Are Decimal 'dtypes' available in NumPy?
Question:
Are Decimal data type objects (dtypes) available in NumPy?
>>> import decimal, numpy
>>> d = decimal.Decimal('1.1')
>>> s = [['123.123','23'],['2323.212','123123.21312']]
>>> ss = numpy.array(s, dtype=numpy.dtype(decimal.Decimal))
>>> a = numpy.array(s, dtype=float)
>>> type(d)
<class 'decimal.Decimal'>
>>> type(ss[1,1])
<class 'str'>
>>> type(a[1,1])
<class 'numpy.float64'>
I suppose numpy.array doesn’t support every dtype, but I sort of thought that it would at least let a dtype propagate as far as it could as long as the right operations were defined. Am I missing something? Is there some way for this to work?
Answers:
Important caveat: this is a bad answer
You would probably do best to skip to the next answer.
It seems that Decimal is available:
>>> import decimal, numpy
>>> d = decimal.Decimal('1.1')
>>> a = numpy.array([d,d,d],dtype=numpy.dtype(decimal.Decimal))
>>> type(a[1])
<class 'decimal.Decimal'>
I’m not sure exactly what you are trying to accomplish. Your example is more complicated than is necessary for simply creating a decimal NumPy array.
Unfortunately, you have to cast each of your items to Decimal when you create the numpy.array. Something like
s = [['123.123','23'],['2323.212','123123.21312']]
decimal_s = [[decimal.Decimal(x) for x in y] for y in s]
ss = numpy.array(decimal_s)
NumPy doesn’t recognize decimal.Decimal as a specific type. The closest it can get is the most general dtype, object. So when converting the elements to the desired dtype, the conversion is a no operation.
>>> ss.dtype
dtype('object')
Keep in mind that because the elements of the array are Python objects, you won’t get much of a speedup using them. For example, if you try to add this to any other array, the other elements will have to be boxed back into Python objects and added via the normal Python addition code. You might gain some speed in that the iteration will be in C, but not that much.
Are Decimal data type objects (dtypes) available in NumPy?
>>> import decimal, numpy
>>> d = decimal.Decimal('1.1')
>>> s = [['123.123','23'],['2323.212','123123.21312']]
>>> ss = numpy.array(s, dtype=numpy.dtype(decimal.Decimal))
>>> a = numpy.array(s, dtype=float)
>>> type(d)
<class 'decimal.Decimal'>
>>> type(ss[1,1])
<class 'str'>
>>> type(a[1,1])
<class 'numpy.float64'>
I suppose numpy.array doesn’t support every dtype, but I sort of thought that it would at least let a dtype propagate as far as it could as long as the right operations were defined. Am I missing something? Is there some way for this to work?
Important caveat: this is a bad answer
You would probably do best to skip to the next answer.
It seems that Decimal is available:
>>> import decimal, numpy
>>> d = decimal.Decimal('1.1')
>>> a = numpy.array([d,d,d],dtype=numpy.dtype(decimal.Decimal))
>>> type(a[1])
<class 'decimal.Decimal'>
I’m not sure exactly what you are trying to accomplish. Your example is more complicated than is necessary for simply creating a decimal NumPy array.
Unfortunately, you have to cast each of your items to Decimal when you create the numpy.array. Something like
s = [['123.123','23'],['2323.212','123123.21312']]
decimal_s = [[decimal.Decimal(x) for x in y] for y in s]
ss = numpy.array(decimal_s)
NumPy doesn’t recognize decimal.Decimal as a specific type. The closest it can get is the most general dtype, object. So when converting the elements to the desired dtype, the conversion is a no operation.
>>> ss.dtype
dtype('object')
Keep in mind that because the elements of the array are Python objects, you won’t get much of a speedup using them. For example, if you try to add this to any other array, the other elements will have to be boxed back into Python objects and added via the normal Python addition code. You might gain some speed in that the iteration will be in C, but not that much.