Error occur when exec the last code block to put the full workflow into action.
ypeError Traceback (most recent call last)
Cell In[18], line 8
5 image_basename="drink_sales"
7 # Run the complete agentic workflow
----> 8 _ = run_workflow(
9 dataset_path="coffee_sales.csv",
10 user_instructions=user_instructions,
11 generation_model=generation_model,
12 reflection_model=reflection_model,
13 image_basename=image_basename
14 )
Cell In[15], line 41, in run_workflow(dataset_path, user_instructions, generation_model, reflection_model, image_basename)
39 initial_code = match.group(1).strip()
40 exec_globals = {"df": df}
---> 41 exec(initial_code, exec_globals)
42 utils.print_html(out_v1, is_image=True, title="Generated Chart (V1)")
44 # 3) Reflect on V1 (image + original code) to get feedback and refined code (V2)
File <string>:8
File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:3153, in GroupBy.sum(self, numeric_only, min_count, engine, engine_kwargs)
3148 else:
3149 # If we are grouping on categoricals we want unobserved categories to
3150 # return zero, rather than the default of NaN which the reindexing in
3151 # _agg_general() returns. GH #31422
3152 with com.temp_setattr(self, "observed", True):
-> 3153 result = self._agg_general(
3154 numeric_only=numeric_only,
3155 min_count=min_count,
3156 alias="sum",
3157 npfunc=np.sum,
3158 )
3160 return self._reindex_output(result, fill_value=0, method="sum")
File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1908, in GroupBy._agg_general(self, numeric_only, min_count, alias, npfunc, **kwargs)
1898 @final
1899 def _agg_general(
1900 self,
(...)
1906 **kwargs,
1907 ):
-> 1908 result = self._cython_agg_general(
1909 how=alias,
1910 alt=npfunc,
1911 numeric_only=numeric_only,
1912 min_count=min_count,
1913 **kwargs,
1914 )
1915 return result.__finalize__(self.obj, method="groupby")
File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:2005, in GroupBy._cython_agg_general(self, how, alt, numeric_only, min_count, **kwargs)
2002 result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
2003 return result
-> 2005 new_mgr = data.grouped_reduce(array_func)
2006 res = self._wrap_agged_manager(new_mgr)
2007 if how in ["idxmin", "idxmax"]:
File /usr/local/lib/python3.11/site-packages/pandas/core/internals/managers.py:1491, in BlockManager.grouped_reduce(self, func)
1489 result_blocks = extend_blocks(applied, result_blocks)
1490 else:
-> 1491 applied = blk.apply(func)
1492 result_blocks = extend_blocks(applied, result_blocks)
1494 if len(result_blocks) == 0:
File /usr/local/lib/python3.11/site-packages/pandas/core/internals/blocks.py:395, in Block.apply(self, func, **kwargs)
389 @final
390 def apply(self, func, **kwargs) -> list[Block]:
391 """
392 apply the function to my values; return a block if we are not
393 one
394 """
--> 395 result = func(self.values, **kwargs)
397 result = maybe_coerce_values(result)
398 return self._split_op_result(result)
File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1980, in GroupBy._cython_agg_general.<locals>.array_func(values)
1978 def array_func(values: ArrayLike) -> ArrayLike:
1979 try:
-> 1980 result = self._grouper._cython_operation(
1981 "aggregate",
1982 values,
1983 how,
1984 axis=data.ndim - 1,
1985 min_count=min_count,
1986 **kwargs,
1987 )
1988 except NotImplementedError:
1989 # generally if we have numeric_only=False
1990 # and non-applicable functions
1991 # try to python agg
1992 # TODO: shouldn't min_count matter?
1993 # TODO: avoid special casing SparseArray here
1994 if how in ["any", "all"] and isinstance(values, SparseArray):
File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/ops.py:840, in BaseGrouper._cython_operation(self, kind, values, how, axis, min_count, **kwargs)
838 ids, _, _ = self.group_info
839 ngroups = self.ngroups
--> 840 return cy_op.cython_operation(
841 values=values,
842 axis=axis,
843 min_count=min_count,
844 comp_ids=ids,
845 ngroups=ngroups,
846 **kwargs,
847 )
File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/ops.py:550, in WrappedCythonOp.cython_operation(self, values, axis, min_count, comp_ids, ngroups, **kwargs)
546 self._validate_axis(axis, values)
548 if not isinstance(values, np.ndarray):
549 # i.e. ExtensionArray
--> 550 return values._groupby_op(
551 how=self.how,
552 has_dropped_na=self.has_dropped_na,
553 min_count=min_count,
554 ngroups=ngroups,
555 ids=comp_ids,
556 **kwargs,
557 )
559 return self._cython_op_ndim_compat(
560 values,
561 min_count=min_count,
(...)
565 **kwargs,
566 )
File /usr/local/lib/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1694, in DatetimeLikeArrayMixin._groupby_op(self, how, has_dropped_na, min_count, ngroups, ids, **kwargs)
1691 if dtype.kind == "M":
1692 # Adding/multiplying datetimes is not valid
1693 if how in ["sum", "prod", "cumsum", "cumprod", "var", "skew"]:
-> 1694 raise TypeError(f"datetime64 type does not support {how} operations")
1695 if how in ["any", "all"]:
1696 # GH#34479
1697 warnings.warn(
1698 f"'{how}' with datetime64 dtypes is deprecated and will raise in a "
1699 f"future version. Use (obj != pd.Timestamp(0)).{how}() instead.",
1700 FutureWarning,
1701 stacklevel=find_stack_level(),
1702 )
TypeError: datetime64 type does not support sum operations