python - 执行时 Pandas Pivot_table 错误 -Type 错误

Question

我是 Pandas 的新手，正在测试和学习。从 Excel 导入的数据框有以下问题:- 数据框包含以下变量:

<class 'pandas.core.frame.DataFrame'>
Int64Index: 48062 entries, 0 to 48061
Data columns (total 11 columns):
Konskund_MEAB         48062  non-null values
Strukturordn          48062  non-null values
Antal_forsandelser    48062  non-null values
ProdID                48062  non-null values
Sort                  48062  non-null values
Storstad              48062  non-null values
Year                  48062  non-null values
snittvikt             48062  non-null values
Totsum                48062  non-null values
Prodsum               48062  non-null values
snittpris             48062  non-null values
dtypes: float64(9), object(2)

• 运行:

  np.average(df['snittpris'],weights=df['Antal_forsandelser'])

产生正确的结果

• 当我尝试使用以下命令运行数据透视表时:

  df_sum=pd.pivot_table(df,rows=['Konskund_MEAB','ProdID'],cols=['年'],
aggfunc=np.average(df ['snittpris'],weights=df['Antal_forsandelser']))

我收到以下错误消息。

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-90-9fd03896c806> in <module>()
----> 1 df_sum=pd.pivot_table(df,rows=['Konskund_MEAB','ProdID'],cols=['Year'],
aggfunc=np.average(df['snittpris'],weights=df['Antal_forsandelser']))

C:\Users\Bengtw\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\tools\pivot.pyc
in pivot_table(data, values, rows, cols, aggfunc, fill_value, margins, dropna)
    101 
    102     grouped = data.groupby(keys)
--> 103     agged = grouped.agg(aggfunc)
    104 
    105     table = agged

C:\Users\Bengtw\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\core\groupby.pyc
 in agg(self, func, *args, **kwargs)
342     @Appender(_agg_doc)
343     def agg(self, func, *args, **kwargs):
--> 344         return self.aggregate(func, *args, **kwargs)
345 
346     def _iterate_slices(self):

C:\Users\Bengtw\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\core\groupby.pyc
 in aggregate(self, arg, *args, **kwargs)
   1741 
   1742             if self.grouper.nkeys > 1:
-> 1743                 return self._python_agg_general(arg, *args, **kwargs)
   1744             else:
   1745                 result = self._aggregate_generic(arg, *args, **kwargs)

C:\Users\Bengtw\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\core\groupby.pyc
 in _python_agg_general(self, func, *args, **kwargs)
    480 
    481         if len(output) == 0:
--> 482             return self._python_apply_general(f)
    483 
    484         if self.grouper._filter_empty_groups:

C:\Users\Bengtw\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\core\groupby.pyc
 in _python_apply_general(self, f)
    332 
    333     def _python_apply_general(self, f):
--> 334         keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
    335 
    336         return self._wrap_applied_output(keys, values,

C:\Users\Bengtw\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\core\groupby.pyc
 in apply(self, f, data, axis, keep_internal)
    628             # group might be modified
    629             group_axes = _get_axes(group)
--> 630             res = f(group)
    631             if not _is_indexed_like(res, group_axes):
    632                 mutated = True

C:\Users\Bengtw\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\core\groupby.pyc
 in <lambda>(x)
    468     def _python_agg_general(self, func, *args, **kwargs):
    469         func = _intercept_function(func)
--> 470         f = lambda x: func(x, *args, **kwargs)
    471 
    472         # iterate through "columns" ex exclusions to populate output dict

TypeError: 'numpy.float64' object is not callable

问题是什么？？行变量 Konskund_MEAB 包含字符串(几百个不同的字符串)，ProdID 是数字并且有 4 个唯一值。年份是什么(4 个离散值)。

Answer 1

参数 aggfunc 应该是一个函数，但你传入的是一个 float 。
因此类型错误:

TypeError: 'numpy.float64' object is not callable

您可以传入一个匿名 (lambda) 函数，这可能就是您想要的:

aggfunc=lambda x: np.average(x['snittpris'], weights=x['Antal_forsandelser'])

不幸的是，这在这种情况下不起作用(因为 aggfunc 无法访问未使用的列)...

相反，您可以使用 groupby :

rows = ['Konskund_MEAB','ProdID']
cols = ['Year']
g = df.groupby(rows + columns)

并将函数应用到每个组，然后是unstack从 Series 到 DataFrame:

s_av = g.apply(lambda x: np.average(x['snittpris'], weights=x['Antal_forsandelser']))
df_av = s_av.unstack(cols)