1. 문제
판다스를 2.0.0 으로 업그레이드 하고 다음과 같은 오류가 발생 하였다.
df = pd.DataFrame([['a',1],['b',1]])df.mean()
df.mean()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/nanops.py:1680, in _ensure_numeric(x)
1679 try:
-> 1680 x = x.astype(np.complex128)
1681 except (TypeError, ValueError):
ValueError: complex() arg is a malformed string
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/nanops.py:1683, in _ensure_numeric(x)
1682 try:
-> 1683 x = x.astype(np.float64)
1684 except ValueError as err:
1685 # GH#29941 we get here with object arrays containing strs
ValueError: could not convert string to float: 'ab'
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
Cell In[7], line 1
----> 1 df.mean()
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/generic.py:11563, in NDFrame._add_numeric_operations.<locals>.mean(self, axis, skipna, numeric_only, **kwargs)
11546 @doc(
11547 _num_doc,
11548 desc="Return the mean of the values over the requested axis.",
(...)
11561 **kwargs,
11562 ):
> 11563 return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/generic.py:11208, in NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
11201 def mean(
11202 self,
11203 axis: Axis | None = 0,
(...)
11206 **kwargs,
11207 ) -> Series | float:
> 11208 return self._stat_function(
11209 "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
11210 )
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/generic.py:11165, in NDFrame._stat_function(self, name, func, axis, skipna, numeric_only, **kwargs)
11161 nv.validate_stat_func((), kwargs, fname=name)
11163 validate_bool_kwarg(skipna, "skipna", none_allowed=False)
> 11165 return self._reduce(
11166 func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
11167 )
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/frame.py:10519, in DataFrame._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
10515 df = df.T
10517 # After possibly _get_data and transposing, we are now in the
10518 # simple case where we can use BlockManager.reduce
> 10519 res = df._mgr.reduce(blk_func)
10520 out = df._constructor(res).iloc[0]
10521 if out_dtype is not None:
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/internals/managers.py:1534, in BlockManager.reduce(self, func)
1532 res_blocks: list[Block] = []
1533 for blk in self.blocks:
-> 1534 nbs = blk.reduce(func)
1535 res_blocks.extend(nbs)
1537 index = Index([None]) # placeholder
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/internals/blocks.py:339, in Block.reduce(self, func)
333 @final
334 def reduce(self, func) -> list[Block]:
335 # We will apply the function and reshape the result into a single-row
336 # Block with the same mgr_locs; squeezing will be done at a higher level
337 assert self.ndim == 2
--> 339 result = func(self.values)
341 if self.values.ndim == 1:
342 # TODO(EA2D): special case not needed with 2D EAs
343 res_values = np.array([[result]])
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/frame.py:10482, in DataFrame._reduce.<locals>.blk_func(values, axis)
10480 return values._reduce(name, skipna=skipna, **kwds)
10481 else:
> 10482 return op(values, axis=axis, skipna=skipna, **kwds)
File /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/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 /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/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 /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/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 /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/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 /opt/homebrew/Caskroom/miniconda/base/envs/test2/lib/python3.9/site-packages/pandas/core/nanops.py:1686, in _ensure_numeric(x)
1683 x = x.astype(np.float64)
1684 except ValueError as err:
1685 # GH#29941 we get here with object arrays containing strs
-> 1686 raise TypeError(f"Could not convert {x} to numeric") from err
1687 else:
1688 if not np.any(np.imag(x)):
TypeError: Could not convert ['ab'] to numeric2. 원인
2.0.0으로 업그레이드 되면서 numeric_only라는 인수의 Default 값이 None 에서 False로 바뀌어져 있다.
Signature:
df.mean(
axis: 'AxisInt | None' = 0,
skipna: 'bool_t' = True,
numeric_only: 'bool_t' = False,
**kwargs,
)3. 해결
numeric_only 인수를 True 로 주면 잘 실행된다.
df.mean(numeric_only=True)