# numpy.find_common_type#

numpy.find_common_type(array_types, scalar_types)[source]#

Determine common type following standard coercion rules.

Deprecated since version NumPy: 1.25

This function is deprecated, use `numpy.promote_types` or `numpy.result_type` instead. To achieve semantics for the scalar_types argument, use `numpy.result_type` and pass the Python values 0, 0.0, or 0j. This will give the same results in almost all cases. More information and rare exception can be found in the NumPy 1.25 release notes.

Parameters:
array_typessequence

A list of dtypes or dtype convertible objects representing arrays.

scalar_typessequence

A list of dtypes or dtype convertible objects representing scalars.

Returns:
datatypedtype

The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (`dtype.kind`). If the kind is not understood, then None is returned.

Examples

```>>> np.find_common_type([], [np.int64, np.float32, complex])
dtype('complex128')
>>> np.find_common_type([np.int64, np.float32], [])
dtype('float64')
```

The standard casting rules ensure that a scalar cannot up-cast an array unless the scalar is of a fundamentally different kind of data (i.e. under a different hierarchy in the data type hierarchy) then the array:

```>>> np.find_common_type([np.float32], [np.int64, np.float64])
dtype('float32')
```

Complex is of a different type, so it up-casts the float in the array_types argument:

```>>> np.find_common_type([np.float32], [complex])
dtype('complex128')
```

Type specifier strings are convertible to dtypes and can therefore be used instead of dtypes:

```>>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
dtype('complex128')
```