`numpy:array_equal`

Signature

def array_equal ( a1 , a2 , equal_nan = False )

True if two arrays have the same shape and elements, False otherwise.

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.

import numpy as np

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np.array_equal([1, 2], [1, 2])

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np.array_equal(np.array([1, 2]), np.array([1, 2]))

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np.array_equal([1, 2], [1, 2, 3])

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np.array_equal([1, 2], [1, 4])

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a = np.array([1, np.nan])
np.array_equal(a, a)

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np.array_equal(a, a, equal_nan=True)

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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])
b = a.copy()
a.real = np.nan
b.imag = np.nan
np.array_equal(a, b, equal_nan=True)

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