numpy.eye#

numpy.eye(N, M=None, k=0, dtype=<class 'float'>, order='C', *, device=None, like=None)[source]#

Return a 2-D array with ones on the diagonal and zeros elsewhere.

Parameters:

Nint: Number of rows in the output.
Mint, optional: Number of columns in the output. If None, defaults to N.
kint, optional: Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.
dtypedata-type, optional: Data-type of the returned array.
order{C, F}, optional: Whether the output should be stored in row-major (C-style) or column-major (Fortran-style) order in memory.
devicestr, optional: The device on which to place the created array. Default: None. For Array-API interoperability only, so must be "cpu" if passed.

New in version 2.0.0.
likearray_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:

Indarray of shape (N,M): An array where all elements are equal to zero, except for the k-th diagonal, whose values are equal to one.

See also

identity: (almost) equivalent function
diag: diagonal 2-D array from a 1-D array specified by the user.

Examples

Try it in your browser!

>>> import numpy as np
>>> np.eye(2, dtype=np.int_)
array([[1, 0],
       [0, 1]])
>>> np.eye(3, k=1)
array([[0.,  1.,  0.],
       [0.,  0.,  1.],
       [0.,  0.,  0.]])

numpy.empty_like

numpy.identity