BUG/API: Index constructor does not enforce specified dtype

[jorisvandenbossche]

Code Sample, a copy-pastable example if possible

Manually specifying a dtype does not garantuee the output is in that dtype. Eg with Series if incompatible data is passed, an error is raised, while for Index it just silently outputs another dtype:

In [11]: pd.Series(['a', 'b', 'c'], dtype='int64')
...
ValueError: invalid literal for int() with base 10: 'a'

In [12]: pd.Index(['a', 'b', 'c'], dtype='int64')
Out[12]: Index(['a', 'b', 'c'], dtype='object')

[jschendel]

xref #21254 (comment)

Meant to create an issue similar to this for the Categorical --> Interval case:

In [2]: cat = pd.Categorical([pd.Interval(0, 1), pd.Interval(1, 2), pd.Interval(0, 1)])

In [3]: pd.Index(cat, dtype='interval')
Out[3]: CategoricalIndex([(0, 1], (1, 2], (0, 1]], categories=[(0, 1], (1, 2]], ordered=False, dtype='category')

This happens because the Index code is structured so that categorical takes precedence over interval:

pandas/pandas/core/indexes/base.py

Lines 262 to 273 in c85ab08

	# categorical
	if is_categorical_dtype(data) or is_categorical_dtype(dtype):
	from .category import CategoricalIndex
	return CategoricalIndex(data, dtype=dtype, copy=copy, name=name,
	**kwargs)
	# interval
	if is_interval_dtype(data) or is_interval_dtype(dtype):
	from .interval import IntervalIndex
	closed = kwargs.get('closed', None)
	return IntervalIndex(data, dtype=dtype, name=name, copy=copy,
	closed=closed)

The code above could be restructured so that the dtype argument, if present, takes precedence over the type of data.

Not immediately sure what the fix for other scenarios entails, as Categorical and Interval are a bit of a special case.