DropNA

class DropNA(axis=0, how=None, thresh=None, remember=None)[source]

Drop missing values transformation.

Drops rows or columns with missing values from X. Mostly wraps pandas.DataFrame.dropna, but allows specifying thresh as a fraction of non-missing observations.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Determine if rows or columns which contain missing values are removed. Must be 0 or ‘index’ for univariate input.

0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.

how{‘any’, ‘all’}, default ‘any’

Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

‘any’ : If any NA values are present, drop that row or column.
‘all’ : If all values are NA, drop that row or column.

threshint or float, optional

If int, require at least that many non-NA values (as in pandas.dropna). If float, minimum share of non-NA values for rows/columns to be retained. Fraction must be contained within (0,1]. Setting fraction to 1.0 is equivalent to setting how=’any’. thresh cannot be combined with how.

rememberbool, default False if axis==0, True if axis==1

If True, drops the same rows/columns in transform as in fit. If false, drops rows/columns according to the NAs seen in transform (equivalent to PandasTransformAdaptor(method=”dropna”)).

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.transformations.dropna import DropNA
>>> import pandas as pd
>>> import numpy as np
>>> X = pd.DataFrame({'a': [1, 2, np.nan, 4], 'b': [5, np.nan, 7, 8]})
>>> transformer = DropNA(axis=0, how='any')
>>> X_transformed = transformer.fit_transform(X)
>>> print(X_transformed)
     a    b
0  1.0  5.0
3  4.0  8.0

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.