Data type of pandas column changes to object when it's passed to a function via apply?

Question

I need to use the dtype of a pandas column in a function, but for some reason when I call the function using apply, the dtype is changed to object. Does anyone know what is happening here?

import pandas as pd

df = pd.DataFrame({'stringcol':['a'], 'floatcol': [1.5]})
df.dtypes
Out[1]: 
floatcol     float64
stringcol     object
dtype: object

df.apply(lambda col: col.dtype)
Out[2]: 
floatcol     object
stringcol    object
dtype: object

Note that this problem doesn't happen if the column is passed directly:

f = lambda col: col.dtype
f(test.floatcol)
Out[3]: dtype('float64')

Answer 1

It appears to be due to an optimization in DataFrame._apply_standard. The "fast path" in the code of that method creates an output Series whose dtype is the dtype of df.values, which in your case is object since the DataFrame is of mixed type. If you pass reduce=False to your apply call, the result is correct:

>>> df.apply(lambda col: col.dtype, reduce=False)
floatcol     float64
stringcol     object
dtype: object

(I must say that it is not clear to me how this behavior of reduce jibes with the documentation.)

Answer 2

For pandas versions v0.23+ the answer is:

>>> df.apply(lambda x: x.dtype, result_type='expand')

This works even though the Pandas documentation claims that the result_type argument "only act when axis=1 (columns)"

credit @jezrael