Weird behaviour when filling a nested numpy array with `np.nan` [duplicate]

Question

I have a numpy array with a nested dtype:

dt = np.dtype([('e', '<f8'), ('n', '<i8'), ('pos', '<f8', (3,))])
arr = np.zeros(5, dtype=dt)

array([(0.0, 0, [0.0, 0.0, 0.0]), (0.0, 0, [0.0, 0.0, 0.0]),
       (0.0, 0, [0.0, 0.0, 0.0]), (0.0, 0, [0.0, 0.0, 0.0]),
       (0.0, 0, [0.0, 0.0, 0.0])],
      dtype=[('e', '<f8'), ('n', '<i8'), ('pos', '<f8', (3,))])

and I'm trying to fill the entire array with np.nan:

arr[:] = np.nan

However, this does not work as expected:

array([(nan, -9223372036854775808, [nan, nan, nan]),
       (nan, -9223372036854775808, [nan, nan, nan]),
       (nan, -9223372036854775808, [nan, nan, nan]),
       (nan, -9223372036854775808, [nan, nan, nan]),
       (nan, -9223372036854775808, [nan, nan, nan])],
      dtype=[('e', '<f8'), ('n', '<i8'), ('pos', '<f8', (3,))])

The second column here is obviously not nan.

Any idea how I can work around this issue?

Well the dtype is i8 and NaN cannot be represented by int dtype so it looks like all that happens is it's being set to some unitialised value — EdChum
– EdChum, Commented Jun 10, 2016 at 15:10

Warren Weckesser · Accepted Answer · 2016-06-10 15:15:48Z

3

The second column can't be nan. It has an integer type. There is no nan representation for integers.

The value -9223372036854775808 is -2**63, i.e. the most negative 64 bit signed integer.

edited Jun 10, 2016 at 15:15

answered Jun 10, 2016 at 15:10

Warren Weckesser

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