dpnp.var

dpnp.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)[source]

Compute the variance along the specified axis.

For full documentation refer to numpy.var.

Parameters:

  • a ({dpnp.ndarray, usm_ndarray}) -- Input array.

  • axis ({None, int, tuple of ints}, optional) -- axis or axes along which the variances must be computed. If a tuple of unique integers is given, the variances are computed over multiple axes. If None, the variance is computed over the entire array. Default: None.

  • dtype ({None, dtype}, optional) -- Type to use in computing the variance. By default, if a has a floating-point data type, the returned array will have the same data type as a. If a has a boolean or integral data type, the returned array will have the default floating point data type for the device where input array a is allocated.

  • out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary.

  • ddof ({int, float}, optional) -- Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N corresponds to the total number of elements over which the variance is calculated. Default: 0.0.

  • keepdims ({None, bool}, optional) -- If True, the reduced axes (dimensions) are included in the result as singleton dimensions, so that the returned array remains compatible with the input array according to Array Broadcasting rules. Otherwise, if False, the reduced axes are not included in the returned array. Default: False.

Returns:

out -- An array containing the variances. If the variance was computed over the entire array, a zero-dimensional array is returned.

Return type:

dpnp.ndarray

Limitations

Parameters where is only supported with its default value. Otherwise NotImplementedError exception will be raised.

Notes

Note that, for complex numbers, the absolute value is taken before squaring, so that the result is always real and non-negative.

See also

dpnp.ndarray.var

corresponding function for ndarrays.

dpnp.std

Compute the standard deviation along the specified axis.

dpnp.mean

Compute the arithmetic mean along the specified axis.

dpnp.nanmean

Compute the arithmetic mean along the specified axis, ignoring NaNs.

dpnp.nanstd

Compute the standard deviation along the specified axis, while ignoring NaNs.

dpnp.nanvar

Compute the variance along the specified axis, while ignoring NaNs.

Examples

>>> import dpnp as np
>>> a = np.array([[1, 2], [3, 4]])
>>> np.var(a)
array(1.25)
>>> np.var(a, axis=0)
array([1.,  1.])
>>> np.var(a, axis=1)
array([0.25,  0.25])