pandas (original) (raw)

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