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# `sklearn.compose`.ColumnTransformer

class sklearn.compose.ColumnTransformer(transformers, *, remainder='drop', sparse_threshold=0.3, n_jobs=None, transformer_weights=None, verbose=False, verbose_feature_names_out=True)

  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.

  New in version 0.20.

  Parameters:
    transformers list of tuples

      List of (name, transformer, columns) tuples specifying the transformer objects to be applied to subsets of the data.

      namestr

        Like in Pipeline and FeatureUnion, this allows the transformer and its parameters to be set using `set_params` and searched in grid search.

      transformer{‘drop’, ‘passthrough’} or estimator

        Estimator must support fit and transform. Special-cased strings ‘drop’ and ‘passthrough’ are accepted as well, to indicate to drop the columns or to pass them through untransformed, respectively.

      columnsstr, array-like of str, int, array-like of int, array-like of bool, slice or callable

        Indexes the data on its second axis. Integers are interpreted as positional columns, while strings can reference DataFrame columns by name. A scalar string or int should be used where `transformer` expects X to be a 1d array-like (vector), otherwise a 2d array will be passed to the transformer. A callable is passed the input data `X` and can return any of the above. To select multiple columns by name or dtype, you can use `make_column_selector`.

    remainder{‘drop’, ‘passthrough’} or estimator, default=’drop’

      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.

    sparse_threshold float, default=0.3

      If 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.

    n_jobs int, default=None

      Number of jobs to run in parallel. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

    transformer_weights dict, default=None

      Multiplicative weights for features per transformer. The output of the transformer is multiplied by these weights. Keys are transformer names, values the weights.

    verbose bool, default=False

      If True, the time elapsed while fitting each transformer will be printed as it is completed.

    verbose_feature_names_out bool, default=True

      If True, `ColumnTransformer.get_feature_names_out` will prefix all feature names with the name of the transformer that generated that feature. If False, `ColumnTransformer.get_feature_names_out` will not prefix any feature names and will error if feature names are not unique.

      New in version 1.0.

  Attributes:
    transformers_list

      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)`.

    `named_transformers_``Bunch`

      Access the fitted transformer by name.

    sparse_output_bool

      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.

    output_indices_dict

      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).

      New in version 1.0.

    n_features_in_int

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

  `make_column_transformer`

    Convenience function for combining the outputs of multiple transformer objects applied to column subsets of the original feature space.

  `make_column_selector`

    Convenience function for selecting columns based on datatype or the columns name with a regex pattern.

  Notes

  The order of the columns in the transformed feature matrix follows the order of how the columns are specified in the `transformers` list. Columns of the original feature matrix that are not specified are dropped from the resulting transformed feature matrix, unless specified in the `passthrough` keyword. Those columns specified with `passthrough` are added at the right to the output of the transformers.

  Examples

      >>> import numpy as np
      >>> from sklearn.compose import ColumnTransformer
      >>> from sklearn.preprocessing import Normalizer
      >>> ct = ColumnTransformer(
      ...     [("norm1", Normalizer(norm='l1'), [0, 1]),
      ...      ("norm2", Normalizer(norm='l1'), slice(2, 4))])
      >>> X = np.array([[0., 1., 2., 2.],
      ...               [1., 1., 0., 1.]])
      >>> # Normalizer scales each row of X to unit norm. A separate scaling
      >>> # is applied for the two first and two last elements of each
      >>> # row independently.
      >>> ct.fit_transform(X)
      array([[0. , 1. , 0.5, 0.5],
             [0.5, 0.5, 0. , 1. ]])

  `ColumnTransformer` can be configured with a transformer that requires a 1d array by setting the column to a string:

      >>> from sklearn.feature_extraction import FeatureHasher
      >>> from sklearn.preprocessing import MinMaxScaler
      >>> import pandas as pd
      >>> X = pd.DataFrame({
      ...     "documents": ["First item", "second one here", "Is this the last?"],
      ...     "width": [3, 4, 5],
      ... })
      >>> # "documents" is a string which configures ColumnTransformer to
      >>> # pass the documents column as a 1d array to the FeatureHasher
      >>> ct = ColumnTransformer(
      ...     [("text_preprocess", FeatureHasher(input_type="string"), "documents"),
      ...      ("num_preprocess", MinMaxScaler(), ["width"])])
      >>> X_trans = ct.fit_transform(X)

  For a more detailed example of usage, see Column Transformer with Mixed Types.

  Methods

  `fit`(X[, y]) | Fit all transformers using X.
  `fit_transform`(X[, y]) | Fit all transformers, transform the data and concatenate results.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `set_output`(*[, transform]) | Set the output container when `"transform"` and `"fit_transform"` are called.
  `set_params`(**kwargs) | Set the parameters of this estimator.
  `transform`(X) | Transform X separately by each transformer, concatenate results.

  fit(X, y=None)

    Fit all transformers using X.

    Parameters:
      X{array-like, dataframe} of shape (n_samples, n_features)

        Input data, of which specified subsets are used to fit the transformers.

      y array-like of shape (n_samples,…), default=None

        Targets for supervised learning.

    Returns:
      self ColumnTransformer

        This estimator.

  fit_transform(X, y=None)

    Fit all transformers, transform the data and concatenate results.

    Parameters:
      X{array-like, dataframe} of shape (n_samples, n_features)

        Input data, of which specified subsets are used to fit the transformers.

      y array-like of shape (n_samples,), default=None

        Targets for supervised learning.

    Returns:
      X_t{array-like, sparse matrix} of shape (n_samples, sum_n_components)

        Horizontally stacked results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. If any result is a sparse matrix, everything will be converted to sparse matrices.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Parameters:
      input_features array-like of str or None, default=None

        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_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, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Returns the parameters given in the constructor as well as the estimators contained within the `transformers` of the `ColumnTransformer`.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  property named_transformers_

    Access the fitted transformer by name.

    Read-only attribute to access any transformer by given name. Keys are transformer names and values are the fitted transformer objects.

  set_output(*, transform=None)

    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_`.

    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:
      self estimator instance

        Estimator instance.

  set_params(**kwargs)

    Set the parameters of this estimator.

    Valid parameter keys can be listed with `get_params()`. Note that you can directly set the parameters of the estimators contained in `transformers` of `ColumnTransformer`.

    Parameters:
      **kwargs dict

        Estimator parameters.

    Returns:
      self ColumnTransformer

        This estimator.

  transform(X)

    Transform X separately by each transformer, concatenate results.

    Parameters:
      X{array-like, dataframe} of shape (n_samples, n_features)

        The data to be transformed by subset.

    Returns:
      X_t{array-like, sparse matrix} of shape (n_samples, sum_n_components)

        Horizontally stacked results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. If any result is a sparse matrix, everything will be converted to sparse matrices.

## Examples using `sklearn.compose.ColumnTransformer`

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.0

Time-related feature engineering

Time-related feature engineering

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Tweedie regression on insurance claims

Partial Dependence and Individual Conditional Expectation Plots

Partial Dependence and Individual Conditional Expectation Plots

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)

Displaying Pipelines

Displaying Pipelines

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

Introducing the set_output API

Introducing the set_output API

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

Column Transformer with Mixed Types

Column Transformer with Mixed Types

Comparing Target Encoder with Other Encoders

Comparing Target Encoder with Other Encoders