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promote_types(type1, type2)

This function is symmetric, but rarely associative.

Notes

versionadded

Starting in NumPy 1.9, promote_types function now returns a valid string length when given an integer or float dtype as one argument and a string dtype as another argument. Previously it always returned the input string dtype, even if it wasn't long enough to store the max integer/float value converted to a string.

Parameters

type1 : dtype or dtype specifier

First data type.

type2 : dtype or dtype specifier

Second data type.

Returns

out : dtype

The promoted data type.

Returns the data type with the smallest size and smallest scalar kind to which both type1 and type2 may be safely cast. The returned data type is always in native byte order.

See Also

can_cast
dtype
result_type

Examples

>>> np.promote_types('f4', 'f8')
dtype('float64')
>>> np.promote_types('i8', 'f4')
dtype('float64')
>>> np.promote_types('>i8', '<c8')
dtype('complex128')
>>> np.promote_types('i4', 'S8')
dtype('S11')

An example of a non-associative case:

>>> p = np.promote_types
... p('S', p('i1', 'u1')) dtype('S6')
>>> p(p('S', 'i1'), 'u1')
dtype('S4')
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.result_type numpy.min_scalar_type numpy.core._multiarray_umath.min_scalar_type numpy.core._multiarray_umath.result_type

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