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Conceptually, applying a function along an axis of a `DataFrame` (i.e., applying it *to* each row or column) inherently produces a `Series`: a two-dimensional result is collapsed to a one-dimensional result, because one-dimensional "lines" of data are fed into a function that produces a scalar value.

Such a series can be *appended as* a row to an existing `DataFrame`, if the labels are compatible - such as with the original `DataFrame`:
```python
>>> df.loc['avg'] = df.mean()
>>> df
       A    B
0    1.0  5.0
1    2.0  6.0
2    3.0  7.0
3    4.0  8.0
avg  2.5  6.5
```
However, creating a row *by itself* - i.e., in a new DataFrame - either requires an existing DataFrame with those labels:
```python
>>> x = df.mean()
>>> y = pd.DataFrame(columns=x.index)
>>> y.loc[0] = x
>>> y
     A    B
0  2.5  6.5
```
or creating it as you have tried already. As a hint, the `T` property saves some typing:
```python
>>> df.mean().to_frame().T
     A    B
0  2.5  6.5
```
However, there is not an option for converting a `Series` *directly* to a single-row `DataFrame`; it converts to a column regardless.

Reference: https://stackoverflow.com/questions/59406045