API/BUG: Series(floating, dtype=intlike) ignores dtype, DataFrame casts

[jbrockmendel]

In most contexts, Series is strict about dtype, so will always either return the given dtype or raise. DataFrame is the opposite, often silently ignoring dtype (xref #24435) (i think on the theory that dtype may be intended to apply to some columns but not others).

With floating data and integer dtypes, its the opposite:

arr = np.random.randn(5)

>>> pd.Series(arr, dtype="int16")
0    1.002695
1    0.259332
2   -1.111468
3   -0.680714
4   -0.008943

>>> pd.DataFrame(arr, dtype="int16")
   0
0  1
1  0
2 -1
3  0
4  0

We have exactly one test that is broken if we change the latter behavior, and that is mostly by coincidence. There are other bugs (e.g. Series(bigints, dtype="int8") silently overflowing) that would be easier to fix if maintaining this behavior weren't a consideration.

Is this intentional? cc @jreback @jorisvandenbossche @TomAugspurger

[jorisvandenbossche]

No idea what the reason would be if it was intentional.

So I would be fine with changing it. I am only thinking if there is some way to deprecate it (which doesn't seem easy without introducing an extra keyword just to manager the deprecation ..), since it is a breaking change (actaul numbers would change)

[jorisvandenbossche]

It seems this changed behaviour before, as a long time ago we actually honored the dtype (I checked pd 0.20)

[jbrockmendel]

xref #26919

[jbrockmendel]

Tried changing this to only disallow float-int casting that included NaNs, but it turns out we explicitly test that Series([1, 2, 3.5], dtype=any_int_dtype) raises.

Is there reasoning/precedent for why we would treat list[float] differently from ndarray[float]? cc @jreback

[jreback]

only recollection is that we didn't want to silently cast floats to ints like numpy (but agreed this is a bit odd that we allow for array but not list)

[jbrockmendel]

any preference for changing/deprecating one or the other?