熊猫群不处理agg函数中的分类列
熊猫群不处理agg函数中的分类列
提问于 2022-06-08 20:00:09
我有一个有3列的Dataframe，两个是类别数据，一个是float16。当我执行groupby并在agg中运行lambda特定函数以根据dtype对每一列进行不同处理时，分类列上会有一个下降。
如果这样做的话，它就会奏效。
i=pd.DataFrame({"A":["a","a","a","b","c","c"],"B":[1,2,3,4,5,6],"C":[ "NaN" ,"b","NaN","b","c","c"]})
i['A'] = i['A'].astype('category')
i['B'] = i['B'].astype('float16')
i.groupby("A", as_index=False)[["B","C"]].agg(lambda x: x.mean() if np.dtype(x)=='float16' else x.value_counts().index[0])输出，这是我想要得到的是：
A B C
0 a 2.0 NaN
1 b 4.0 b
2 c 5.5 c但是，每当我声明C列为绝对列时，python就会自动删除列C。
i=pd.DataFrame({"A":["a","a","a","b","c","c"],"B":[1,2,3,4,5,6],"C":[ "NaN" ,"b","NaN","b","c","c"]})
i['A'] = i['A'].astype('category')
i['B'] = i['B'].astype('float16')
i['C'] = i['C'].astype('category')
i.groupby("A", as_index=False)[["B","C"]].agg(lambda x: x.mean() if np.dtype(x)=='float16' else x.value_counts().index[0])答案如下：
['C'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.
A B
0 a 2.0
1 b 4.0
2 c 5.5是否有人知道groupby中的agg不能处理分类列？
Stack Overflow用户
发布于 2022-06-08 23:47:58
请注意，您没有正确检索类型：
i.groupby("A", as_index=False)[["B","C"]].agg(lambda x: print(np.dtype(x)))给出“无”，而使用x.dtype=='float16'，因为x是pd.Series。您可以向.agg(lambda x: print(type(x)))查询
i['A'] = i['A'].astype('category')
i['B'] = i['B'].astype('float16')
i['C'] = i['C'].astype('category')
i.groupby("A", as_index=False)[["B","C"]].agg(lambda x: x.mean() if x.dtype=='float16' else x.value_counts().index[0])给予：
A B C
0 a 2.0 NaN
1 b 4.0 b
2 c 5.5 c页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/72551544
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