Applies transformers to columns of an array or pandas DataFrame.
EXPERIMENTAL: some behaviors may change between releases without deprecation.
This estimator allows different columns or column subsets of the input to be transformed separately and the results combined into 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.
Parameters: 

transformers : list of tuples 
List of (name, transformer, column(s)) tuples specifying the transformer objects to be applied to subsets of the data. 
name : string 
Like in Pipeline and FeatureUnion, this allows the transformer and its parameters to be set using set_params and searched in grid search. 
transformer : estimator or {‘passthrough’, ‘drop’} 
Estimator must support fit and transform . Specialcased strings ‘drop’ and ‘passthrough’ are accepted as well, to indicate to drop the columns or to pass them through untransformed, respectively. 
column(s) : string or int, arraylike of string or int, slice, boolean mask array 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 arraylike (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. 
remainder : {‘drop’, ‘passthrough’} or estimator, default ‘drop’ 
By default, only the specified columns in transformers are transformed and combined in the output, and the nonspecified columns are dropped. (default of 'drop' ). By specifying remainder='passthrough' , all remaining columns that were not specified in transformers will be automatically passed through. This subset of columns is concatenated with the output of the transformers. By setting remainder to be an estimator, the remaining nonspecified columns will use the remainder estimator. The estimator must support fit and transform . 
sparse_threshold : float, default = 0.3 
If the transformed output consists of a mix of sparse and dense data, it will be stacked as a sparse matrix if the density is lower than this value. Use sparse_threshold=0 to always return dense. When the transformed output consists of all sparse or all dense data, the stacked result will be sparse or dense, respectively, and this keyword will be ignored. 
n_jobs : int or None, optional (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, optional 
Multiplicative weights for features per transformer. The output of the transformer is multiplied by these weights. Keys are transformer names, values the weights. 
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 object, a dictionary with attribute access 
Access the fitted transformer by name. 
sparse_output_ : boolean 
Boolean flag indicating wether 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. 
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
>>> 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. ]])
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 ()  Get feature names from all transformers. 
get_params ([deep])  Get parameters for this estimator. 
set_params (**kwargs)  Set the parameters of this estimator. 
transform (X)  Transform X separately by each transformer, concatenate results. 

__init__(transformers, remainder=’drop’, sparse_threshold=0.3, n_jobs=None, transformer_weights=None)
[source]

fit(X, y=None)
[source]

Fit all transformers using X.
Parameters: 

X : arraylike or DataFrame of shape [n_samples, n_features] 
Input data, of which specified subsets are used to fit the transformers. 
y : arraylike, shape (n_samples, …), optional 
Targets for supervised learning. 
Returns: 

self : ColumnTransformer 
This estimator 

fit_transform(X, y=None)
[source]

Fit all transformers, transform the data and concatenate results.
Parameters: 

X : arraylike or DataFrame of shape [n_samples, n_features] 
Input data, of which specified subsets are used to fit the transformers. 
y : arraylike, shape (n_samples, …), optional 
Targets for supervised learning. 
Returns: 

X_t : arraylike or sparse matrix, shape (n_samples, sum_n_components) 
hstack of 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()
[source]

Get feature names from all transformers.
Returns: 

feature_names : list of strings 
Names of the features produced by transform. 

get_params(deep=True)
[source]

Get parameters for this estimator.
Parameters: 

deep : boolean, optional 
If True, will return the parameters for this estimator and contained subobjects that are estimators. 
Returns: 

params : mapping of string to any 
Parameter names mapped to their values. 

named_transformers_

Access the fitted transformer by name.
Readonly attribute to access any transformer by given name. Keys are transformer names and values are the fitted transformer objects.

set_params(**kwargs)
[source]

Set the parameters of this estimator.
Valid parameter keys can be listed with get_params()
.

transform(X)
[source]

Transform X separately by each transformer, concatenate results.
Parameters: 

X : arraylike or DataFrame of shape [n_samples, n_features] 
The data to be transformed by subset. 
Returns: 

X_t : arraylike or sparse matrix, shape (n_samples, sum_n_components) 
hstack of 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. 