DF.groupby(col).min works, mean gets a "not implemented" error

[deanhystad]

My guess is mpg.csv contains something that confuses pands.read_csv.

I can get the same error like this:

import pandas as pd

df = pd.read_csv("data.csv")

print(df)
print(df.dtypes)
print(df.groupby("model_year").mean()["mpg"])

Output:
   model_year mpg
0           1   l
1           2   1
2           2   2
3           3   1
4           3   2
5           3   3
model_year     int64
mpg           object
dtype: object

Error:
Traceback (most recent call last):
  File "venv\lib\site-packages\pandas\core\groupby\groupby.py", line 1871, in _agg_py_fallback
    res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
  File "venv\lib\site-packages\pandas\core\groupby\ops.py", line 850, in agg_series
    result = self._aggregate_series_pure_python(obj, func)
  File "venv\lib\site-packages\pandas\core\groupby\ops.py", line 871, in _aggregate_series_pure_python
    res = func(group)
  File "venv\lib\site-packages\pandas\core\groupby\groupby.py", line 2377, in <lambda>
    alt=lambda x: Series(x).mean(numeric_only=numeric_only),
  File "venv\lib\site-packages\pandas\core\series.py", line 6221, in mean
    return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
  File "venv\lib\site-packages\pandas\core\generic.py", line 11978, in mean
    return self._stat_function(
  File "venv\lib\site-packages\pandas\core\generic.py", line 11935, in _stat_function
    return self._reduce(
  File "venv\lib\site-packages\pandas\core\series.py", line 6129, in _reduce
    return op(delegate, skipna=skipna, **kwds)
  File "venv\lib\site-packages\pandas\core\nanops.py", line 147, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)
  File "venv\lib\site-packages\pandas\core\nanops.py", line 404, in new_func
    result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
  File "venv\lib\site-packages\pandas\core\nanops.py", line 720, in nanmean
    the_sum = _ensure_numeric(the_sum)
  File "venv\lib\site-packages\pandas\core\nanops.py", line 1693, in _ensure_numeric
    raise TypeError(f"Could not convert string '{x}' to numeric")
TypeError: Could not convert string 'l' to numeric

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

Traceback (most recent call last):
  File "test.py", line 7, in <module>
    print(df.groupby("model_year").mean()["mpg"])
  File "venv\lib\site-packages\pandas\core\groupby\groupby.py", line 2375, in mean
    result = self._cython_agg_general(
  File "venv\lib\site-packages\pandas\core\groupby\groupby.py", line 1926, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)
  File "venv\lib\site-packages\pandas\core\internals\managers.py", line 1428, in grouped_reduce
    applied = sb.apply(func)
  File "venv\lib\site-packages\pandas\core\internals\blocks.py", line 366, in apply
    result = func(self.values, **kwargs)
  File "venv\lib\site-packages\pandas\core\groupby\groupby.py", line 1923, in array_func
    result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
  File "venv\lib\site-packages\pandas\core\groupby\groupby.py", line 1875, in _agg_py_fallback
    raise type(err)(msg) from err
TypeError: agg function failed [how->mean,dtype->object]

In my example my mpg.csv has a lowercase L instead of a one. You get a slightly different error trace, but that may be due to the mpg.csv file having a slightly different error. Can you post mpg.csv?