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.

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.


Indicates if the measurements are paired or not.


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.

x, ynp.ndarray

Data without missing values


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)