterables of strings, the DictVectorizer can be followed by OneHotEncoder to complete binary one-hot encoding.

Features that do not occur in a sample (mapping) will have a zero value in the resulting array/matrix.

For an efficiency comparison of the different feature extractors, see FeatureHasher and DictVectorizer Comparison.

See also

FeatureHasher: Performs vectorization using only a hash function.
sklearn.preprocessing.OrdinalEncoder: Handles nominal/categorical features encoded as columns of arbitrary data types.

Examples

>>> from sklearn.feature_extraction import DictVectorizer
>>> v = DictVectorizer(sparse=False)
>>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
>>> X = v.fit_transform(D)
>>> X
array([[2., 0., 1.],
       [0., 1., 3.]])
>>> v.inverse_transform(X) == [{'bar': 2.0, 'foo': 1.0},
...                            {'baz': 1.0, 'foo': 3.0}]
True
>>> v.transform({'foo': 4, 'unseen_feature': 3})
array([[0., 0., 4.]])

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

Learn a list of feature name -> indices mappings.

Parameters:

XMapping or iterable over Mappings: Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype).

Changed in version 0.24: Accepts multiple string values for one categorical feature.
y(ignored): Ignored parameter.

Returns:

selfobject: DictVectorizer class instance.

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

Learn a list of feature name -> indices mappings and transform X.

Like fit(X) followed by transform(X), but does not require materializing X in memory.

Parameters:

XMapping or iterable over Mappings: Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype).

Changed in version 0.24: Accepts multiple string values for one categorical feature.
y(ignored): Ignored parameter.

Returns:

Xa{array, sparse matrix}: Feature vectors; always 2-d.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None: Not used, present here for API consistency by convention.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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, dict_type=<class 'dict'>)[source]#

Transform array or sparse matrix X back to feature mappings.

X must have been produced by this DictVectorizer’s transform or fit_transform method; it may only have passed through transformers that preserve the number of features and their order.

In the case of one-hot/one-of-K coding, the constructed feature names and values are returned rather than the original ones.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Sample matrix.
dict_typetype, default=dict: Constructor for feature mappings. Must conform to the collections.Mapping API.

Returns:

Dlist of dict_type objects of shape (n_samples,): Feature mappings for the samples in X.

restrict(support, indices=False)[source]#

Restrict the features to those in support using feature selection.

This function modifies the estimator in-place.

Parameters:

supportarray-like: Boolean mask or list of indices (as returned by the get_support member of feature selectors).
indicesbool, default=False: Whether support is a list of indices.

Returns:

selfobject: DictVectorizer class instance.

Examples

>>> from sklearn.feature_extraction import DictVectorizer
>>> from sklearn.feature_selection import SelectKBest, chi2
>>> v = DictVectorizer()
>>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
>>> X = v.fit_transform(D)
>>> support = SelectKBest(chi2, k=2).fit(X, [0, 1])
>>> v.get_feature_names_out()
array(['bar', 'baz', 'foo'], ...)
>>> v.restrict(support.get_support())
DictVectorizer()
>>> v.get_feature_names_out()
array(['bar', 'foo'], ...)

set_inverse_transform_request(*, dict_type: bool | None | str = '$UNCHANGED$') → DictVectorizer[source]#

Request metadata passed to the inverse_transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to inverse_transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to inverse_transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

dict_typestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for dict_type parameter in inverse_transform.

Returns:

selfobject: The updated object.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

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.

transform(X)[source]#

Transform feature->value dicts to array or sparse matrix.

Named features not encountered during fit or fit_transform will be silently ignored.

Parameters:

XMapping or iterable over Mappings of shape (n_samples,): Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype).

Returns:

Xa{array, sparse matrix}: Feature vectors; always 2-d.

Gallery examples#

Column Transformer with Heterogeneous Data Sources

FeatureHasher and DictVectorizer Comparison