pingouin.remove_na#

pingouin.remove_na(x, y=None, paired=False, axis='rows')[source]#

Remove missing values along a given axis in one or more (paired) numpy arrays.

Parameters:

x, y1D or 2D arrays: Data. x and y must have the same number of dimensions. y can be None to only remove missing values in x.
pairedbool: Indicates if the measurements are paired or not.
axisstr: Axis or axes along which missing values are removed. Can be ‘rows’ or ‘columns’. This has no effect if x and y are one-dimensional arrays.

Returns:

x, ynp.ndarray: Data without missing values

Examples

Single 1D array

>>> import numpy as np
>>> from pingouin import remove_na
>>> x = [6.4, 3.2, 4.5, np.nan]
>>> remove_na(x)
array([6.4, 3.2, 4.5])

With two paired 1D arrays

>>> y = [2.3, np.nan, 5.2, 4.6]
>>> remove_na(x, y, paired=True)
(array([6.4, 4.5]), array([2.3, 5.2]))

With two independent 2D arrays

>>> x = np.array([[4, 2], [4, np.nan], [7, 6]])
>>> y = np.array([[6, np.nan], [3, 2], [2, 2]])
>>> x_no_nan, y_no_nan = remove_na(x, y, paired=False)