# numpy.minimum

numpy.minimum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'minimum'>

  Element-wise minimum of array elements.

  Compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated.

  Parameters
    x1, x2 array_like

      The arrays holding the elements to be compared. If `x1.shape != x2.shape`, they must be broadcastable to a common shape (which becomes the shape of the output).

    out ndarray, None, or tuple of ndarray and None, optional

      A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

    where array_like, optional

      This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default `out=None`, locations within it where the condition is False will remain uninitialized.

    **kwargs

      For other keyword-only arguments, see the ufunc docs.

  Returns
    y ndarray or scalar

      The minimum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.

  See also

  `maximum`

    Element-wise maximum of two arrays, propagates NaNs.

  `fmin`

    Element-wise minimum of two arrays, ignores NaNs.

  `amin`

    The minimum value of an array along a given axis, propagates NaNs.

  `nanmin`

    The minimum value of an array along a given axis, ignores NaNs.

  `fmax`, `amax`, `nanmax`

  Notes

  The minimum is equivalent to `np.where(x1 <= x2, x1, x2)` when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting.

  Examples

      >>> np.minimum([2, 3, 4], [1, 5, 2])
      array([1, 3, 2])

      >>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting
      array([[ 0.5,  0. ],
             [ 0. ,  1. ]])

      >>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])
      array([nan, nan, nan])
      >>> np.minimum(-np.Inf, 1)
      -inf

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