具有多索引列的 Pandas 数据框 - 合并级别 [英] Pandas dataframe with multiindex column - merge levels
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问题描述
我有一个数据框,grouped
,多索引列如下:
I have a dataframe, grouped
, with multiindex columns as below:
import pandas as pd
codes = ["one","two","three"];
colours = ["black", "white"];
textures = ["soft", "hard"];
N= 100 # length of the dataframe
df = pd.DataFrame({ 'id' : range(1,N+1),
'weeks_elapsed' : [random.choice(range(1,25)) for i in range(1,N+1)],
'code' : [random.choice(codes) for i in range(1,N+1)],
'colour': [random.choice(colours) for i in range(1,N+1)],
'texture': [random.choice(textures) for i in range(1,N+1)],
'size': [random.randint(1,100) for i in range(1,N+1)],
'scaled_size': [random.randint(100,1000) for i in range(1,N+1)]
}, columns= ['id', 'weeks_elapsed', 'code','colour', 'texture', 'size', 'scaled_size'])
grouped = df.groupby(['code', 'colour']).agg( {'size': [np.sum, np.average, np.size, pd.Series.idxmax],'scaled_size': [np.sum, np.average, np.size, pd.Series.idxmax]}).reset_index()
>> grouped
code colour size scaled_size
sum average size idxmax sum average size idxmax
0 one black 1031 60.647059 17 81 185.153944 10.891408 17 47
1 one white 481 37.000000 13 53 204.139249 15.703019 13 53
2 three black 822 48.352941 17 6 123.269405 7.251141 17 31
3 three white 1614 57.642857 28 50 285.638337 10.201369 28 37
4 two black 523 58.111111 9 85 80.908912 8.989879 9 88
5 two white 669 41.812500 16 78 82.098870 5.131179 16 78
[6 rows x 10 columns]
如何将列索引级别展平/合并为:Level1|Level2",例如size|sum
,scaled_size|sum
.等等?如果这是不可能的,有没有办法像我上面所做的那样 groupby()
而不创建多索引列?
How can I flatten/merge the column index levels as: "Level1|Level2", e.g. size|sum
, scaled_size|sum
. etc? If this is not possible, is there a way to groupby()
as I did above without creating multi-index columns?
推荐答案
您可以随时更改列:
grouped.columns = ['%s%s' % (a, '|%s' % b if b else '') for a, b in grouped.columns]
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