np.zeros(5)
array([ 0., 0., 0., 0., 0.])


np.zeros((5,), dtype=np.int)
array([0, 0, 0, 0, 0])


np.zeros((2, 1))
array([[ 0.],
    [ 0.]])


s = (2,2)
np.zeros(s)
array([[ 0., 0.],
    [ 0., 0.]])


np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
array([(0, 0), (0, 0)],
   dtype=[('x', '<i4'), ('y', '<i4')])


########################################################

zeros(shape, dtype=float, order='C')



Return a new array of given shape and type, filled with zeros.


Parameters
----------
shape : int or sequence of ints
  Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
  The desired data-type for the array, e.g., `numpy.int8`. Default is
  `numpy.float64`.
order : {'C', 'F'}, optional
  Whether to store multidimensional data in C- or Fortran-contiguous
  (row- or column-wise) order in memory.


Returns
-------
out : ndarray
  Array of zeros with the given shape, dtype, and order.


See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
empty_like : Return an empty array with shape and type of input.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.


Examples
--------
np.zeros(5)
array([ 0., 0., 0., 0., 0.])


np.zeros((5,), dtype=np.int)
array([0, 0, 0, 0, 0])


np.zeros((2, 1))
array([[ 0.],
    [ 0.]])


s = (2,2)
np.zeros(s)
array([[ 0., 0.],
    [ 0., 0.]])


np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
array([(0, 0), (0, 0)],
   dtype=[('x', '<i4'), ('y', '<i4')])
Type:   builtin_function_or_method