Transformer
DropNA
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.
Quickstart
python
from sktime.transformations.dropna import DropNA
estimator = DropNA(axis=0, how=None, thresh=None, remember=None)Tags
Capabilities
- Multivariate
- Unequal-length series
- Inverse transform: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- No
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(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”)).
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