In Python, if I define a 2D array, and set the second row as np.nan, the second row will become all -9223372036854775808 rather than missing values. An example is here:
b = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 5],
[0, 3, 4, 4, 6, 6, 6, 5, 4, 5],
[0, 0, 0, 3, 6, 6, 6, 6, 6, 6],
[0, 0, 3, 4, 6, 6, 6, 6, 6, 6],
[0, 1, 2, 4, 4, 4, 4, 4, 4, 4]])
b[1, :] = np.nan
print(b)
[[ 0 0 0
0 0 0
0 0 0
5]
[-9223372036854775808 -9223372036854775808 -9223372036854775808
-9223372036854775808 -9223372036854775808 -9223372036854775808
-9223372036854775808 -9223372036854775808 -9223372036854775808
-9223372036854775808]
[ 0 0 0
3 6 6
6 6 6
6]
[ 0 0 3
4 6 6
6 6 6
6]
[ 0 1 2
4 4 4
4 4 4
4]]
Does anyone have any idea? And how should I correctly assign one row to np.nan?
For your reference, I am running these codes on python 3.7.10 environment created by mamba on Ubuntu 16.04.7 LTS (GNU/Linux 4.15.0-132-generic x86_64).
np.nan is a special floating point value that cannot be used in integer arrays. Since b is an array of integers, the code b[1, :] = np.nan attempts to convert np.nan to an integer, which is an undefined behavior. See this for a discussion of a similar issue.