问题
I have a pandas dataframe and I would like to add a column level to split specific columns (metric_a, metric_b, metric_c) into several subcolumns based on the value of another column (parameter).
Current data format:
participant param metric_a metric_b metric_c
0 alice a 0,700 0,912 0,341
1 alice b 0,736 0,230 0,370
2 bob a 0,886 0,364 0,995
3 bob b 0,510 0,704 0,990
4 charlie a 0,173 0,462 0,709
5 charlie b 0,085 0,950 0,807
6 david a 0,676 0,653 0,189
7 david b 0,823 0,524 0,430
Wanted data format:
participant metric_a metric_b metric_c
a b a b a b
0 alice 0,700 0,736 0,912 0,230 0,341 0,370
1 bob 0,886 0,510 0,364 0,704 0,995 0,990
2 charlie 0,173 0,085 0,462 0,950 0,709 0,807
3 david 0,676 0,823 0,653 0,524 0,189 0,430
I have tried
df.set_index(['participant', 'param']).unstack(['param'])
which gives me a close result but not satisfies me as I want to keep a single-level index and participant a regular column.
metric_a metric_b metric_c
param a b a b a b
participant
alice 0,700 0,736 0,912 0,230 0,341 0,370
bob 0,886 0,510 0,364 0,704 0,995 0,990
charlie 0,173 0,085 0,462 0,950 0,709 0,807
david 0,676 0,823 0,653 0,524 0,189 0,430
I have the intuition that groupby() or pivot_table() functions could do the job but cannot figure out how.
回答1:
IIUC, use DataFrame.set_index and unstack, and reset_index specifying col_level parameter:
df.set_index(['participant', 'param']).unstack('param').reset_index(col_level=0)
[out]
participant metric_a metric_b metric_c
param a b a b a b
0 alice 0,700 0,736 0,912 0,230 0,341 0,370
1 bob 0,886 0,510 0,364 0,704 0,995 0,990
2 charlie 0,173 0,085 0,462 0,950 0,709 0,807
3 david 0,676 NaN 0,653 NaN 0,189 NaN
4 heidi NaN 0,823 NaN 0,524 NaN 0,430
来源：https://stackoverflow.com/questions/55849519/pandas-reshape-dataframe-by-adding-a-column-level-based-on-the-value-of-another