我有一个包含 names = ['id','t','metric_1','metric_2','metric_3'] 的数据框。我在 groupby('id') 中的每个 grp 上运行一些信号处理。我需要反转另一个进程的时间，该进程正在接收整个数据帧并在后台进行处理。简单地说，给定一个 grp，我只需要反转时间列，保持所有其他列完好无损，所有不在 grp 中的行完好无损。
输入数据框:
id t metric_1 metric_2 metric_3
0 0 86 13.333 61.989 0.017444
1 0 87 13.333 61.993 0.017569
2 0 88 13.333 61.992 0.017711
3 0 89 13.333 61.998 0.017746
4 0 90 13.333 61.993 0.017871
5 1 32 13.333 61.964 0.018511
6 1 33 20.000 61.913 0.020058
7 1 34 20.000 61.864 0.022475
8 1 35 26.667 61.802 0.025995
9 1 36 33.123 61.563 0.032345
10 1 37 33.763 61.836 0.060189
11 2 2 13.333 61.964 0.018511
12 2 3 20.000 61.613 0.020058
13 2 4 20.000 61.164 0.027475
14 2 5 26.667 61.802 0.024995
15 2 6 33.333 61.736 0.030689
我想使用一个产生像这样的数据帧的操作:
id t metric_1 metric_2 metric_3
0 0 90 13.333 61.989 0.017444
1 0 89 13.333 61.993 0.017569
2 0 88 13.333 61.992 0.017711
3 0 87 13.333 61.998 0.017746
4 0 86 13.333 61.993 0.017871
5 1 37 13.333 61.964 0.018511
6 1 36 20.000 61.913 0.020058
7 1 35 20.000 61.864 0.022475
8 1 34 26.667 61.802 0.025995
9 1 33 33.333 61.736 0.030689
10 1 32 33.763 61.836 0.060189
11 2 6 13.333 61.964 0.018511
12 2 5 20.000 61.613 0.020058
13 2 4 20.000 61.164 0.027475
14 2 3 26.667 61.802 0.024995
15 2 2 33.333 61.736 0.030689
最佳答案
更新 2: 对 t 列中的值进行排序/替换，但仅限于 id == 0 ( as described in this comment ) :
In [373]: df
Out[373]:
id t metric_1 metric_2 metric_3
0 0 86 13.333 61.989 0.017444
1 0 87 13.333 61.993 0.017569
2 0 88 13.333 61.992 0.017711
3 0 89 13.333 61.998 0.017746
4 0 90 13.333 61.993 0.017871
5 1 86 13.333 61.964 0.018511
6 1 87 20.000 61.913 0.020058
7 1 88 20.000 61.864 0.022475
8 1 89 26.667 61.802 0.025995
9 1 90 33.333 61.736 0.030689
In [374]: df.loc[df.id == 0, 't'] = df.loc[df.id == 0, 't'].sort_values(ascending=0).values
In [375]: df
Out[375]:
id t metric_1 metric_2 metric_3
0 0 90 13.333 61.989 0.017444
1 0 89 13.333 61.993 0.017569
2 0 88 13.333 61.992 0.017711
3 0 87 13.333 61.998 0.017746
4 0 86 13.333 61.993 0.017871
5 1 86 13.333 61.964 0.018511
6 1 87 20.000 61.913 0.020058
7 1 88 20.000 61.864 0.022475
8 1 89 26.667 61.802 0.025995
9 1 90 33.333 61.736 0.030689
UPDATE:更新数据集
原始DF:
In [363]: df
Out[363]:
id t metric_1 metric_2 metric_3
0 0 86 13.333 61.989 0.017444
1 0 87 13.333 61.993 0.017569
2 0 88 13.333 61.992 0.017711
3 0 89 13.333 61.998 0.017746
4 0 90 13.333 61.993 0.017871
5 1 86 13.333 61.964 0.018511
6 1 87 20.000 61.913 0.020058
7 1 88 20.000 61.864 0.022475
8 1 89 26.667 61.802 0.025995
9 1 90 33.333 61.736 0.030689
排序完整的行:
In [364]: df.sort_values(['id','t'], ascending=[1,0])
Out[364]:
id t metric_1 metric_2 metric_3
4 0 90 13.333 61.993 0.017871
3 0 89 13.333 61.998 0.017746
2 0 88 13.333 61.992 0.017711
1 0 87 13.333 61.993 0.017569
0 0 86 13.333 61.989 0.017444
9 1 90 33.333 61.736 0.030689
8 1 89 26.667 61.802 0.025995
7 1 88 20.000 61.864 0.022475
6 1 87 20.000 61.913 0.020058
5 1 86 13.333 61.964 0.018511 # <--
对两列的值进行排序 (['id',''])，替换它们的值:
In [366]: df[['id','t']] = df[['id','t']].sort_values(['id','t'], ascending=[1,0]).values
In [367]: df
Out[367]:
id t metric_1 metric_2 metric_3
0 0 90 13.333 61.989 0.017444
1 0 89 13.333 61.993 0.017569
2 0 88 13.333 61.992 0.017711
3 0 87 13.333 61.998 0.017746
4 0 86 13.333 61.993 0.017871
5 1 90 13.333 61.964 0.018511
6 1 89 20.000 61.913 0.020058
7 1 88 20.000 61.864 0.022475
8 1 87 26.667 61.802 0.025995
9 1 86 33.333 61.736 0.030689 # <--
旧答案:
IIUC 您可以简单地按两列对数据进行排序:
In [349]: df.sort_values(['id','t'], ascending=[1,1])
Out[349]:
id t metric_1 metric_2 metric_3
4 0 86 13.333 61.993 0.017871
3 0 87 13.333 61.998 0.017746
2 0 88 13.333 61.992 0.017711
1 0 89 13.333 61.993 0.017569
0 0 90 13.333 61.989 0.017444
9 1 86 33.333 61.736 0.030689
8 1 87 26.667 61.802 0.025995
7 1 88 20.000 61.864 0.022475
6 1 89 20.000 61.913 0.020058
5 1 90 13.333 61.964 0.018511
如果您想按照所需的数据集对其进行排序(替换 t 列值):
In [357]: df[['id','t']] = df[['id','t']].sort_values(['id','t']).values
In [358]: df
Out[358]:
id t metric_1 metric_2 metric_3
0 0 86 13.333 61.989 0.017444
1 0 87 13.333 61.993 0.017569
2 0 88 13.333 61.992 0.017711
3 0 89 13.333 61.998 0.017746
4 0 90 13.333 61.993 0.017871
5 1 86 13.333 61.964 0.018511
6 1 87 20.000 61.913 0.020058
7 1 88 20.000 61.864 0.022475
8 1 89 26.667 61.802 0.025995
9 1 90 33.333 61.736 0.030689 # 1 90 33.333 61.736 0.030689 as in your desired DF
关于python - Pandas dataframe 使用 groupby 对子集进行反向排序，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/41474500/