我正在尝试折叠多索引表，以便折叠的值显示在新列中。有人可以提供一些帮助吗？非常感谢。
import pandas
df = pd.DataFrame({'product_id' : [1, 1, 1, 2, 2, 2, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3],
'product_name' : ['soccer ball', 'soccer ball', 'soccer ball', 'basketball', 'basketball', 'basketball', 'football', 'football', 'football', 'soccer ball', 'soccer ball', 'soccer ball', 'basketball', 'basketball', 'basketball', 'football', 'football', 'football'],
'color' : ['black', 'black', 'black', 'red', 'red', 'red', 'red', 'red', 'red', 'black', 'black', 'black', 'red', 'red', 'red', 'red', 'red', 'red'],
'brand' : ['sadida', 'sadida', 'sadida', 'gnidlaps', 'gnidlaps', 'gnidlaps', 'nosliw', 'nosliw', 'nosliw', 'sadida', 'sadida', 'sadida', 'gnidlaps', 'gnidlaps', 'gnidlaps', 'nosliw', 'nosliw', 'nosliw'],
'manufacturing_cost' : [0.004893964, 0.004893964, 0.004893964, 6.287116543, 6.287116543, 6.287116543, 16.19975422, 16.19975422, 16.19975422, 0.006019576, 0.006019576, 0.006019576, 7.733153347, 7.733153347, 7.733153347, 19.92569769, 19.92569769, 19.92569769],
'shipping_cost' : [0.026574225, 0.026574225, 0.026574225, 34.13904283, 34.13904283, 34.13904283, 87.9646654, 87.9646654, 87.9646654, 0.032686297, 0.032686297, 0.032686297, 41.99102268, 41.99102268, 41.99102268, 108.1965384, 108.1965384, 108.1965384],
'units' : ['dollars', 'dollars', 'dollars', 'cents', 'cents', 'cents', 'cents', 'cents', 'cents', 'dollars', 'dollars', 'dollars', 'cents', 'cents', 'cents', 'cents', 'cents', 'cents'],
'seasonal' : [False, False, False, True, True, True, True, True, True, False, False, False, True, True, True, True, True, True],
'market' : ['West', 'Southwest', 'East', 'East', 'South', 'Northeast', 'Northeast', 'South', 'East', 'West', 'Southwest', 'East', 'East', 'South', 'Northeast', 'Northeast', 'South', 'East'],
'high_season' : ['summer', 'summer', 'summer', 'spring', 'spring', 'spring', 'fall', 'fall', 'fall', 'summer', 'summer', 'summer', 'spring', 'spring', 'spring', 'fall', 'fall', 'fall'],
'relationship' : ['exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive', 'exclusive'],
'shipper' : ['Acme', 'Acme', 'Acme', 'Acme', 'Acme', 'Acme', 'Acme', 'Acme', 'Acme', 'Emca', 'Emca', 'Emca', 'Emca', 'Emca', 'Emca', 'Emca', 'Emca', 'Emca'],
'sales' : [0.58, 0.23, 0.23, 0.1, 0.04, 0.02, 0.06, 0.15, 0.03, 0.71, 0.28, 0.28, 0.13, 0.05, 0.03, 0.08, 0.19, 0.04],
'alert' : [True, False, True, False, True, False, True, False, True, True, False, True, False, True, False, True, False, True]})
+------------+--------------+-------+----------+--------------------+---------------+---------+----------+-----------+-------------+--------------+---------+-------+-------+ | product_id | product_name | color | brand | manufacturing_cost | shipping_cost | units | seasonal | market | high_season | relationship | shipper | sales | alert | +------------+--------------+-------+----------+--------------------+---------------+---------+----------+-----------+-------------+--------------+---------+-------+-------+ | 1 | soccer ball | black | sadida | 0.004893964 | 0.026574225 | dollars | FALSE | West | summer | exclusive | Acme | 0.58 | TRUE | | 1 | soccer ball | black | sadida | 0.004893964 | 0.026574225 | dollars | FALSE | Southwest | summer | exclusive | Acme | 0.23 | FALSE | | 1 | soccer ball | black | sadida | 0.004893964 | 0.026574225 | dollars | FALSE | East | summer | exclusive | Acme | 0.23 | TRUE | | 2 | basketball | red | gnidlaps | 6.287116543 | 34.13904283 | cents | TRUE | East | spring | exclusive | Acme | 0.1 | FALSE | | 2 | basketball | red | gnidlaps | 6.287116543 | 34.13904283 | cents | TRUE | South | spring | exclusive | Acme | 0.04 | TRUE | | 2 | basketball | red | gnidlaps | 6.287116543 | 34.13904283 | cents | TRUE | Northeast | spring | exclusive | Acme | 0.02 | FALSE | | 3 | football | red | nosliw | 16.19975422 | 87.9646654 | cents | TRUE | Northeast | fall | exclusive | Acme | 0.06 | TRUE | | 3 | football | red | nosliw | 16.19975422 | 87.9646654 | cents | TRUE | South | fall | exclusive | Acme | 0.15 | FALSE | | 3 | football | red | nosliw | 16.19975422 | 87.9646654 | cents | TRUE | East | fall | exclusive | Acme | 0.03 | TRUE | | 1 | soccer ball | black | sadida | 0.006019576 | 0.032686297 | dollars | FALSE | West | summer | exclusive | Emca | 0.71 | TRUE | | 1 | soccer ball | black | sadida | 0.006019576 | 0.032686297 | dollars | FALSE | Southwest | summer | exclusive | Emca | 0.28 | FALSE | | 1 | soccer ball | black | sadida | 0.006019576 | 0.032686297 | dollars | FALSE | East | summer | exclusive | Emca | 0.28 | TRUE | | 2 | basketball | red | gnidlaps | 7.733153347 | 41.99102268 | cents | TRUE | East | spring | exclusive | Emca | 0.13 | FALSE | | 2 | basketball | red | gnidlaps | 7.733153347 | 41.99102268 | cents | TRUE | South | spring | exclusive | Emca | 0.05 | TRUE | | 2 | basketball | red | gnidlaps | 7.733153347 | 41.99102268 | cents | TRUE | Northeast | spring | exclusive | Emca | 0.03 | FALSE | | 3 | football | red | nosliw | 19.92569769 | 108.1965384 | cents | TRUE | Northeast | fall | exclusive | Emca | 0.08 | TRUE | | 3 | football | red | nosliw | 19.92569769 | 108.1965384 | cents | TRUE | South | fall | exclusive | Emca | 0.19 | FALSE | | 3 | football | red | nosliw | 19.92569769 | 108.1965384 | cents | TRUE | East | fall | exclusive | Emca | 0.04 | TRUE | +------------+--------------+-------+----------+--------------------+---------------+---------+----------+-----------+-------------+--------------+---------+-------+-------+
以下内容：
+------------+--------------+-------+----------+--------------------+---------------+---------+----------+-------------+--------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| product_id | product_name | color | brand | manufacturing_cost | shipping_cost | units | seasonal | high_season | relationship | shipper | status
+------------+--------------+-------+----------+--------------------+---------------+---------+----------+-------------+--------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 1 | soccer ball | black | sadida | 0.004893964 | 0.026574225 | dollars | FALSE | summer | exclusive | Acme | "{"West"" : {"sales" : 0.58, "alert" : True}, "Southwest" : {"sales" : 0.23, "alert" : False}, "East" : {"sales" : 0.23, "alert" : True}}"
| 2 | basketball | red | gnidlaps | 6.287116543 | 34.13904283 | cents | TRUE | spring | exclusive | Acme | "{"East" : {"sales" : 0.1, "alert" : True}, "South" : {"sales" : 0.04, "alert" : False}, "Northeast" : {"sales" : 0.02, "alert" : True}}"
| 3 | football | red | nosliw | 16.19975422 | 87.9646654 | cents | TRUE | fall | exclusive | Acme | "{"Northeast" : {"sales" : 0.06, "alert" : True}, "South" : {"sales" : 0.15, "alert" : False}, "East" : {"sales" : 0.03, "alert" : True}}"
| 1 | soccer ball | black | sadida | 0.006019576 | 0.032686297 | dollars | FALSE | summer | exclusive | Emca | "{"West" : {"sales" : 0.71, "alert" : True}, "Southwest" : {"sales" : 0.28, "alert" : False}, "East" : {"sales" : 0.28, "alert" : True}}"
| 2 | basketball | red | gnidlaps | 7.733153347 | 41.99102268 | cents | TRUE | spring | exclusive | Emca | "{"West" : {"sales" : 0.13, "alert"" : True}, "Southwest" : {"sales" : 0.05, "alert" : False}, "East" : {"sales" : 0.03, "alert" : True}}"
| 3 | football | red | nosliw | 19.92569769 | 108.1965384 | cents | TRUE | fall | exclusive | Emca | "{"West" : {"sales" : 0.08, "alert" : True}, "Southwest" : {"sales" : 0.19, "alert" : False}, "East" : {"sales" : 0.04,"alert" : True}}"
+------------+--------------+-------+----------+--------------------+---------------+---------+----------+-------------+--------------+---------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
答案 0 :(得分：0)
您可以将groupby和to_dict与set_index和reset_index一起使用：
print df.groupby(['product_id',
'product_name',
'color',
'brand',
'manufacturing_cost',
'shipping_cost',
'units',
'seasonal',
'high_season',
'relationship',
'shipper'])[["market", "sales","alert"]]
.apply(lambda x: x.set_index('market').to_dict(orient='index') )
.reset_index(name='status')