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# numpy.linalg.vector_norm

linalg.vector_norm(x, /, *, axis=None, keepdims=False, ord=2)

  Computes the vector norm of a vector (or batch of vectors) `x`.

  This function is Array API compatible.

  Parameters:
    x array_like

      Input array.

    axis{None, int, 2-tuple of ints}, optional

      If an integer, `axis` specifies the axis (dimension) along which to compute vector norms. If an n-tuple, `axis` specifies the axes (dimensions) along which to compute batched vector norms. If `None`, the vector norm must be computed over all array values (i.e., equivalent to computing the vector norm of a flattened array). Default: `None`.

    keepdims bool, optional

      If this is set to True, the axes which are normed over are left in the result as dimensions with size one. Default: False.

    ord{int, float, inf, -inf}, optional

      The order of the norm. For details see the table under `Notes` in `numpy.linalg.norm`.

  See also

  `numpy.linalg.norm`

    Generic norm function

  Examples

      >>> from numpy import linalg as LA
      >>> a = np.arange(9) + 1
      >>> a
      array([1, 2, 3, 4, 5, 6, 7, 8, 9])
      >>> b = a.reshape((3, 3))
      >>> b
      array([[1, 2, 3],
             [4, 5, 6],
             [7, 8, 9]])

      >>> LA.vector_norm(b)
      16.881943016134134
      >>> LA.vector_norm(b, ord=np.inf)
      9.0
      >>> LA.vector_norm(b, ord=-np.inf)
      1.0

      >>> LA.vector_norm(b, ord=0)
      9.0
      >>> LA.vector_norm(b, ord=1)
      45.0
      >>> LA.vector_norm(b, ord=-1)
      0.3534857623790153
      >>> LA.vector_norm(b, ord=2)
      16.881943016134134
      >>> LA.vector_norm(b, ord=-2)
      0.8058837395885292