in operator, float("NaN") and np.nan

To check if the item is in the list, Python tests for object identity first, and then tests for equality only if the objects are different.¹

float("NaN") in [float("NaN")] is False because two different NaN objects are involved in the comparison. The test for identity therefore returns False, and then the test for equality also returns False since NaN != NaN.

np.nan in [np.nan, 1, 2] however is True because the same NaN object is involved in the comparison. The test for object identity returns True and so Python immediately recognises the item as being in the list.

The __contains__ method (invoked using in) for many of Python's other builtin Container types, such as tuples and sets, is implemented using the same check.

¹ At least this is true in CPython. Object identity here means that the objects are found at the same memory address: the contains method for lists is performed using PyObject_RichCompareBool which quickly compares object pointers before a potentially more complicated object comparison. Other Python implementations may differ.

One thing worth mentioning is that numpy arrays do behave as expected:

a = np.array((np.nan,))a[0] in a# False

Variations of the theme:

[np.nan]==[np.nan]# True[float('nan')]==[float('nan')]# False{np.nan: 0}[np.nan]# 0{float('nan'): 0}[float('nan')]# Traceback (most recent call last):#   File "<stdin>", line 1, in <module># KeyError: nan

Everything else is covered in @AlexRiley's excellent answer.