使用熔体后类别dtype发生变化 [英] Categorical dtype changes after using melt

问题描述

在回答这个问题时，我发现在熊猫数据框上使用melt之后，以前是有序的分类dtype成为object.这是预期的行为吗?

In answering this question, I found that after using melt on a pandas dataframe, a column that was previously an ordered Categorical dtype becomes an object. Is this intended behaviour?

注意:不是在寻找解决方案，只是想知道是否有任何原因导致此行为，或者这不是预期的行为.

Note: not looking for a solution, just wondering if there is any reason for this behaviour or if it's not intended behavior.

示例:

使用以下数据框df:

  Cat  L_1  L_2  L_3
0   A    1    2    3
1   B    4    5    6
2   C    7    8    9

df['Cat'] = pd.Categorical(df['Cat'], categories = ['C','A','B'], ordered=True)

# As you can see `Cat` is a category
>>> df.dtypes
Cat    category
L_1       int64
L_2       int64
L_3       int64
dtype: object

melted = df.melt('Cat')

>>> melted
  Cat variable  value
0   A      L_1      1
1   B      L_1      4
2   C      L_1      7
3   A      L_2      2
4   B      L_2      5
5   C      L_2      8
6   A      L_3      3
7   B      L_3      6
8   C      L_3      9

现在，如果我查看Cat，它已成为对象:

Now, if I look at Cat, it's become an object:

>>> melted.dtypes
Cat         object
variable    object
value        int64
dtype: object

这是故意的吗?

推荐答案

在

In source code . 0.22.0(My old version)

 for col in id_vars:
        mdata[col] = np.tile(frame.pop(col).values, K)
     mcolumns = id_vars + var_name + [value_name]

将使用np.tile返回数据类型对象.

Which will return the datatype object with np.tile.

它已在0.23.4中修复(我更新pandas之后)

It has been fixed in 0.23.4(After I update my pandas)

df.melt('Cat')
Out[6]: 
  Cat variable  value
0   A      L_1      1
1   B      L_1      4
2   C      L_1      7
3   A      L_2      2
4   B      L_2      5
5   C      L_2      8
6   A      L_3      3
7   B      L_3      6
8   C      L_3      9
df.melt('Cat').dtypes
Out[7]: 
Cat         category
variable      object
value          int64
dtype: object

更多信息如何修复:

for col in id_vars:
    id_data = frame.pop(col)
    if is_extension_type(id_data): # here will return True , then become concat not np.tile
        id_data = concat([id_data] * K, ignore_index=True)
    else:
        id_data = np.tile(id_data.values, K)
    mdata[col] = id_data

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