Pandas API: Series(floaty, dtype=inty)

import numpy as np
import pandas as pd

vals = [1.5, 2.5]

pd.Series(vals, dtype=np.int64)  # <- float64
pd.Series(np.array(vals), dtype=np.int64)  # <- float64
pd.Series(np.array(vals, dtype=object), dtype=np.int64)  # <- raises
pd.Series(vals).astype(np.int64)  # <- int64

pd.Index(vals, dtype=np.int64)  # <- raises
pd.Index(np.array(vals), dtype=np.int64)  # <- float64
pd.Index(np.array(vals, dtype=object), dtype=np.int64)  # <- raises
pd.Index(vals).astype(np.int64)  # <- int64

xref #49372

When passing non-round float-like data and an integer dtype we have special logic https://github.com/pandas-dev/pandas/blob/d56f6e2bca75f10bc490aa320c4e1bc295e367bd/pandas/core/construction.py#L598 that ignores the dtype keyword and silently returns a float dtype. This violates at least 3 aspirational rules of thumb :

1) when a user asks for specific dtype, we should either return that dtype or raise 2) pd.Index behavior should match pd.Series behavior 3) pd.Series(values, dtype=dtype) should match pd.Series(values).astype(dtype)

I would be OK with either returning integer dtype or raising here. If we prioritize consistency with .astype behavior then this will depend on decisions in #45588.

Currently we have 13 tests that fail if we disable this behavior (9 of which are a single parametrized test) and they are all directly testing this behavior, so changing it wouldn't be world-breaking.