BUG: Constructing a Series from a scalar generally doesn't work for extension types

[jschendel]

Code Sample, a copy-pastable example if possible

Construction without specifying a dtype results in object dtype for Interval and Period:

In [2]: pd.Series(pd.Interval(0, 1), index=range(3))
Out[2]: 
0    (0, 1]
1    (0, 1]
2    (0, 1]
dtype: object

In [3]: pd.Series(pd.Period("2019Q1", freq="Q"), index=range(3))
Out[3]: 
0    2019Q1
1    2019Q1
2    2019Q1
dtype: object

This looks okay for a tz-aware Timestamp:

In [4]: pd.Series(pd.Timestamp("2019", tz="US/Eastern"), index=range(3))
Out[4]: 
0   2019-01-01 00:00:00-05:00
1   2019-01-01 00:00:00-05:00
2   2019-01-01 00:00:00-05:00
dtype: datetime64[ns, US/Eastern]

Specifying a dtype raises for Interval, Period and tz-aware Timestamp:

In [5]: pd.Series(pd.Interval(0, 1), index=range(3), dtype=pd.IntervalDtype("int64"))
---------------------------------------------------------------------------
TypeError: IntervalArray(...) must be called with a collection of some kind, (0, 1] was passed

In [6]: pd.Series(pd.Period("2019Q1", freq="Q"), index=range(3), dtype=pd.PeriodDtype("Q"))
---------------------------------------------------------------------------
ValueError: Buffer has wrong number of dimensions (expected 1, got 0)

In [7]: pd.Series(pd.Timestamp("2019", tz="US/Eastern"), index=range(3), dtype=pd.DatetimeTZDtype(tz="US/Eastern"))
---------------------------------------------------------------------------
TypeError: 'Timestamp' object is not iterable

Both of the above patterns appear to be working fine when using scalars that correspond to non-extensions dtypes (e.g. numeric, tz-naive, timedelta).

Problem description

The Series constructor is not correctly inferring the dtype from scalar Interval/Period objects when a dtype isn't specified, and raises for Interval/Period/tz-aware Timestamp when a dtype is specified.

Expected Output

I'd expect the Series to be constructed with the proper dtype.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 261c3a6
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.14-041914-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.25.0+332.g261c3a667
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.10
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8

[jorisvandenbossche]

infer_dtype works fine on lists of intervals/periods:

In [11]: pd.api.types.infer_dtype([pd.Interval(0,1)], skipna=True)
Out[11]: 'interval'

so we could use that on object dtype data? (although that can also be costly)
In the Series construction code, we currently have special code for "maybe casting to datetime" (eg sanitize_array -> maybe_cast_to_datetime)

[KangMingHsi]

For this issue, I found that line 469 in construction.py
dtype, value = infer_dtype_from_scalar(value)
should change to dtype, value = infer_dtype_from_scalar(value, pandas_type=True)
After doing this revision, dtype bug would be fixed

> pd.Series(pd.Interval(0, 1), index=range(3))
0    (0, 1]
1    (0, 1]
2    (0, 1]
dtype: interval

And dtype of pd.Series(pd.Period("2019Q1", freq="Q"), index=range(3)) would also be correct,
but here comes another bug in line 711 cast.py
val = val.ordinal
It makes return value of Period to be int, and then

Traceback (most recent call last):
  File "pandas/_libs/tslibs/period.pyx", line 1399, in pandas._libs.tslibs.period.extract_ordinals
    ordinals[i] = p.ordinal
AttributeError: 'int' object has no attribute 'ordinal'

Why method infer_dtype_from_scalar change return val of Period to Period.ordinal?

[arw2019]

This works on 1.2 master:

In [2]:  pd.Series(pd.Interval(0, 1), index=range(3))
Out[2]: 
0    (0, 1]
1    (0, 1]
2    (0, 1]
dtype: interval

In [3]: pd.Series(pd.Period("2019Q1", freq="Q"), index=range(3))
Out[3]: 
0    2019Q1
1    2019Q1
2    2019Q1
dtype: period[Q-DEC]