I have a list of numbers which I put into a numpy array:

```
>>> import numpy as np
>>> v=np.array([10.0, 11.0])
```

then I want to subtract a number from each value in the array. It can be done like this with numpy arrays:

```
>>> print v - 1.0
[ 9. 10.]
```

Unfortunately, my data often contains missing values, represented by `None`

. For this kind of data I get this error:

```
>>> v=np.array([10.0, 11.0, None])
>>> print v - 1.0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for -: 'NoneType' and 'float'
```

What I would like to get for the above example is:

```
[ 9. 10. None]
```

How can I achieve it in an easy and efficient way?

Best answer

My recommendation is to either use masked arrays:

```
v = np.ma.array([10., 11, 0],mask=[0, 0, 1])
print v - 10
>>> [0.0 1.0 --]
```

or NaNs

```
v = np.array([10.,11,np.nan])
print v - 10
>>> [ 0. 1. nan]
```

I actually prefer NaNs as missing data indicators.