我的目标很简单,但不确定是否可行。可重复的例子:
你能不能这样做:
raw_data = {'score': [1, 3, 4, 4, 1, 2, 2, 4, 4, 2],
'player': ['Miller', 'Jacobson', 'Ali', 'George', 'Cooze', 'Wilkinson', 'Lewis', 'Lewis', 'Lewis', 'Jacobson']}
df = pd.DataFrame(raw_data, columns = ['score', 'player'])
df
score player
0 1 Miller
1 3 Jacobson
2 4 Ali
3 4 George
4 1 Cooze
5 2 Wilkinson
6 2 Lewis
7 4 Lewis
8 4 Lewis
9 2 Jacobson
对此:
score col_1 col_2 col_3 col_4
score
1 2 Miller Cooze n/a n/a
2 3 Wilkinson Lewis Jacobson n/a
3 1 Jacobson n/a n/a n/a
4 4 Ali George Lewis Lewis
通过groupby
?
我可以做到这一点df.groupby(['score']).agg({'score': np.size})
,但无法解决如何使用列值创建新列。
答案 0 :(得分:6)
我可以用
复制你的输出选项1
g = df.groupby('score').player
g.size().to_frame('score').join(g.apply(list).apply(pd.Series).add_prefix('col_'))
score col_0 col_1 col_2 col_3
score
1 2 Miller Cooze NaN NaN
2 3 Wilkinson Lewis Jacobson NaN
3 1 Jacobson NaN NaN NaN
4 4 Ali George Lewis Lewis
选项2
d1 = df.groupby('score').agg({'score': 'size', 'player': lambda x: tuple(x)})
d1.join(pd.DataFrame(d1.pop('player').values.tolist()).add_prefix('col_'))
score col_0 col_1 col_2 col_3
score
1 2 Miller Cooze NaN NaN
2 3 Wilkinson Lewis Jacobson NaN
3 1 Jacobson NaN NaN NaN
4 4 Ali George Lewis Lewis