SpecificationError 的解决方案：在 agg() 和 groupby() 时不支持嵌套重命名器

Question

def stack_plot(data, xtick, col2='project_is_approved', col3='total'):
    ind = np.arange(data.shape[0])

    plt.figure(figsize=(20,5))
    p1 = plt.bar(ind, data[col3].values)
    p2 = plt.bar(ind, data[col2].values)

    plt.ylabel('Projects')
    plt.title('Number of projects aproved vs rejected')
    plt.xticks(ind, list(data[xtick].values))
    plt.legend((p1[0], p2[0]), ('total', 'accepted'))
    plt.show()

def univariate_barplots(data, col1, col2='project_is_approved', top=False):
    # Count number of zeros in dataframe python: /sf/answers/3607836501/
    temp = pd.DataFrame(project_data.groupby(col1)[col2].agg(lambda x: x.eq(1).sum())).reset_index()

    # Pandas dataframe grouby count: /sf/answers/1356991401/
    temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']

    temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']

    temp.sort_values(by=['total'],inplace=True, ascending=False)

    if top:
        temp = temp[0:top]

    stack_plot(temp, xtick=col1, col2=col2, col3='total')
    print(temp.head(5))
    print("="*50)
    print(temp.tail(5))

univariate_barplots(project_data, 'school_state', 'project_is_approved', False)

错误：

SpecificationError                        Traceback (most recent call last)
<ipython-input-21-2cace8f16608> in <module>()
----> 1 univariate_barplots(project_data, 'school_state', 'project_is_approved', False)

<ipython-input-20-856fcc83737b> in univariate_barplots(data, col1, col2, top)
      4 
      5     # Pandas dataframe grouby count: /sf/answers/1356991401/
----> 6     temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']
      7     print (temp['total'].head(2))
      8     temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
    251             # but not the class list / tuple itself.
    252             func = _maybe_mangle_lambdas(func)
--> 253             ret = self._aggregate_multiple_funcs(func)
    254             if relabeling:
    255                 ret.columns = columns

~\AppData\Roaming\Python\Python36\site-packages\pandas\core\groupby\generic.py in _aggregate_multiple_funcs(self, arg)
    292             # GH 15931
    293             if isinstance(self._selected_obj, Series):
--> 294                 raise SpecificationError("nested renamer is not supported")
    295 
    296             columns = list(arg.keys())

SpecificationError: **nested renamer is not supported**

Answer 1

改变

temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']

temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']

到

temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg(total='count')).reset_index()['total']
temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg(Avg='mean')).reset_index()['Avg']

原因：在新的 Pandas 版本中，命名聚合是已弃用的“dict-of-dicts”方法的推荐替代品，用于命名列特定聚合的输出（重命名时使用字典弃用 groupby.agg()）。

来源：https : //pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html

Answer 2

如果数据框中不存在聚合函数 dict 中指定的列，也会发生此错误：

In [190]: group = pd.DataFrame([[1, 2]], columns=['A', 'B']).groupby('A')
In [195]: group.agg({'B': 'mean'})
Out[195]: 
   B
A   
1  2

In [196]: group.agg({'B': 'mean', 'non-existing-column': 'mean'})
...
SpecificationError: nested renamer is not supported

Answer 3

我找到了方法：而不是像

g2 = df.groupby(["Description","CustomerID"],as_index=False).agg({'Quantity':{"maxQ":np.max,"minQ":np.min,"meanQ":np.mean}})
g2.columns = ["Description","CustomerID","maxQ","minQ",'meanQ']

执行如下操作：

g2 = df.groupby(["Description","CustomerID"],as_index=False).agg({'Quantity':{np.max,np.min,np.mean}})
g2.columns = ["Description","CustomerID","maxQ","minQ",'meanQ']

我有同样的错误，这就是我解决它的方法！