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# numpy.modf

numpy.modf(x, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'modf'>

  Return the fractional and integral parts of an array, element-wise.

  The fractional and integral parts are negative if the given number is negative.

  Parameters:
    x array_like

      Input array.

    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:
    y1 ndarray

      Fractional part of x. This is a scalar if x is a scalar.

    y2 ndarray

      Integral part of x. This is a scalar if x is a scalar.

  See also

  `divmod`

    `divmod(x, 1)` is equivalent to `modf` with the return values switched, except it always has a positive remainder.

  Notes

  For integer input the return values are floats.

  Examples

      >>> import numpy as np
      >>> np.modf([0, 3.5])
      (array([ 0. ,  0.5]), array([ 0.,  3.]))
      >>> np.modf(-0.5)
      (-0.5, -0)