Same is the behavior for setitem flow too:

In [1]: import pandas as pd

In [2]: df = pd.DataFrame()

In [3]: df['a'] = []

In [4]: df.dtypes
Out[4]: 
a    float64
dtype: object

Thanks for the report. I would also expect this to be object type from these similar constructions

In [4]: pd.DataFrame(columns=["a"]).dtypes
Out[4]: 
a    object
dtype: object

In [5]: pd.DataFrame([], columns=["a"]).dtypes
Out[5]: 
a    object
dtype: object

In [6]: pd.DataFrame({}, columns=["a"]).dtypes
Out[6]: 
a    object
dtype: object

In [7]: pd.DataFrame({"a": []}, columns=["a"]).dtypes
Out[7]: 
a    float64
dtype: object

In [8]: pd.DataFrame({"a": pd.Series()}).dtypes
Out[8]: 
a    object
dtype: object

It looks like this goes through sanitize_array where there's this comment

        if len(data) == 0 and dtype is None:
            # We default to float64, matching numpy
            subarr = np.array([], dtype=np.float64)

I'm not sure if there a reason internally why we need to treat this as float64 but I would expect at least via this constructor route that object is still returned

I would like to take a look at this issue

Thanks for the report. I would also expect this to be object type from these similar constructions

In [4]: pd.DataFrame(columns=["a"]).dtypes
Out[4]: 
a    object
dtype: object

In [5]: pd.DataFrame([], columns=["a"]).dtypes
Out[5]: 
a    object
dtype: object

In [6]: pd.DataFrame({}, columns=["a"]).dtypes
Out[6]: 
a    object
dtype: object

In [7]: pd.DataFrame({"a": []}, columns=["a"]).dtypes
Out[7]: 
a    float64
dtype: object

In [8]: pd.DataFrame({"a": pd.Series()}).dtypes
Out[8]: 
a    object
dtype: object

It looks like this goes through sanitize_array where there's this comment

        if len(data) == 0 and dtype is None:
            # We default to float64, matching numpy
            subarr = np.array([], dtype=np.float64)

I'm not sure if there a reason internally why we need to treat this as float64 but I would expect at least via this constructor route that object is still returned

I have debugged and observed that for all the cases except pd.DataFrame({'a': []}) we are getting the length of the data argument in sanitize_array to be 1.

does that mean there is inconsistency with only when an empty dictionary with specified column is passed.
and i think sanitize_array is being called twice for when its Dataframe, and in the second time we are getting float64. As you can see below in the call stack. The first time during initialising we are getting the data to be ['a'] but the second time its empty. when its being called from arrays_to_mgr() which you can see in second call stack.

Related: on an empty DataFrame, .values and ._values is a float whereas I'd expect object.

print(pd.DataFrame().values.dtype)
# float64

due to this line:

I ran into this because DataFrame.stack uses ._values to determine the result dtype, and on an empty frame we wind up with float whereas I'd expect object.

I'm not sure if there a reason internally why we need to treat this as float64 but I would expect at least via this constructor route that object is still returned

With both changes (handling the OP and the one I mentioned above), I'm seeing 35 tests fail in the expected way (i.e. there isn't some functionality we definitely don't want to change that breaks). It seems clear to me these changes would make dtypes on empty objects more consistent. The only question on my mind is if this is a bug fix or needs deprecation.

Especially with 3.0 as the next release I would be OK treating this as a "bug fix"

In starting to work on this, one of the things I noticed is that pd.array has the same "default to float" behavior, albeit slightly different.

print(pd.array([]).dtype)
# Float64

I'm thinking this should also be a NumPy object array?

I'm thinking this should also be a NumPy object array?

cc @jbrockmendel thoughts?