Pandas DataFrame 按分类列排序，但按特定类排序
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/39223256/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas DataFrame sort by categorical column but by specific class ordering
提问by elzurdo
I would like to select the top entries in a Pandas dataframe base on the entries of a specific column by using df_selected = df_targets.head(N).
我想根据特定列的条目选择 Pandas 数据框中的顶部条目，方法是使用df_selected = df_targets.head(N).
Each entry has a targetvalue (by order of importance):
每个条目都有一个target值（按重要性排序）：
Likely Supporter, GOTV, Persuasion, Persuasion+GOTV
Unfortunately if I do
不幸的是，如果我这样做
df_targets = df_targets.sort("target")
the ordering will be alphabetical (GOTV,Likely Supporter, ...).
排序将按字母顺序 ( GOTV, Likely Supporter, ...)。
I was hoping for a keyword like list_orderingas in:
我希望有一个像这样的关键字list_ordering：
my_list = ["Likely Supporter", "GOTV", "Persuasion", "Persuasion+GOTV"]
df_targets = df_targets.sort("target", list_ordering=my_list)
To deal with this issue I create a dictionary:
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"
, but it seems like a non-pythonic approach.
，但这似乎是一种非 Pythonic 的方法。
Suggestions would be much appreciated!
建议将不胜感激！
回答by jezrael
I think you need Categoricalwith parameter ordered=Trueand then sorting by sort_valuesworks very nice:
我认为您需要Categorical使用参数ordered=True，然后按sort_values工作方式排序非常好：
If check documentation of Categorical:
如果检查以下文件Categorical：
Ordered Categoricalscan be sorted according to the custom order of the categories and can have a min and max value.
Ordered Categoricals可以根据类别的自定义顺序进行排序，并且可以具有最小值和最大值。
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
回答by elzurdo
The method shown in my previous answer is now deprecated.
我之前的答案中显示的方法现已弃用。
In stead it is best to use pandas.Categoricalas shown here.
相反，最好pandas.Categorical按照此处所示使用。
So:
所以：
list_ordering = ["Likely Supporter","GOTV","Persuasion","Persuasion+GOTV"]
df["target"] = pd.Categorical(df["target"], categories=list_ordering)
回答by elzurdo
Thanks to jerzrael's input and references,
感谢 jerzrael 的输入和参考，
I like this sliced solution:
我喜欢这个切片解决方案：
list_ordering = ["Likely Supporter","GOTV","Persuasion","Persuasion+GOTV"]
df["target"] = df["target"].astype("category", categories=list_ordering, ordered=True)
