Name 컬럼을 인덱스로 설정하는 과정에서 오류 발생
- in
pd.pivot_table(df, index="Name")
df.pivot_table(index="Name")-out
NotImplementedError Traceback (most recent call last)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\groupby.py:1490, in GroupBy._cython_agg_general.<locals>.array_func(values)
1489 try:
-> 1490 result = self.grouper._cython_operation(
1491 "aggregate",
1492 values,
1493 how,
1494 axis=data.ndim - 1,
1495 min_count=min_count,
1496 **kwargs,
1497 )
1498 except NotImplementedError:
1499 # generally if we have numeric_only=False
1500 # and non-applicable functions
1501 # try to python agg
1502 # TODO: shouldn't min_count matter?
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\ops.py:959, in BaseGrouper._cython_operation(self, kind, values, how, axis, min_count, **kwargs)
958 ngroups = self.ngroups
--> 959 return cy_op.cython_operation(
960 values=values,
961 axis=axis,
962 min_count=min_count,
963 comp_ids=ids,
964 ngroups=ngroups,
965 **kwargs,
966 )
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\ops.py:657, in WrappedCythonOp.cython_operation(self, values, axis, min_count, comp_ids, ngroups, **kwargs)
649 return self._ea_wrap_cython_operation(
650 values,
651 min_count=min_count,
(...)
654 **kwargs,
655 )
--> 657 return self._cython_op_ndim_compat(
658 values,
659 min_count=min_count,
660 ngroups=ngroups,
661 comp_ids=comp_ids,
662 mask=None,
663 **kwargs,
664 )
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\ops.py:497, in WrappedCythonOp._cython_op_ndim_compat(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)
495 return res.T
--> 497 return self._call_cython_op(
498 values,
499 min_count=min_count,
500 ngroups=ngroups,
501 comp_ids=comp_ids,
502 mask=mask,
503 result_mask=result_mask,
504 **kwargs,
505 )
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\ops.py:541, in WrappedCythonOp._call_cython_op(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)
540 out_shape = self._get_output_shape(ngroups, values)
--> 541 func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric)
542 values = self._get_cython_vals(values)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\ops.py:173, in WrappedCythonOp._get_cython_function(cls, kind, how, dtype, is_numeric)
171 if "object" not in f.__signatures__:
172 # raise NotImplementedError here rather than TypeError later
--> 173 raise NotImplementedError(
174 f"function is not implemented for this dtype: "
175 f"[how->{how},dtype->{dtype_str}]"
176 )
177 return f
NotImplementedError: function is not implemented for this dtype: [how->mean,dtype->object]
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\nanops.py:1692, in _ensure_numeric(x)
1691 try:
-> 1692 x = float(x)
1693 except (TypeError, ValueError):
1694 # e.g. "1+1j" or "foo"
ValueError: could not convert string to float: 'CPU'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\nanops.py:1696, in _ensure_numeric(x)
1695 try:
-> 1696 x = complex(x)
1697 except ValueError as err:
1698 # e.g. "foo"
ValueError: complex() arg is a malformed string
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
Cell In[39], line 2
1 #멀티 인덱스 설정
----> 2 df.pivot_table(index=["Name", "Rep", "Manager"])
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\frame.py:8579, in DataFrame.pivot_table(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)
8562 @Substitution("")
8563 @Appender(_shared_docs["pivot_table"])
8564 def pivot_table(
(...)
8575 sort: bool = True,
8576 ) -> DataFrame:
8577 from pandas.core.reshape.pivot import pivot_table
-> 8579 return pivot_table(
8580 self,
8581 values=values,
8582 index=index,
8583 columns=columns,
8584 aggfunc=aggfunc,
8585 fill_value=fill_value,
8586 margins=margins,
8587 dropna=dropna,
8588 margins_name=margins_name,
8589 observed=observed,
8590 sort=sort,
8591 )
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\reshape\pivot.py:97, in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)
94 table = concat(pieces, keys=keys, axis=1)
95 return table.__finalize__(data, method="pivot_table")
---> 97 table = __internal_pivot_table(
98 data,
99 values,
100 index,
101 columns,
102 aggfunc,
103 fill_value,
104 margins,
105 dropna,
106 margins_name,
107 observed,
108 sort,
109 )
110 return table.__finalize__(data, method="pivot_table")
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\reshape\pivot.py:167, in __internal_pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)
164 values = list(values)
166 grouped = data.groupby(keys, observed=observed, sort=sort)
--> 167 agged = grouped.agg(aggfunc)
169 if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
170 agged = agged.dropna(how="all")
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\generic.py:1269, in DataFrameGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
1266 func = maybe_mangle_lambdas(func)
1268 op = GroupByApply(self, func, args, kwargs)
-> 1269 result = op.agg()
1270 if not is_dict_like(func) and result is not None:
1271 return result
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\apply.py:160, in Apply.agg(self)
157 kwargs = self.kwargs
159 if isinstance(arg, str):
--> 160 return self.apply_str()
162 if is_dict_like(arg):
163 return self.agg_dict_like()
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\apply.py:496, in Apply.apply_str(self)
494 if "axis" in arg_names:
495 self.kwargs["axis"] = self.axis
--> 496 return self._try_aggregate_string_function(obj, f, *self.args, **self.kwargs)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\apply.py:565, in Apply._try_aggregate_string_function(self, obj, arg, *args, **kwargs)
563 if f is not None:
564 if callable(f):
--> 565 return f(*args, **kwargs)
567 # people may try to aggregate on a non-callable attribute
568 # but don't let them think they can pass args to it
569 assert len(args) == 0
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\groupby.py:1855, in GroupBy.mean(self, numeric_only, engine, engine_kwargs)
1853 return self._numba_agg_general(sliding_mean, engine_kwargs)
1854 else:
-> 1855 result = self._cython_agg_general(
1856 "mean",
1857 alt=lambda x: Series(x).mean(numeric_only=numeric_only),
1858 numeric_only=numeric_only,
1859 )
1860 return result.__finalize__(self.obj, method="groupby")
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\groupby.py:1507, in GroupBy._cython_agg_general(self, how, alt, numeric_only, min_count, **kwargs)
1503 result = self._agg_py_fallback(values, ndim=data.ndim, alt=alt)
1505 return result
-> 1507 new_mgr = data.grouped_reduce(array_func)
1508 res = self._wrap_agged_manager(new_mgr)
1509 out = self._wrap_aggregated_output(res)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\internals\managers.py:1503, in BlockManager.grouped_reduce(self, func)
1499 if blk.is_object:
1500 # split on object-dtype blocks bc some columns may raise
1501 # while others do not.
1502 for sb in blk._split():
-> 1503 applied = sb.apply(func)
1504 result_blocks = extend_blocks(applied, result_blocks)
1505 else:
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\internals\blocks.py:329, in Block.apply(self, func, **kwargs)
323 @final
324 def apply(self, func, **kwargs) -> list[Block]:
325 """
326 apply the function to my values; return a block if we are not
327 one
328 """
--> 329 result = func(self.values, **kwargs)
331 return self._split_op_result(result)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\groupby.py:1503, in GroupBy._cython_agg_general.<locals>.array_func(values)
1490 result = self.grouper._cython_operation(
1491 "aggregate",
1492 values,
(...)
1496 **kwargs,
1497 )
1498 except NotImplementedError:
1499 # generally if we have numeric_only=False
1500 # and non-applicable functions
1501 # try to python agg
1502 # TODO: shouldn't min_count matter?
-> 1503 result = self._agg_py_fallback(values, ndim=data.ndim, alt=alt)
1505 return result
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\groupby.py:1457, in GroupBy._agg_py_fallback(self, values, ndim, alt)
1452 ser = df.iloc[:, 0]
1454 # We do not get here with UDFs, so we know that our dtype
1455 # should always be preserved by the implemented aggregations
1456 # TODO: Is this exactly right; see WrappedCythonOp get_result_dtype?
-> 1457 res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
1459 if isinstance(values, Categorical):
1460 # Because we only get here with known dtype-preserving
1461 # reductions, we cast back to Categorical.
1462 # TODO: if we ever get "rank" working, exclude it here.
1463 res_values = type(values)._from_sequence(res_values, dtype=values.dtype)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\ops.py:994, in BaseGrouper.agg_series(self, obj, func, preserve_dtype)
987 if len(obj) > 0 and not isinstance(obj._values, np.ndarray):
988 # we can preserve a little bit more aggressively with EA dtype
989 # because maybe_cast_pointwise_result will do a try/except
990 # with _from_sequence. NB we are assuming here that _from_sequence
991 # is sufficiently strict that it casts appropriately.
992 preserve_dtype = True
--> 994 result = self._aggregate_series_pure_python(obj, func)
996 npvalues = lib.maybe_convert_objects(result, try_float=False)
997 if preserve_dtype:
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\ops.py:1015, in BaseGrouper._aggregate_series_pure_python(self, obj, func)
1012 splitter = self._get_splitter(obj, axis=0)
1014 for i, group in enumerate(splitter):
-> 1015 res = func(group)
1016 res = libreduction.extract_result(res)
1018 if not initialized:
1019 # We only do this validation on the first iteration
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\groupby\groupby.py:1857, in GroupBy.mean.<locals>.<lambda>(x)
1853 return self._numba_agg_general(sliding_mean, engine_kwargs)
1854 else:
1855 result = self._cython_agg_general(
1856 "mean",
-> 1857 alt=lambda x: Series(x).mean(numeric_only=numeric_only),
1858 numeric_only=numeric_only,
1859 )
1860 return result.__finalize__(self.obj, method="groupby")
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\generic.py:11556, in NDFrame._add_numeric_operations.<locals>.mean(self, axis, skipna, numeric_only, **kwargs)
11539 @doc(
11540 _num_doc,
11541 desc="Return the mean of the values over the requested axis.",
(...)
11554 **kwargs,
11555 ):
> 11556 return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\generic.py:11201, in NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
11194 def mean(
11195 self,
11196 axis: Axis | None = 0,
(...)
11199 **kwargs,
11200 ) -> Series | float:
> 11201 return self._stat_function(
11202 "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
11203 )
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\generic.py:11158, in NDFrame._stat_function(self, name, func, axis, skipna, numeric_only, **kwargs)
11154 nv.validate_stat_func((), kwargs, fname=name)
11156 validate_bool_kwarg(skipna, "skipna", none_allowed=False)
> 11158 return self._reduce(
11159 func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
11160 )
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\series.py:4670, in Series._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
4665 raise TypeError(
4666 f"Series.{name} does not allow {kwd_name}={numeric_only} "
4667 "with non-numeric dtypes."
4668 )
4669 with np.errstate(all="ignore"):
-> 4670 return op(delegate, skipna=skipna, **kwds)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\nanops.py:96, in disallow.__call__.<locals>._f(*args, **kwargs)
94 try:
95 with np.errstate(invalid="ignore"):
---> 96 return f(*args, **kwargs)
97 except ValueError as e:
98 # we want to transform an object array
99 # ValueError message to the more typical TypeError
100 # e.g. this is normally a disallowed function on
101 # object arrays that contain strings
102 if is_object_dtype(args[0]):
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\nanops.py:158, in bottleneck_switch.__call__.<locals>.f(values, axis, skipna, **kwds)
156 result = alt(values, axis=axis, skipna=skipna, **kwds)
157 else:
--> 158 result = alt(values, axis=axis, skipna=skipna, **kwds)
160 return result
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\nanops.py:421, in _datetimelike_compat.<locals>.new_func(values, axis, skipna, mask, **kwargs)
418 if datetimelike and mask is None:
419 mask = isna(values)
--> 421 result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
423 if datetimelike:
424 result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\nanops.py:727, in nanmean(values, axis, skipna, mask)
724 dtype_count = dtype
726 count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
--> 727 the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
729 if axis is not None and getattr(the_sum, "ndim", False):
730 count = cast(np.ndarray, count)
File ~\miniconda3\envs\ds_study\lib\site-packages\pandas\core\nanops.py:1699, in _ensure_numeric(x)
1696 x = complex(x)
1697 except ValueError as err:
1698 # e.g. "foo"
-> 1699 raise TypeError(f"Could not convert {x} to numeric") from err
1700 return x
TypeError: Could not convert CPU to numeric원인: Pandas 업데이트 후부터는 피벗하려는 컬럼을 value 인자로 전달해주어야함.
즉, df.pivot_table(index="Name", values=["Quantity", "Price"]
자믿노가