Python 熊猫:在每组中找到N个最大值,然后创建N列
我想从每组中找到Python 熊猫:在每组中找到N个最大值,然后创建N列,python,pandas,Python,Pandas,我想从每组中找到N最大值,然后用ITEM和VAL创建N列 df = pd.DataFrame() df['DATE'] = ['2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-02', '2018-01-02', '2018-01-02', '2018-01-02'] df['ITEM'] = ['A', 'B', 'C', 'D', 'A', 'B', 'C', 'E'] df['VA
N
最大值,然后用ITEM
和VAL
创建N
列
df = pd.DataFrame()
df['DATE'] = ['2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01',
'2018-01-02', '2018-01-02', '2018-01-02', '2018-01-02']
df['ITEM'] = ['A', 'B', 'C', 'D', 'A', 'B', 'C', 'E']
df['VAL'] = [1, 4, 5, 3, 5, 4, 4, 6]
df
DATE ITEM VAL
0 2018-01-01 A 1
1 2018-01-01 B 4
2 2018-01-01 C 5
3 2018-01-01 D 3
4 2018-01-02 A 5
5 2018-01-02 B 4
6 2018-01-02 C 4
7 2018-01-02 E 6
我尝试了下面的代码,我被困在这里了。我找不到一个有效的方法来获得我的预期产出。有什么想法吗
N = 3
df.groupby(['DATE']).apply(lambda x: x.set_index('ITEM').VAL.nlargest(N)).unstack()
ITEM A B C D E
DATE
2018-01-01 NaN 4.0 5.0 3.0 NaN
2018-01-02 5.0 4.0 NaN NaN 6.0
预期产出:
DATE TOP_1 VAL_1 TOP_2 VAL_2 TOP_3 VAL_3
0 2018-01-01 C 5 B 4 D 3
1 2019-01-02 E 6 A 5 B 4
用于计数器列,通过重塑形状,并用于展平多索引
使用列表理解与f-string
s:
df1 = df.groupby(['DATE']).apply(lambda x: x.set_index('ITEM').VAL.nlargest(N)).reset_index()
或:
df1 = df.sort_values(['DATE','VAL'], ascending=[True, False]).groupby('DATE').head(N)
g = df1.groupby('DATE').cumcount().add(1)
df1 = df1.set_index(['DATE',g]).unstack().sort_index(level=1, axis=1)
df1.columns = [f'{x}_{y}' for x, y in df1.columns]
df1 = df1.reset_index()
print (df1)
DATE ITEM_1 VAL_1 ITEM_2 VAL_2 ITEM_3 VAL_3
0 2018-01-01 C 5 B 4 D 3
1 2018-01-02 E 6 A 5 B 4