array_equal(a1, a2, equal_nan=False)
Input arrays.
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
.
Returns True if the arrays are equal.
True if two arrays have the same shape and elements, False otherwise.
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.
>>> 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) True
When 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])See :
... b = a.copy()
... a.real = np.nan
... b.imag = np.nan
... np.array_equal(a, b, equal_nan=True) True
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.allclose
scipy.signal._bsplines.cubic
scipy.signal._bsplines.quadratic
dask.array.routines.insert
scipy.optimize._qap.quadratic_assignment
scipy.signal._bsplines.bspline
dask.array.creation.meshgrid
pandas.core.generic.NDFrame.equals
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