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/ compose / _column_transformer.py
"""
The :mod:`sklearn.compose._column_transformer` module implements utilities
to work with heterogeneous data and to apply different transformers to
different columns.
"""
# Author: Andreas Mueller
# Joris Van den Bossche
# License: BSD
import warnings
from itertools import chain
import numbers
import numpy as np
from scipy import sparse
from joblib import Parallel, delayed
from ..base import clone, TransformerMixin
from ..pipeline import _fit_transform_one, _transform_one, _name_estimators
from ..preprocessing import FunctionTransformer
from ..utils import Bunch
from ..utils import _safe_indexing
from ..utils import _get_column_indices
from ..utils import _determine_key_type
from ..utils.metaestimators import _BaseComposition
from ..utils.validation import check_array, check_is_fitted
__all__ = [
'ColumnTransformer', 'make_column_transformer', 'make_column_selector'
]
_ERR_MSG_1DCOLUMN = ("1D data passed to a transformer that expects 2D data. "
"Try to specify the column selection as a list of one "
"item instead of a scalar.")
class ColumnTransformer(TransformerMixin, _BaseComposition):
"""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 :ref:`User Guide <column_transformer>`.
.. versionadded:: 0.20
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 :term:`fit` and :term:`transform`.
Special-cased 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, array-like 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 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
:obj:`make_column_transformer`.
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` 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
non-specified columns will use the ``remainder`` estimator. The
estimator must support :term:`fit` and :term:`transform`.
Note that using this feature requires that the DataFrame columns
input at :term:`fit` and :term:`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 or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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.
verbose : boolean, optional(default=False)
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
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
Read-only attribute to access any transformer by given name.
Keys are transformer names and values are the fitted transformer
objects.
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.
See also
--------
sklearn.compose.make_column_transformer : convenience function for
combining the outputs of multiple transformer objects applied to
column subsets of the original feature space.
sklearn.compose.make_column_selector : convenience function for selecting
columns based on datatype or the columns name with a regex pattern.
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. ]])
"""
_required_parameters = ['transformers']
def __init__(self,
transformers,
remainder='drop',
sparse_threshold=0.3,
n_jobs=None,
transformer_weights=None,
verbose=False):
self.transformers = transformers
self.remainder = remainder
self.sparse_threshold = sparse_threshold
self.n_jobs = n_jobs
self.transformer_weights = transformer_weights
self.verbose = verbose
@property
def _transformers(self):
"""
Internal list of transformer only containing the name and
transformers, dropping the columns. This is for the implementation
of get_params via BaseComposition._get_params which expects lists
of tuples of len 2.
"""
return [(name, trans) for name, trans, _ in self.transformers]
@_transformers.setter
def _transformers(self, value):
self.transformers = [
(name, trans, col) for ((name, trans), (_, _, col))
in zip(value, self.transformers)]
def get_params(self, deep=True):
"""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.
"""
return self._get_params('_transformers', deep=deep)
def set_params(self, **kwargs):
"""Set the parameters of this estimator.
Valid parameter keys can be listed with ``get_params()``.
Returns
-------
self
"""
self._set_params('_transformers', **kwargs)
return self
def _iter(self, fitted=False, replace_strings=False):
"""
Generate (name, trans, column, weight) tuples.
If fitted=True, use the fitted transformers, else use the
user specified transformers updated with converted column names
and potentially appended with transformer for remainder.
"""
if fitted:
transformers = self.transformers_
else:
# interleave the validated column specifiers
transformers = [
(name, trans, column) for (name, trans, _), column
in zip(self.transformers, self._columns)
]
# add transformer tuple for remainder
if self._remainder[2] is not None:
transformers = chain(transformers, [self._remainder])
get_weight = (self.transformer_weights or {}).get
for name, trans, column in transformers:
if replace_strings:
# replace 'passthrough' with identity transformer and
# skip in case of 'drop'
if trans == 'passthrough':
trans = FunctionTransformer(
accept_sparse=True, check_inverse=False
)
elif trans == 'drop':
continue
elif _is_empty_column_selection(column):
continue
yield (name, trans, column, get_weight(name))
def _validate_transformers(self):
if not self.transformers:
return
names, transformers, _ = zip(*self.transformers)
# validate names
self._validate_names(names)
# validate estimators
for t in transformers:
if t in ('drop', 'passthrough'):
continue
if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not
hasattr(t, "transform")):
raise TypeError("All estimators should implement fit and "
"transform, or can be 'drop' or 'passthrough' "
"specifiers. '%s' (type %s) doesn't." %
(t, type(t)))
def _validate_column_callables(self, X):
"""
Converts callable column specifications.
"""
columns = []
for _, _, column in self.transformers:
if callable(column):
column = column(X)
columns.append(column)
self._columns = columns
def _validate_remainder(self, X):
"""
Validates ``remainder`` and defines ``_remainder`` targeting
the remaining columns.
"""
is_transformer = ((hasattr(self.remainder, "fit")
or hasattr(self.remainder, "fit_transform"))
and hasattr(self.remainder, "transform"))
if (self.remainder not in ('drop', 'passthrough')
and not is_transformer):
raise ValueError(
"The remainder keyword needs to be one of 'drop', "
"'passthrough', or estimator. '%s' was passed instead" %
self.remainder)
# Make it possible to check for reordered named columns on transform
if (hasattr(X, 'columns') and
any(_determine_key_type(cols) == 'str'
for cols in self._columns)):
self._df_columns = X.columns
self._n_features = X.shape[1]
cols = []
for columns in self._columns:
cols.extend(_get_column_indices(X, columns))
remaining_idx = list(set(range(self._n_features)) - set(cols))
remaining_idx = sorted(remaining_idx) or None
self._remainder = ('remainder', self.remainder, remaining_idx)
@property
def named_transformers_(self):
"""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.
"""
# Use Bunch object to improve autocomplete
return Bunch(**{name: trans for name, trans, _
in self.transformers_})
def get_feature_names(self):
"""Get feature names from all transformers.
Returns
-------
feature_names : list of strings
Names of the features produced by transform.
"""
check_is_fitted(self)
feature_names = []
for name, trans, _, _ in self._iter(fitted=True):
if trans == 'drop':
continue
elif trans == 'passthrough':
raise NotImplementedError(
"get_feature_names is not yet supported when using "
"a 'passthrough' transformer.")
elif not hasattr(trans, 'get_feature_names'):
raise AttributeError("Transformer %s (type %s) does not "
"provide get_feature_names."
% (str(name), type(trans).__name__))
feature_names.extend([name + "__" + f for f in
trans.get_feature_names()])
return feature_names
def _update_fitted_transformers(self, transformers):
# transformers are fitted; excludes 'drop' cases
fitted_transformers = iter(transformers)
transformers_ = []
for name, old, column, _ in self._iter():
if old == 'drop':
trans = 'drop'
elif old == 'passthrough':
# FunctionTransformer is present in list of transformers,
# so get next transformer, but save original string
next(fitted_transformers)
trans = 'passthrough'
elif _is_empty_column_selection(column):
trans = old
else:
trans = next(fitted_transformers)
transformers_.append((name, trans, column))
# sanity check that transformers is exhausted
assert not list(fitted_transformers)
self.transformers_ = transformers_
def _validate_output(self, result):
"""
Ensure that the output of each transformer is 2D. Otherwise
hstack can raise an error or produce incorrect results.
"""
names = [name for name, _, _, _ in self._iter(fitted=True,
replace_strings=True)]
for Xs, name in zip(result, names):
if not getattr(Xs, 'ndim', 0) == 2:
raise ValueError(
"The output of the '{0}' transformer should be 2D (scipy "
"matrix, array, or pandas DataFrame).".format(name))
def _validate_features(self, n_features, feature_names):
"""Ensures feature counts and names are the same during fit and
transform.
TODO: It should raise an error from v0.24
"""
if ((self._feature_names_in is None or feature_names is None)
and self._n_features == n_features):
return
neg_col_present = np.any([_is_negative_indexing(col)
for col in self._columns])
if neg_col_present and self._n_features != n_features:
raise RuntimeError("At least one negative column was used to "
"indicate columns, and the new data's number "
"of columns does not match the data given "
"during fit. "
"Please make sure the data during fit and "
"transform have the same number of columns.")
if (self._n_features != n_features or
np.any(self._feature_names_in != np.asarray(feature_names))):
warnings.warn("Given feature/column names or counts do not match "
"the ones for the data given during fit. This will "
"fail from v0.24.",
FutureWarning)
def _log_message(self, name, idx, total):
if not self.verbose:
return None
return '(%d of %d) Processing %s' % (idx, total, name)
def _fit_transform(self, X, y, func, fitted=False):
"""
Private function to fit and/or transform on demand.
Return value (transformers and/or transformed X data) depends
on the passed function.
``fitted=True`` ensures the fitted transformers are used.
"""
transformers = list(
self._iter(fitted=fitted, replace_strings=True))
try:
return Parallel(n_jobs=self.n_jobs)(
delayed(func)(
transformer=clone(trans) if not fitted else trans,
X=_safe_indexing(X, column, axis=1),
y=y,
weight=weight,
message_clsname='ColumnTransformer',
message=self._log_message(name, idx, len(transformers)))
for idx, (name, trans, column, weight) in enumerate(
self._iter(fitted=fitted, replace_strings=True), 1))
except ValueError as e:
if "Expected 2D array, got 1D array instead" in str(e):
raise ValueError(_ERR_MSG_1DCOLUMN)
else:
raise
def fit(self, X, y=None):
"""Fit all transformers using X.
Parameters
----------
X : array-like or DataFrame of shape [n_samples, n_features]
Input data, of which specified subsets are used to fit the
transformers.
y : array-like, shape (n_samples, ...), optional
Targets for supervised learning.
Returns
-------
self : ColumnTransformer
This estimator
"""
# we use fit_transform to make sure to set sparse_output_ (for which we
# need the transformed data) to have consistent output type in predict
self.fit_transform(X, y=y)
return self
def fit_transform(self, X, y=None):
"""Fit all transformers, transform the data and concatenate results.
Parameters
----------
X : array-like or DataFrame of shape [n_samples, n_features]
Input data, of which specified subsets are used to fit the
transformers.
y : array-like, shape (n_samples, ...), optional
Targets for supervised learning.
Returns
-------
X_t : array-like 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.
"""
# TODO: this should be `feature_names_in_` when we start having it
if hasattr(X, "columns"):
self._feature_names_in = np.asarray(X.columns)
else:
self._feature_names_in = None
X = _check_X(X)
self._validate_transformers()
self._validate_column_callables(X)
self._validate_remainder(X)
result = self._fit_transform(X, y, _fit_transform_one)
if not result:
self._update_fitted_transformers([])
# All transformers are None
return np.zeros((X.shape[0], 0))
Xs, transformers = zip(*result)
# determine if concatenated output will be sparse or not
if any(sparse.issparse(X) for X in Xs):
nnz = sum(X.nnz if sparse.issparse(X) else X.size for X in Xs)
total = sum(X.shape[0] * X.shape[1] if sparse.issparse(X)
else X.size for X in Xs)
density = nnz / total
self.sparse_output_ = density < self.sparse_threshold
else:
self.sparse_output_ = False
self._update_fitted_transformers(transformers)
self._validate_output(Xs)
return self._hstack(list(Xs))
def transform(self, X):
"""Transform X separately by each transformer, concatenate results.
Parameters
----------
X : array-like or DataFrame of shape [n_samples, n_features]
The data to be transformed by subset.
Returns
-------
X_t : array-like 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.
"""
check_is_fitted(self)
X = _check_X(X)
if hasattr(X, "columns"):
X_feature_names = np.asarray(X.columns)
else:
X_feature_names = None
if self._n_features > X.shape[1]:
raise ValueError('Number of features of the input must be equal '
'to or greater than that of the fitted '
'transformer. Transformer n_features is {0} '
'and input n_features is {1}.'
.format(self._n_features, X.shape[1]))
# No column reordering allowed for named cols combined with remainder
# TODO: remove this mechanism in 0.24, once we enforce strict column
# name order and count. See #14237 for details.
if (self._remainder[2] is not None and
hasattr(self, '_df_columns') and
hasattr(X, 'columns')):
n_cols_fit = len(self._df_columns)
n_cols_transform = len(X.columns)
if (n_cols_transform >= n_cols_fit and
any(X.columns[:n_cols_fit] != self._df_columns)):
raise ValueError('Column ordering must be equal for fit '
'and for transform when using the '
'remainder keyword')
self._validate_features(X.shape[1], X_feature_names)
Xs = self._fit_transform(X, None, _transform_one, fitted=True)
self._validate_output(Xs)
if not Xs:
# All transformers are None
return np.zeros((X.shape[0], 0))
return self._hstack(list(Xs))
def _hstack(self, Xs):
"""Stacks Xs horizontally.
This allows subclasses to control the stacking behavior, while reusing
everything else from ColumnTransformer.
Parameters
----------
Xs : List of numpy arrays, sparse arrays, or DataFrames
"""
if self.sparse_output_:
try:
# since all columns should be numeric before stacking them
# in a sparse matrix, `check_array` is used for the
# dtype conversion if necessary.
converted_Xs = [check_array(X,
accept_sparse=True,
force_all_finite=False)
for X in Xs]
except ValueError:
raise ValueError("For a sparse output, all columns should"
" be a numeric or convertible to a numeric.")
return sparse.hstack(converted_Xs).tocsr()
else:
Xs = [f.toarray() if sparse.issparse(f) else f for f in Xs]
return np.hstack(Xs)
def _check_X(X):
"""Use check_array only on lists and other non-array-likes / sparse"""
if hasattr(X, '__array__') or sparse.issparse(X):
return X
return check_array(X, force_all_finite='allow-nan', dtype=np.object)
def _is_empty_column_selection(column):
"""
Return True if the column selection is empty (empty list or all-False
boolean array).
"""
if hasattr(column, 'dtype') and np.issubdtype(column.dtype, np.bool_):
return not column.any()
elif hasattr(column, '__len__'):
return len(column) == 0
else:
return False
def _get_transformer_list(estimators):
"""
Construct (name, trans, column) tuples from list
"""
transformers, columns = zip(*estimators)
names, _ = zip(*_name_estimators(transformers))
transformer_list = list(zip(names, transformers, columns))
return transformer_list
def make_column_transformer(*transformers, **kwargs):
"""Construct a ColumnTransformer from the given transformers.
This is a shorthand for the ColumnTransformer constructor; it does not
require, and does not permit, naming the transformers. Instead, they will
be given names automatically based on their types. It also does not allow
weighting with ``transformer_weights``.
Read more in the :ref:`User Guide <make_column_transformer>`.
Parameters
----------
*transformers : tuples
Tuples of the form (transformer, column(s)) specifying the
transformer objects to be applied to subsets of the data.
transformer : estimator or {'passthrough', 'drop'}
Estimator must support :term:`fit` and :term:`transform`.
Special-cased 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, array-like 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 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.
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` 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
non-specified columns will use the ``remainder`` estimator. The
estimator must support :term:`fit` and :term:`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 :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
verbose : boolean, optional(default=False)
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
Returns
-------
ct : ColumnTransformer
See also
--------
sklearn.compose.ColumnTransformer : Class that allows combining the
outputs of multiple transformer objects used on column subsets
of the data into a single feature space.
Examples
--------
>>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
>>> from sklearn.compose import make_column_transformer
>>> make_column_transformer(
... (StandardScaler(), ['numerical_column']),
... (OneHotEncoder(), ['categorical_column']))
ColumnTransformer(transformers=[('standardscaler', StandardScaler(...),
['numerical_column']),
('onehotencoder', OneHotEncoder(...),
['categorical_column'])])
"""
# transformer_weights keyword is not passed through because the user
# would need to know the automatically generated names of the transformers
n_jobs = kwargs.pop('n_jobs', None)
remainder = kwargs.pop('remainder', 'drop')
sparse_threshold = kwargs.pop('sparse_threshold', 0.3)
verbose = kwargs.pop('verbose', False)
if kwargs:
raise TypeError('Unknown keyword arguments: "{}"'
.format(list(kwargs.keys())[0]))
transformer_list = _get_transformer_list(transformers)
return ColumnTransformer(transformer_list, n_jobs=n_jobs,
remainder=remainder,
sparse_threshold=sparse_threshold,
verbose=verbose)
def _is_negative_indexing(key):
# TODO: remove in v0.24
def is_neg(x): return isinstance(x, numbers.Integral) and x < 0
if isinstance(key, slice):
return is_neg(key.start) or is_neg(key.stop)
elif _determine_key_type(key) == 'int':
return np.any(np.asarray(key) < 0)
return False
class make_column_selector:
"""Create a callable to select columns to be used with
:class:`ColumnTransformer`.
:func:`make_column_selector` can select columns based on datatype or the
columns name with a regex. When using multiple selection criteria, **all**
criteria must match for a column to be selected.
Parameters
----------
pattern : str, default=None
Name of columns containing this regex pattern will be included. If
None, column selection will not be selected based on pattern.
dtype_include : column dtype or list of column dtypes, default=None
A selection of dtypes to include. For more details, see
:meth:`pandas.DataFrame.select_dtypes`.
dtype_exclude : column dtype or list of column dtypes, default=None
A selection of dtypes to exclude. For more details, see
:meth:`pandas.DataFrame.select_dtypes`.
Returns
-------
selector : callable
Callable for column selection to be used by a
:class:`ColumnTransformer`.
See also
--------
sklearn.compose.ColumnTransformer : Class that allows combining the
outputs of multiple transformer objects used on column subsets
of the data into a single feature space.
Examples
--------
>>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
>>> from sklearn.compose import make_column_transformer
>>> from sklearn.compose import make_column_selector
>>> import pandas as pd # doctest: +SKIP
>>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'],
... 'rating': [5, 3, 4, 5]}) # doctest: +SKIP
>>> ct = make_column_transformer(
... (StandardScaler(),
... make_column_selector(dtype_include=np.number)), # rating
... (OneHotEncoder(),
... make_column_selector(dtype_include=object))) # city
>>> ct.fit_transform(X) # doctest: +SKIP
array([[ 0.90453403, 1. , 0. , 0. ],
[-1.50755672, 1. , 0. , 0. ],
[-0.30151134, 0. , 1. , 0. ],
[ 0.90453403, 0. , 0. , 1. ]])
"""
def __init__(self, pattern=None, dtype_include=None, dtype_exclude=None):
self.pattern = pattern
self.dtype_include = dtype_include
self.dtype_exclude = dtype_exclude
def __call__(self, df):
if not hasattr(df, 'iloc'):
raise ValueError("make_column_selector can only be applied to "
"pandas dataframes")
df_row = df.iloc[:1]
if self.dtype_include is not None or self.dtype_exclude is not None:
df_row = df_row.select_dtypes(include=self.dtype_include,
exclude=self.dtype_exclude)
cols = df_row.columns
if self.pattern is not None:
cols = cols[cols.str.contains(self.pattern, regex=True)]
return cols.tolist()