Documentation
Classes
ColumnTransformer

ColumnTransformer

Applies transformers to columns of an array or pandas DataFrame.

This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be concatenated to form a single feature space. This is useful for heterogeneous or columnar data, to combine several feature extraction mechanisms or transformations into a single transformer.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new ColumnTransformer(opts?: object): ColumnTransformer;

Parameters

NameTypeDescription
opts?object-
opts.n_jobs?numberNumber of jobs to run in parallel. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.remainder?"drop" | "passthrough"By default, only the specified columns in transformers are transformed and combined in the output, and the non-specified columns are dropped. (default of 'drop'). By specifying remainder='passthrough', all remaining columns that were not specified in transformers, but present in the data passed to fit will be automatically passed through. This subset of columns is concatenated with the output of the transformers. For dataframes, extra columns not seen during fit will be excluded from the output of transform. By setting remainder to be an estimator, the remaining non-specified columns will use the remainder estimator. The estimator must support fit and transform. Note that using this feature requires that the DataFrame columns input at fit and transform have identical order. Default Value 'drop'
opts.sparse_threshold?numberIf the output of the different transformers contains sparse matrices, these will be stacked as a sparse matrix if the overall density is lower than this value. Use sparse\_threshold=0 to always return dense. When the transformed output consists of all dense data, the stacked result will be dense, and this keyword will be ignored. Default Value 0.3
opts.transformer_weights?anyMultiplicative weights for features per transformer. The output of the transformer is multiplied by these weights. Keys are transformer names, values the weights.
opts.transformers?anyList of (name, transformer, columns) tuples specifying the transformer objects to be applied to subsets of the data.
opts.verbose?booleanIf true, the time elapsed while fitting each transformer will be printed as it is completed. Default Value false
opts.verbose_feature_names_out?booleanIf true, get\_feature\_names\_out will prefix all feature names with the name of the transformer that generated that feature. If false, get\_feature\_names\_out will not prefix any feature names and will error if feature names are not unique. Default Value true

Returns

ColumnTransformer

Defined in: generated/compose/ColumnTransformer.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/compose/ColumnTransformer.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/compose/ColumnTransformer.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/compose/ColumnTransformer.ts:21 (opens in a new tab)

id

string

Defined in: generated/compose/ColumnTransformer.ts:18 (opens in a new tab)

opts

any

Defined in: generated/compose/ColumnTransformer.ts:19 (opens in a new tab)

Accessors

n_features_in_

Number of features seen during fit. Only defined if the underlying transformers expose such an attribute when fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/compose/ColumnTransformer.ts:432 (opens in a new tab)

output_indices_

A dictionary from each transformer name to a slice, where the slice corresponds to indices in the transformed output. This is useful to inspect which transformer is responsible for which transformed feature(s).

Signature

output_indices_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/compose/ColumnTransformer.ts:405 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/compose/ColumnTransformer.ts:73 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/compose/ColumnTransformer.ts:77 (opens in a new tab)

sparse_output_

Boolean flag indicating whether the output of transform is a sparse matrix or a dense numpy array, which depends on the output of the individual transformers and the sparse\_threshold keyword.

Signature

sparse_output_(): Promise<boolean>;

Returns

Promise<boolean>

Defined in: generated/compose/ColumnTransformer.ts:378 (opens in a new tab)

transformers_

The collection of fitted transformers as tuples of (name, fitted_transformer, column). fitted\_transformer can be an estimator, ‘drop’, or ‘passthrough’. In case there were no columns selected, this will be the unfitted transformer. If there are remaining columns, the final element is a tuple of the form: (‘remainder’, transformer, remaining_columns) corresponding to the remainder parameter. If there are remaining columns, then len(transformers\_)==len(transformers)+1, otherwise len(transformers\_)==len(transformers).

Signature

transformers_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/compose/ColumnTransformer.ts:351 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/compose/ColumnTransformer.ts:138 (opens in a new tab)

fit()

Fit all transformers using X.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input data, of which specified subsets are used to fit the transformers.
opts.y?ArrayLike[]Targets for supervised learning.

Returns

Promise<any>

Defined in: generated/compose/ColumnTransformer.ts:155 (opens in a new tab)

fit_transform()

Fit all transformers, transform the data and concatenate results.

Signature

fit_transform(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input data, of which specified subsets are used to fit the transformers.
opts.y?ArrayLikeTargets for supervised learning.

Returns

Promise<ArrayLike>

Defined in: generated/compose/ColumnTransformer.ts:197 (opens in a new tab)

get_feature_names_out()

Get output feature names for transformation.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.input_features?anyInput features.

Returns

Promise<any>

Defined in: generated/compose/ColumnTransformer.ts:241 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/compose/ColumnTransformer.ts:86 (opens in a new tab)

set_output()

Set the output container when "transform" and "fit\_transform" are called.

Calling set\_output will set the output of all estimators in transformers and transformers\_.

Signature

set_output(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/compose/ColumnTransformer.ts:281 (opens in a new tab)

transform()

Transform X separately by each transformer, concatenate results.

Signature

transform(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The data to be transformed by subset.

Returns

Promise<ArrayLike>

Defined in: generated/compose/ColumnTransformer.ts:316 (opens in a new tab)