BUG: DataFrame creates an object-dtype array for extension type scalars in dictionaries

[justinessert]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Code Sample, a copy-pastable example

df = pd.DataFrame({
    'a': [pd.Period('2020-01')],
    'b': pd.Period('2020-01')
})

df.dtypes

Problem description

In the example above, column a will be correctly created as a period[m] column, but column b will be an object type. This is similar to an issue 34832, but for a different DF instantiation method.

Expected Output

These two columns should have identical types. They should both be type period[m].

Output of pd.show_versions()

INSTALLED VERSIONS

commit : c413df6
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Sun Dec 1 18:59:03 PST 2019; root:xnu-4903.278.19~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.2.0.dev0+173.gc413df6d6.dirty
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200616
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.16.2
sphinx : 3.1.1
blosc : 1.9.1
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.0

[dsaxton]

Also when some array-likes are passed in directly we get object dtype:

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: pd.__version__
Out[3]: '1.1.1'

In [4]: pd.DataFrame(3 * [pd.Period("2020-01")]).dtypes
Out[4]:
0    object
dtype: object

In [5]: pd.DataFrame(np.array(3 * [pd.Period("2020-01")])).dtypes
Out[5]:
0    object
dtype: object

[justinessert]

Notably, a 2d array does work as expected though

In [1]: pd.DataFrame([[pd.Period('2020-01'), 1], [pd.Period('2020-01'), 1]]).dtypes
Out [1]:
0    period[M]
1        int64
dtype: object