sklearn.feature_extraction.DictVectorizer

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

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.

Read more in the User Guide.

Parameters:

dtypedtype, default=np.float64

The type of feature values. Passed to Numpy array/scipy.sparse matrix constructors as the dtype argument.

separatorstr, default=”=”

Separator string used when constructing new features for one-hot coding.

sparsebool, default=True

Whether transform should produce scipy.sparse matrices.

sortbool, default=True

Whether feature_names_ and vocabulary_ should be sorted when fitting.

Attributes:

vocabulary_dict

A dictionary mapping feature names to feature indices.

feature_names_list

A list of length n_features containing the feature names (e.g., “f=ham” and “f=spam”).

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.]])

Methods

fit(X[, y])	Learn a list of feature name -> indices mappings.
fit_transform(X[, y])	Learn a list of feature name -> indices mappings and transform X.
get_feature_names_out([input_features])	Get output feature names for transformation.
get_params([deep])	Get parameters for this estimator.
inverse_transform(X[, dict_type])	Transform array or sparse matrix X back to feature mappings.
restrict(support[, indices])	Restrict the features to those in support using feature selection.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform feature->value dicts to array or sparse matrix.

fit(X, y=None)

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)

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)

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_params(deep=True)

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'>)

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)

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_output(*, transform=None)

Set output container.

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

Parameters:

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

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• None: Transform configuration is unchanged

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

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)

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.

Examples using sklearn.feature_extraction.DictVectorizer

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison