[In]：

pd.set_option('display.max_colwidth', 200)
topic_stats_df = corpus_topic_df.groupby('Dominant Topic').agg({
                                                'Dominant Topic': {
                                                    'Doc Count': np.size,
                                                    '% Total Docs': np.size }
                                              })
topic_stats_df = topic_stats_df['Dominant Topic'].reset_index()
topic_stats_df['% Total Docs'] = topic_stats_df['% Total Docs'].apply(lambda row: round((row*100) / len(papers), 2))
topic_stats_df['Topic Desc'] = [topics_df.iloc[t]['Terms per Topic'] for t in range(len(topic_stats_df))]
topic_stats_df

[Out]：

---------------------------------------------------------------------------
SpecificationError                        Traceback (most recent call last)
Cell In[47], line 2
      1 pd.set_option('display.max_colwidth', 200)
----> 2 topic_stats_df = corpus_topic_df.groupby('Dominant Topic').agg({
      3                                                 'Dominant Topic': {
      4                                                     'Doc Count': np.size,
      5                                                     '% Total Docs': np.size }
      6                                               })
      7 topic_stats_df = topic_stats_df['Dominant Topic'].reset_index()
      8 topic_stats_df['% Total Docs'] = topic_stats_df['% Total Docs'].apply(lambda row: round((row*100) / len(papers), 2))

File ~/miniconda3/envs/nlp/lib/python3.8/site-packages/pandas/core/groupby/generic.py:894, in DataFrameGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
    891 func = maybe_mangle_lambdas(func)
    893 op = GroupByApply(self, func, args, kwargs)
--> 894 result = op.agg()
    895 if not is_dict_like(func) and result is not None:
    896     return result

File ~/miniconda3/envs/nlp/lib/python3.8/site-packages/pandas/core/apply.py:169, in Apply.agg(self)
    166     return self.apply_str()
    168 if is_dict_like(arg):
--> 169     return self.agg_dict_like()
    170 elif is_list_like(arg):
    171     # we require a list, but not a 'str'
    172     return self.agg_list_like()

File ~/miniconda3/envs/nlp/lib/python3.8/site-packages/pandas/core/apply.py:478, in Apply.agg_dict_like(self)
    475     selected_obj = obj._selected_obj
    476     selection = obj._selection
--> 478 arg = self.normalize_dictlike_arg("agg", selected_obj, arg)
    480 if selected_obj.ndim == 1:
    481     # key only used for output
    482     colg = obj._gotitem(selection, ndim=1)

File ~/miniconda3/envs/nlp/lib/python3.8/site-packages/pandas/core/apply.py:594, in Apply.normalize_dictlike_arg(self, how, obj, func)
    587 # Can't use func.values(); wouldn't work for a Series
    588 if (
    589     how == "agg"
    590     and isinstance(obj, ABCSeries)
    591     and any(is_list_like(v) for _, v in func.items())
    592 ) or (any(is_dict_like(v) for _, v in func.items())):
    593     # GH 15931 - deprecation of renaming keys
--> 594     raise SpecificationError("nested renamer is not supported")
    596 if obj.ndim != 1:
    597     # Check for missing columns on a frame
    598     cols = set(func.keys()) - set(obj.columns)

SpecificationError: nested renamer is not supported

这段代码的作者是Sarkar，D.（2019）. * 使用Python进行文本分析 * Apress，Topic modeling section.
小Pandas0.25.3失败，因为我在一个m1的Mac.

• 试过了：pip安装Pandas==0.25.3
• 已经试过了：arch-x86_64 pip安装Pandas==0.25.3