Pandas DataFrame 按分类列排序，但按特定类排序

我想通过使用基于特定列的条目来选择 Pandas 数据框中的顶部条目df_selected = df_targets.head(N).

每个条目都有一个target值（按重要性排序）：

Likely Supporter, GOTV, Persuasion, Persuasion+GOTV  

不幸的是如果我这样做

df_targets = df_targets.sort("target")

排序将按字母顺序（GOTV,Likely Supporter, ...).

我希望有一个像这样的关键字list_ordering如：

my_list = ["Likely Supporter", "GOTV", "Persuasion", "Persuasion+GOTV"] 
df_targets = df_targets.sort("target", list_ordering=my_list)

为了解决这个问题，我创建了一个字典：

dict_targets = OrderedDict()
dict_targets["Likely Supporter"] = "0 Likely Supporter"
dict_targets["GOTV"] = "1 GOTV"
dict_targets["Persuasion"] = "2 Persuasion"
dict_targets["Persuasion+GOTV"] = "3 Persuasion+GOTV"

，但这似乎是一种非Pythonic方法。

建议将不胜感激！

我想你需要Categorical带参数ordered=True然后排序sort_values效果很好：

检查文档Categorical:

有序分类可以根据类别的自定义顺序进行排序，并且可以有最小值和最大值。

import pandas as pd

df = pd.DataFrame({'a': ['GOTV', 'Persuasion', 'Likely Supporter', 
                         'GOTV', 'Persuasion', 'Persuasion+GOTV']})

df.a = pd.Categorical(df.a, 
                      categories=["Likely Supporter","GOTV","Persuasion","Persuasion+GOTV"],
                      ordered=True)

print (df)
                  a
0              GOTV
1        Persuasion
2  Likely Supporter
3              GOTV
4        Persuasion
5   Persuasion+GOTV

print (df.a)
0                GOTV
1          Persuasion
2    Likely Supporter
3                GOTV
4          Persuasion
5     Persuasion+GOTV
Name: a, dtype: category
Categories (4, object): [Likely Supporter < GOTV < Persuasion < Persuasion+GOTV]

df.sort_values('a', inplace=True)
print (df)
                  a
2  Likely Supporter
0              GOTV
3              GOTV
1        Persuasion
4        Persuasion
5   Persuasion+GOTV