ERR: Shorten traceback in groupby _cython_agg_general
- [x] closes #52930 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

The code

```
df = pd.DataFrame({"a": [1, 2, 3], "b": 1, "c": "aaaa"})
df.groupby("a").mean(numeric_only=False)
```

now raises

```
Traceback (most recent call last):
  File "/home/richard/scratch.py", line 59, in <module>
    df.groupby("a").mean(numeric_only=False)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1971, in mean
    result = self._cython_agg_general(
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1651, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)
  File "/home/richard/dev/pandas/pandas/core/internals/managers.py", line 1497, in grouped_reduce
    applied = sb.apply(func)
  File "/home/richard/dev/pandas/pandas/core/internals/blocks.py", line 328, in apply
    result = func(self.values, **kwargs)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1648, in array_func
    result = self._agg_py_fallback(values, ndim=data.ndim, alt=alt)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1604, in _agg_py_fallback
    res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
  File "/home/richard/dev/pandas/pandas/core/groupby/ops.py", line 841, in agg_series
    result = self._aggregate_series_pure_python(obj, func)
  File "/home/richard/dev/pandas/pandas/core/groupby/ops.py", line 862, in _aggregate_series_pure_python
    res = func(group)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1973, in <lambda>
    alt=lambda x: Series(x).mean(numeric_only=numeric_only),
  File "/home/richard/dev/pandas/pandas/core/series.py", line 5954, in mean
    return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
  File "/home/richard/dev/pandas/pandas/core/generic.py", line 11436, in mean
    return self._stat_function(
  File "/home/richard/dev/pandas/pandas/core/generic.py", line 11393, in _stat_function
    return self._reduce(
  File "/home/richard/dev/pandas/pandas/core/series.py", line 5862, in _reduce
    return op(delegate, skipna=skipna, **kwds)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 148, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 404, in new_func
    result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 720, in nanmean
    the_sum = _ensure_numeric(the_sum)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 1686, in _ensure_numeric
    raise TypeError(f"Could not convert string '{x}' to numeric")
TypeError: Could not convert string 'aaaa' to numeric
```
What's your opinion on raising 
```
Traceback (most recent call last):
  File "/Users/patrick/PycharmProjects/pandas/pandas/core/groupby/groupby.py", line 1643, in array_func
    result = self.grouper._cython_operation(
  File "/Users/patrick/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 806, in _cython_operation
    return cy_op.cython_operation(
  File "/Users/patrick/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 531, in cython_operation
    return self._cython_op_ndim_compat(
  File "/Users/patrick/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 335, in _cython_op_ndim_compat
    return self._call_cython_op(
  File "/Users/patrick/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 392, in _call_cython_op
    func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric)
  File "/Users/patrick/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 195, in _get_cython_function
    raise NotImplementedError(
NotImplementedError: function is not implemented for this dtype: [how->mean,dtype->object]
```

instead? This seems more deliberate than an internal bug like th other does to me I think that message would be misleading, e.g.

```
df = pd.DataFrame({"a": [1, 2, 3], "b": 1, "c": 1}, dtype=object)
print(df.groupby("a").mean(numeric_only=False))
#      b    c
# a          
# 1  1.0  1.0
# 2  1.0  1.0
# 3  1.0  1.0
```

mean on dtype object is implemented, it just takes the Python fallback path.

> This seems more deliberate than an internal bug like th other does to me

Not sure what you mean by internal bug here. If I get an error message like the above, my first instinct would be that the maintainers of the library made a mistake, not that my computation is wrong.

Wdyt about an error message like: ``Operation foo is not supported for this type``? > TypeError: Could not convert string 'aaaa' to numeric

My reaction to this is "of course you can't convert, I shouldn't be trying an operations that tries to convert the string to numeric".

> Wdyt about an error message like: `Operation foo is not supported for this type`?

What is "type" referring to here? It seems to me users could interpret this as object dtype which is technically not accurate.
 Yeah this is the tricky part, specifying what exactly is wrong.

I think I illustrated my example in a bad way. It's hard to see which operation was wrong with this error. For example:

```
df = pd.DataFrame(
    {
        "a": 1,
        "int": [1, 2],
        "str": "a"
    }
)

df.groupby("a").agg({"int": "sum", "float": "str"})
```

No reference to column/operation and dtype isn't great I think
 I think your last line should be:

    df.groupby("a").agg({"int": "sum", "str": "mean"})

Agreed this should be improved. ~I'd like to handle it in a separate PR since it's a different path.~ I think it can be handled here, and improve this case as well. @phofl - how does something like this look:

```
Traceback (most recent call last):
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1598, in _agg_py_fallback
    res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
  File "/home/richard/dev/pandas/pandas/core/groupby/ops.py", line 847, in agg_series
    result = self._aggregate_series_pure_python(obj, func)
  File "/home/richard/dev/pandas/pandas/core/groupby/ops.py", line 868, in _aggregate_series_pure_python
    res = func(group)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1971, in <lambda>
    alt=lambda x: Series(x).mean(numeric_only=numeric_only),
  File "/home/richard/dev/pandas/pandas/core/series.py", line 6030, in mean
    return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
  File "/home/richard/dev/pandas/pandas/core/generic.py", line 11510, in mean
    return self._stat_function(
  File "/home/richard/dev/pandas/pandas/core/generic.py", line 11467, in _stat_function
    return self._reduce(
  File "/home/richard/dev/pandas/pandas/core/series.py", line 5938, in _reduce
    return op(delegate, skipna=skipna, **kwds)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 147, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 402, in new_func
    result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 718, in nanmean
    the_sum = _ensure_numeric(the_sum)
  File "/home/richard/dev/pandas/pandas/core/nanops.py", line 1687, in _ensure_numeric
    raise TypeError(f"Could not convert string '{x}' to numeric")
TypeError: Could not convert string 'aaaa' to numeric

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

Traceback (most recent call last):
  File "/home/richard/.config/JetBrains/PyCharmCE2022.2/scratches/scratch.py", line 46, in <module>
    df.groupby("a").mean(numeric_only=False)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1969, in mean
    result = self._cython_agg_general(
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1649, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)
  File "/home/richard/dev/pandas/pandas/core/internals/managers.py", line 1511, in grouped_reduce
    applied = sb.apply(func)
  File "/home/richard/dev/pandas/pandas/core/internals/blocks.py", line 330, in apply
    result = func(self.values, **kwargs)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1646, in array_func
    result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
  File "/home/richard/dev/pandas/pandas/core/groupby/groupby.py", line 1601, in _agg_py_fallback
    raise NotImplementedError(msg) from err
NotImplementedError: function is not implemented for [how->mean,dtype->object]

Process finished with exit code 1
```

The negatives in my opinion are that it's back to being rather long, and it seems to me the user could think it means the function is not implemented for object dtype (which can be incorrect). However, it specifies the method of aggregation, the dtype, and the value on which it failed which can help users understand where it's coming from. I'd prefer that one. In general, I am not a fan that this works sometimes for object, but this is a different discussion. This message is much clearer based on what I'd expect @phofl - I reworked the raising here. We now raise the same type of exception as main; I think this is better than raising a NotImplementedError in general, and has the added benefit of being less likely to break user code. @phofl - friendly ping. thx @rhshadrach  How would you be able to solve this?

import pandas as pd
df = pd.read_csv('salaries_by_college_major.csv')
df.head()
df.shape
df['Starting Median Salary']
df.loc[43]
print(df['Mid-Career Median Salary'].max())
print(f"Index for the max mid career salary: {df['Mid-Career Median Salary'].idxmax()}")
df['Undergraduate Major'][8]
print(df["Starting Median Salary"].min())
df['Undergraduate Major'].loc[df['Starting Median Salary'].idxmin()]
df.loc[df['Mid-Career Median Salary'].idxmin()]
df['Mid-Career 90th Percentile Salary']  -  df['Mid-Career 10th Percentile Salary']
spread_col = df['Mid-Career 90th Percentile Salary'] - df['Mid-Career 10th Percentile Salary']
df.insert(1, 'Spread', spread_col)
df.head()
low_risk = df.sort_values('Spread')
low_risk[['Undergraduate Major', 'Spread']].head()
highest_potential = df.sort_values('Mid-Career 90th Percentile Salary', ascending=False)
highest_potential[['Undergraduate Major', 'Mid-Career 90th Percentile Salary']].head()

highest_spread = df.sort_values('Spread', ascending=False)
highest_spread[['Undergraduate Major', 'Spread']].head()
df.groupby('Group').mean()
pd.options.display.float_format = '{:,.2f}'.format






ERR: TypeError                                 Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py](https://localhost:8080/#) in _agg_py_fallback(self, how, values, ndim, alt)
   1873         try:
-> 1874             res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
   1875         except Exception as err:

15 frames
TypeError: Cannot convert ['Accounting' 'Agriculture' 'Architecture' 'Business Management'
 'Construction' 'Economics' 'Finance' 'Forestry'
 'Health Care Administration' 'Hospitality & Tourism' 'Marketing'
 'Nursing'] to numeric

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

TypeError                                 Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py](https://localhost:8080/#) in _agg_py_fallback(self, how, values, ndim, alt)
   1876             msg = f"agg function failed [how->{how},dtype->{ser.dtype}]"
   1877             # preserve the kind of exception that raised
-> 1878             raise type(err)(msg) from err
   1879 
   1880         if ser.dtype == object:

TypeError: agg function failed [how->median,dtype->object]