Dtype inconsistency when appending to empty dataframe

[jonathanrocher]

Code Sample, a copy-pastable example if possible

>>> pd.DataFrame().append({"a": 1}, ignore_index=True)
     a
0  1.0

Problem description

I would expect append not to change the dtype of the a column from int to float, and for the output to be identical to that of pd.concat([pd.DataFrame(), pd.DataFrame([{"a": 1}])])

Expected Output

>>> pd.DataFrame().append({"a": 1}, ignore_index=True)
     a
0  1

Output of pd.show_versions()

INSTALLED VERSIONS

commit: 4612312
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.24.0.dev0+547.g4612312
pytest: None
pip: 18.0
setuptools: 34.3.3
Cython: 0.25.2
numpy: 1.13.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

[TomAugspurger]

I suppose it's because the default dtype for a reindex is float

In [28]: pd.DataFrame().reindex(columns=['a']).dtypes
Out[28]:
a    float64
dtype: object

then we append an int Series, and you end up with float.

I'm not sure how feasible changing Out[28] to be anything other than float is.

[jonathanrocher]

Ah right. But pd.concat does the right thing so I wonder if we could let concat handle more so this use case behaves better. I need to spend more time with the method's implementation...

[devamin]

This could lead to unexpected bugs, specially if we do an aggregation after