array_equal¶
Documentation¶
Numpy Version Related
This documentation is based on numpy.array_equal 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.
1 | 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¶
-
True if two arrays have the same shape and elements, False otherwise.
Parameters¶
- a1, a2 : array_like
Input arrays.
- equal_nan : bool
Whether to compare NaN’s as equal. If the dtype of a1 and a2 is complex, values will be considered equal if either the real or the imaginary component of a given value is
nan
.New in version 1.19.0.
Returns¶
- b : bool
Returns True if the arrays are equal.
See Also¶
- allclose: Returns True if two arrays are element-wise equal within a
tolerance.
- array_equiv: Returns True if input arrays are shape consistent and all
elements equal.
Examples¶
>>> np.array_equal([1, 2], [1, 2]) True >>> np.array_equal(np.array([1, 2]), np.array([1, 2])) True >>> np.array_equal([1, 2], [1, 2, 3]) False >>> np.array_equal([1, 2], [1, 4]) False >>> a = np.array([1, np.nan]) >>> np.array_equal(a, a) False >>> np.array_equal(a, a, equal_nan=True) TrueWhen
equal_nan
is True, complex values with nan components are considered equal if either the real or the imaginary components are nan.>>> a = np.array([1 + 1j]) >>> b = a.copy() >>> a.real = np.nan >>> b.imag = np.nan >>> np.array_equal(a, b, equal_nan=True) True