jax.numpy.nanmean#

jax.numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=False, where=None)[source]#

Return the mean of the array elements along a given axis, ignoring NaNs.

JAX implementation of numpy.nanmean().

Parameters:

  • a (ArrayLike) – Input array.

  • axis (Axis) – int or sequence of ints, default=None. Axis along which the mean is computed. If None, the mean is computed along the flattened array.

  • dtype (DTypeLike | None) – The type of the output array. Default=None.

  • keepdims (bool) – bool, default=False. If True, reduced axes are left in the result with size 1.

  • where (ArrayLike | None) – array of boolean dtype, default=None. The elements to be used in computing mean. Array should be broadcast compatible to the input.

  • out (None) – Unused by JAX.

Returns:

An array containing the mean of array elements along the given axis, ignoring NaNs. If all elements along the given axis are NaNs, returns nan.

Return type:

Array

See also

  • jax.numpy.nanmin(): Compute the minimum of array elements along a given axis, ignoring NaNs.

  • jax.numpy.nanmax(): Compute the maximum of array elements along a given axis, ignoring NaNs.

  • jax.numpy.nansum(): Compute the sum of array elements along a given axis, ignoring NaNs.

  • jax.numpy.nanprod(): Compute the product of array elements along a given axis, ignoring NaNs.

Examples

By default, jnp.nanmean computes the mean of elements along the flattened array.

>>> nan = jnp.nan
>>> x = jnp.array([[2, nan, 4, 3],
...                [nan, -2, nan, 9],
...                [4, -7, 6, nan]])
>>> jnp.nanmean(x)
Array(2.375, dtype=float32)

If axis=1, mean will be computed along axis 1.

>>> jnp.nanmean(x, axis=1)
Array([3. , 3.5, 1. ], dtype=float32)

If keepdims=True, ndim of the output will be same of that of the input.

>>> jnp.nanmean(x, axis=1, keepdims=True)
Array([[3. ],
       [3.5],
       [1. ]], dtype=float32)

where can be used to include only specific elements in computing the mean.

>>> where = jnp.array([[1, 0, 1, 0],
...                    [0, 0, 1, 1],
...                    [1, 1, 0, 1]], dtype=bool)
>>> jnp.nanmean(x, axis=1, keepdims=True, where=where)
Array([[ 3. ],
       [ 9. ],
       [-1.5]], dtype=float32)

If where is False at all elements, jnp.nanmean returns nan along the given axis.

>>> where = jnp.array([[False],
...                    [False],
...                    [False]])
>>> jnp.nanmean(x, axis=0, keepdims=True, where=where)
Array([[nan, nan, nan, nan]], dtype=float32)