`dask_ml.preprocessing`.Categorizer¶

class dask_ml.preprocessing.Categorizer(categories=None, columns=None)¶

Transform columns of a DataFrame to categorical dtype.

This is a useful pre-processing step for dummy, one-hot, or categorical encoding.

Parameters:	categories : mapping, optional A dictionary mapping column name to instances of `pandas.api.types.CategoricalDtype`. Alternatively, a mapping of column name to `(categories, ordered)` tuples. columns : sequence, optional A sequence of column names to limit the categorization to. This argument is ignored when `categories` is specified.
Attributes:	columns_ : pandas.Index The columns that were categorized. Useful when `categories` is None, and we detect the categorical and object columns categories_ : dict A dictionary mapping column names to dtypes. For pandas>=0.21.0, the values are instances of `pandas.api.types.CategoricalDtype`. For older pandas, the values are tuples of `(categories, ordered)`.

Notes

This transformer only applies to dask.DataFrame and pandas.DataFrame. By default, all object-type columns are converted to categoricals. The set of categories will be the values present in the column and the categoricals will be unordered. Pass dtypes to control this behavior.

All other columns are included in the transformed output untouched.

For dask.DataFrame, any unknown categoricals will become known.

Examples

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": ['a', 'a', 'b']})
>>> ce = Categorizer()
>>> ce.fit_transform(df).dtypes
A       int64
B    category
dtype: object

>>> ce.categories_
{'B': CategoricalDtype(categories=['a', 'b'], ordered=False)}

Using CategoricalDtypes for specifying the categories:

>>> from pandas.api.types import CategoricalDtype
>>> ce = Categorizer(categories={"B": CategoricalDtype(['a', 'b', 'c'])})
>>> ce.fit_transform(df).B.dtype
CategoricalDtype(categories=['a', 'b', 'c'], ordered=False)

Methods

`fit`(self, X[, y])	Find the categorical columns.
`fit_transform`(self, X[, y])	Fit to data, then transform it.
`get_params`(self[, deep])	Get parameters for this estimator.
`set_params`(self, \\params)	Set the parameters of this estimator.
`transform`(self, X[, y])	Transform the columns in `X` according to `self.categories_`.

__init__(self, categories=None, columns=None)¶: Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)¶

Find the categorical columns.

Parameters:	X : pandas.DataFrame or dask.DataFrame y : ignored
Returns:	self

fit_transform(self, X, y=None, **fit_params)¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. **fit_params : dict Additional fit parameters.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(self, deep=True)¶

Get parameters for this estimator.

Parameters:	deep : bool, default=True 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.

set_params(self, **params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:	**params : dict Estimator parameters.
Returns:	self : object Estimator instance.

transform(self, X, y=None)¶

Transform the columns in X according to self.categories_.

Parameters:	X : pandas.DataFrame or dask.DataFrame y : ignored
Returns:	X_trn : pandas.DataFrame or dask.DataFrame Same type as the input. The columns in `self.categories_` will be converted to categorical dtype.

dask_ml.preprocessing.Categorizer¶

`dask_ml.preprocessing`.Categorizer¶