python - Set values in numpy array to NaN by index

Question

I want to set specific values in a numpy array to NaN (to exclude them from a row-wise mean calculation).

I tried

import numpy

x = numpy.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]])
cutoff = [5, 7]
for i in range(len(x)):
    x[i][0:cutoff[i]:1] = numpy.nan

Looking at x, I only see -9223372036854775808 where I expect NaN.

I thought about an alternative:

for i in range(len(x)):
    for k in range(cutoff[i]):
        x[i][k] = numpy.nan

Nothing happens. What am I doing wrong?

Answer 1

nan is a floating-point value. When x is an array with integer dtype, it can not be assigned a nan value. When nan is assigned to an array of integer dtype, the value is automatically converted to an int:

In [85]: np.array(np.nan).astype(int).item()
Out[85]: -9223372036854775808

So to fix your code, make x an array of float dtype:

x = numpy.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]], 
                dtype=float)

---

import numpy

x = numpy.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]], 
                dtype=float)
cutoff = [5, 7]
for i in range(len(x)):
    x[i][0:cutoff[i]:1] = numpy.nan
 print(x)

yields

array([[ nan,  nan,  nan,  nan,  nan,   5.,   6.,   7.,   8.,   9.],
       [ nan,  nan,  nan,  nan,  nan,  nan,  nan,   0.,   1.,   0.]])