BUG: Creating DataFrame frome Series with DatetimeIndex results in NaN-filled df

[yohplala]

• I have checked that this issue has not already been reported (but not 100% sure either - search returns many tickets).

• 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

# Work when creating a df without datetime index.
pd.DataFrame({'a':pd.Series([1,2])})
Out[11]: 
   a
0  1
1  2

# Work when working with a datetime index and only with values of the Series
# (either with 'to_numpy()' or 'values').
pd.DataFrame({'a':pd.Series([1,2]).values},
             index=[pd.Timestamp('2021/01/01 08:00'), pd.Timestamp('2021/01/01 09:00')])
Out[12]: 
                     a
2021-01-01 08:00:00  1
2021-01-01 09:00:00  2

# Does not work when working directly with Series and datetime index.
pd.DataFrame({'a':pd.Series([1,2])},
             index=[pd.Timestamp('2021/01/01 08:00'), pd.Timestamp('2021/01/01 09:00')])
Out[13]: 
                      a
2021-01-01 08:00:00 NaN
2021-01-01 09:00:00 NaN

Problem description

In my real case problem, I am generating data through dict completion in which are handled pd.Series.
Something like:

var = pd.Series(list(range(1,5)))
data = { n : (var.shift(-n) - var)/var for n in range(1,3)}
df = pd.DataFrame(data, index=pd.date_range(pd.Timestamp('2021/01/01 08:00'), periods=4, freq='1h'))

And this results in a df filled with NaN

Is this a bug or a feature?

Expected Output

A df not filled with NaN.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 2cb9652
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-55-generic
Version : #62~20.04.1-Ubuntu SMP Wed Jun 2 08:55:04 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8

pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : 0.6.3
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2

[jreback]

when u create a frame from a series it is reindexed

this is a feature

[phofl]

Yes, this is the same as

df = pd.DataFrame({"a": pd.Series([1, 2])}, index=[3, 4])

[yohplala]

Hi @jreback @phofl !
Thanks a lot for your reactivity. I feel this line makes it completely crystal clear

df = pd.DataFrame({"a": pd.Series([1, 2])}, index=[0,3,4])

df
Out[7]: 
     a
0  1.0
3  NaN
4  NaN

Sorry for the ticket then and thanks again!
Bests