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

• I have confirmed this bug exists on the latest version of pandas (1.3.0rc1).

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

Code Sample, a copy-pastable example

import pandas as pd data = pd.array([0, 1, 2, 3], dtype="Int32") df = expected = pd.DataFrame({"data": pd.Series(data)}) result = pd.DataFrame(index=df.index) result.loc[df.index, "data"] = df["data"] print(df["data"].dtype) # prints: Int32 print(result["data"].dtype) # prints: float64 <--

Problem description

In my mind, this behavior seems unexpected because the provided dtype should be preserved and not coerced to the default type for an empty Series. This occurs for the nullable integer dtypes as well as Float32/Float64.

I came across this when trying to implement an ExtensionDtype that ended up failing on BaseSetitemTest. test_setitem_with_expansion_dataframe_column:

Interestingly, in the tests for IntegerArray and FloatingArray, the test data includes NaN values which do not result in the coercion to float64:

import pandas as pd data = pd.array([0, pd.NaT, 2, 3], dtype="Int32") df = expected = pd.DataFrame({"data": pd.Series(data)}) result = pd.DataFrame(index=df.index) result.loc[df.index, "data"] = df["data"] print(df["data"].dtype) # prints: Int32 print(result["data"].dtype) # prints: Int32 <--

My expectation was that the dtype should be preserved in such cases, with/without NaN values.

Expected Output

I would expect that the dtype of the pd.Series being added to result would be preserved, in the case of the minimal example, result["data"] should be Int32Dtype.

print(df["data"].dtype) # prints: Int32 print(result["data"].dtype) # prints: Int32 <--

Output of pd.show_versions()

This was generated from the latest release candidate, but it appears to also occur on the master branch (1.4.0.dev0+56.g648eb40abc)