numpy.result_type¶

numpy.
result_type
(*arrays_and_dtypes)¶ Returns the type that results from applying the NumPy type promotion rules to the arguments.
Type promotion in NumPy works similarly to the rules in languages like C++, with some slight differences. When both scalars and arrays are used, the array’s type takes precedence and the actual value of the scalar is taken into account.
For example, calculating 3*a, where a is an array of 32bit floats, intuitively should result in a 32bit float output. If the 3 is a 32bit integer, the NumPy rules indicate it can’t convert losslessly into a 32bit float, so a 64bit float should be the result type. By examining the value of the constant, ‘3’, we see that it fits in an 8bit integer, which can be cast losslessly into the 32bit float.
 Parameters
 arrays_and_dtypeslist of arrays and dtypes
The operands of some operation whose result type is needed.
 Returns
 outdtype
The result type.
See also
Notes
New in version 1.6.0.
The specific algorithm used is as follows.
Categories are determined by first checking which of boolean, integer (int/uint), or floating point (float/complex) the maximum kind of all the arrays and the scalars are.
If there are only scalars or the maximum category of the scalars is higher than the maximum category of the arrays, the data types are combined with
promote_types
to produce the return value.Otherwise,
min_scalar_type
is called on each array, and the resulting data types are all combined withpromote_types
to produce the return value.The set of int values is not a subset of the uint values for types with the same number of bits, something not reflected in
min_scalar_type
, but handled as a special case inresult_type
.Examples
>>> np.result_type(3, np.arange(7, dtype='i1')) dtype('int8')
>>> np.result_type('i4', 'c8') dtype('complex128')
>>> np.result_type(3.0, 2) dtype('float64')