Traceback (most recent call last):
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\groupby.py", line 1942, in _agg_py_fallback
    res_values = self._grouper.agg_series(ser, alt, preserve_dtype=True)
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\ops.py", line 864, in agg_series
    result = self._aggregate_series_pure_python(obj, func)
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\ops.py", line 885, in _aggregate_series_pure_python
    res = func(group)
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\groupby.py", line 2454, in <lambda>
    alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only),
                  ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\site\python313\Lib\site-packages\pandas\core\series.py", line 6549, in mean
    return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
           ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\site\python313\Lib\site-packages\pandas\core\generic.py", line 12420, in mean
    return self._stat_function(
           ~~~~~~~~~~~~~~~~~~~^
        "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "D:\site\python313\Lib\site-packages\pandas\core\generic.py", line 12377, in _stat_function
    return self._reduce(
           ~~~~~~~~~~~~^
        func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "D:\site\python313\Lib\site-packages\pandas\core\series.py", line 6457, in _reduce
    return op(delegate, skipna=skipna, **kwds)
  File "D:\site\python313\Lib\site-packages\pandas\core\nanops.py", line 147, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)
  File "D:\site\python313\Lib\site-packages\pandas\core\nanops.py", line 404, in new_func
    result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
  File "D:\site\python313\Lib\site-packages\pandas\core\nanops.py", line 720, in nanmean
    the_sum = _ensure_numeric(the_sum)
  File "D:\site\python313\Lib\site-packages\pandas\core\nanops.py", line 1701, in _ensure_numeric
    raise TypeError(f"Could not convert string '{x}' to numeric")
TypeError: Could not convert string 'FiestaFocusMondeoB-Max' to numeric

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "example.py", line 11, in <module>
    print(df.groupby(["car"]).mean())
          ~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\groupby.py", line 2452, in mean
    result = self._cython_agg_general(
        "mean",
        alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only),
        numeric_only=numeric_only,
    )
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\groupby.py", line 1998, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)
  File "D:\site\python313\Lib\site-packages\pandas\core\internals\managers.py", line 1469, in grouped_reduce
    applied = sb.apply(func)
  File "D:\site\python313\Lib\site-packages\pandas\core\internals\blocks.py", line 393, in apply
    result = func(self.values, **kwargs)
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\groupby.py", line 1995, in array_func
    result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
  File "D:\site\python313\Lib\site-packages\pandas\core\groupby\groupby.py", line 1946, in _agg_py_fallback
    raise type(err)(msg) from err
TypeError: agg function failed [how->mean,dtype->object]