python - Pandas DataFrame 按分类列排序，但按特定类排序

Question

我想使用 df_selected = df_targets.head(N) 根据特定列的条目选择 Pandas 数据框中的顶部条目。

每个条目都有一个目标值(按重要性顺序):

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、可能的支持者、...)。

我希望有一个像 list_ordering 这样的关键字:

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的方法。

建议将不胜感激!

Answer 1

我认为你需要Categorical使用参数 ordered=True 然后按 sort_values 排序效果非常好:

检查 Categorical 的文档:

Ordered Categoricals can be sorted according to the custom order of the categories and can have a min and max value.

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