问题描述
我有一个数据帧数据记录堆叠,其中同一主题每 3 个月左右有不同的测量。例如,Subj BAR02002 有 4 个不同的数据记录:
Subj months X Y Z
BAR02002 0 14 53 52
BAR02002 3 24 61 96
BAR02002 6 5 53 3
BAR02002 9 3 64 33
BAR02003 0 22 63 55
BAR02003 6 44 22 53
BAR02003 9 42 12 72
BAR02003 12 15 1 12
我试图让 BAR02002 只占一行而不是 4 行。我相信这个过程被称为从长到宽重塑数据(我可能是错的)。说明最终结果:
Subj X Y Z X1 Y2 Z3 X2 Y3 Z3 ...
BAR02002 14 53 52 24 61 96 5 53 3 ...
BAR02003 0 22 63 55 NA NA NA 44 22 ...
以下代码没有给出我想要的。有没有办法使用 pandas/python(甚至 R)转换数据?
df.pivot(index='Subj_FU',columns='Subj',values= ['Months','PM_N',...])
解决方法
将map
用于新列并将其用于参数columns
,最后展平MultiIndex
:
df['g'] = df['months'].map({0:0,3:1,6:2,9:3,12:4})
df1 = df.pivot_table(index='Subj',columns='g',values= ['X','Y','Z'],aggfunc='sum')
df1.columns = df1.columns.map(lambda x: f'{x[0]}{x[1]}')
print (df1)
X0 X1 X2 X3 X4 Y0 Y1 Y2 Y3 Y4 Z0 \
Subj
BAR02002 14.0 24.0 5.0 3.0 NaN 53.0 61.0 53.0 64.0 NaN 52.0
BAR02003 22.0 NaN 44.0 42.0 15.0 63.0 NaN 22.0 12.0 1.0 55.0
Z1 Z2 Z3 Z4
Subj
BAR02002 96.0 3.0 33.0 NaN
BAR02003 NaN 53.0 72.0 12.0
如果使用列 month
:
df1 = df.pivot_table(index='Subj',columns='months',aggfunc='sum')
df1.columns = df1.columns.map(lambda x: f'{x[0]}{x[1]}')
print (df1)
X0 X3 X6 X9 X12 Y0 Y3 Y6 Y9 Y12 Z0 \
Subj
BAR02002 14.0 24.0 5.0 3.0 NaN 53.0 61.0 53.0 64.0 NaN 52.0
BAR02003 22.0 NaN 44.0 42.0 15.0 63.0 NaN 22.0 12.0 1.0 55.0
Z3 Z6 Z9 Z12
Subj
BAR02002 96.0 3.0 33.0 NaN
BAR02003 NaN 53.0 72.0 12.0
或者使用Series.unstack
:
g = df['months'].map({0:0,12:4})
df1 = df.groupby(['Subj',g])[['X','Z']].sum().unstack()
df1.columns = df1.columns.map(lambda x: f'{x[0]}{x[1]}')
,
你可以简单地drop重复,它会保留第一项:
import pandas as pd
data = [ { "Subj": "BAR02002","months": 0,"X": 14,"Y": 53,"Z": 52 },{ "Subj": "BAR02002","months": 3,"X": 24,"Y": 61,"Z": 96 },"months": 6,"X": 5,"Z": 3 },"months": 9,"X": 3,"Y": 64,"Z": 33 },{ "Subj": "BAR02003","X": 22,"Y": 63,"Z": 55 },"X": 44,"Y": 22,"Z": 53 },"X": 42,"Y": 12,"Z": 72 },"months": 12,"X": 15,"Y": 1,"Z": 12 } ]
df = pd.DataFrame(data)
结果:
Subj | 月 | X | Y | Z | |
---|---|---|---|---|---|
0 | BAR02002 | 0 | 14 | 53 | 52 |
4 | BAR02003 | 0 | 22 | 63 | 55 |