DropNA

Schnellstart

from sktime.transformations.dropna import DropNA

estimator = DropNA(axis=0, how=None, thresh=None, remember=None)

Parameter(4)

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”)).

Beispiele

>>> 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