我正在尝试 reshape 我的数据。我有战斗和一排包含在给定战斗中战斗的每个战士的数据。我想从每次战斗中获取对手的第二行，并将该行中的值转换为列。我已经成功地将我的初始数据集从长数据集转向宽数据集，但在这最后一步中遇到了困难。
这是我的数据示例:
{'event_id': {0: '417', 1: '417', 2: '56', 3: '56'},
'fighter': {0: 'PRICE', 1: 'RABOTTE', 2: 'PRICE', 3: 'WILDER'},
'punches_landed|Jabs': {0: 51, 1: 25, 2: 1, 3: 12},
'punches_landed|Power Punches': {0: 10, 1: 11, 2: 19, 3: 16},
'punches_landed|Total Punches': {0: 61, 1: 36, 2: 20, 3: 28},
'punches_thrown|Jabs': {0: 271, 1: 94, 2: 86, 3: 49},
'punches_thrown|Power Punches': {0: 29, 1: 47, 2: 41, 3: 23},
'punches_thrown|Total Punches': {0: 300, 1: 141, 2: 127, 3: 72}}
所需的输出类似于此
event_id fighter puncheslanded... punches_throwns... fighter2 puncheslanded2....punches_thrown2
417 PRICE ... ... RABOTTE
56 PRICE ... ... WILDER
这就是我到目前为止所做的
#this pivoted the original dataset
fight_stats = fight_stats.pivot_table(['punches_landed','punches_thrown'],['event_id','fighter'],'punch_stat').reset_index()
最佳答案
您需要通过GroupBy.cumcount计数器创建MultIindex ， reshape DataFrame.unstack最后通过 map 压平列中的 MultiIndex:
df = (df.set_index(['event_id',df.groupby('event_id').cumcount().add(1)])
.unstack()
.sort_index(axis=1, level=1))
df.columns = df.columns.map('{0[0]}{0[1]}'.format)
df = df.reset_index()
print (df)
event_id fighter1 punches_landed|Jabs1 punches_landed|Power Punches1 \
0 417 PRICE 51 10
1 56 PRICE 1 19
punches_landed|Total Punches1 punches_thrown|Jabs1 \
0 61 271
1 20 86
punches_thrown|Power Punches1 punches_thrown|Total Punches1 fighter2 \
0 29 300 RABOTTE
1 41 127 WILDER
punches_landed|Jabs2 punches_landed|Power Punches2 \
0 25 11
1 12 16
punches_landed|Total Punches2 punches_thrown|Jabs2 \
0 36 94
1 28 49
punches_thrown|Power Punches2 punches_thrown|Total Punches2
0 47 141
1 23 72
关于python - 有条件 reshape pandas，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/59209699/