zeros

Documentation

treetensor.numpy.zeros(shape, *args, **kwargs)

Numpy Version Related

This documentation is based on numpy.zeros in numpy v1.24.4. Its arguments’ arrangements depend on the version of numpy you installed.

If some arguments listed here are not working properly, please check your numpy’s version with the following command and find its documentation.

python -c 'import numpy as np;print(np.__version__)'

The arguments and keyword arguments supported in numpy v1.24.4 is listed below.

Description From Numpy v1.24

numpy.zeros(shape, dtype=float, order='C', *, like=None)

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

Parameters

shape : int or tuple 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, default: ‘C’

Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.

like : array_like, optional

Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

New in version 1.20.0.

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. empty : Return a new uninitialized array. ones : Return a new array setting values to one. full : Return a new array of given shape filled with value.

Examples

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

>>> np.zeros((5,), dtype=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')])