我有以下數據框。+-----------+----------+-----+| InvoiceNo | ItemCode | Qty |+-----------+----------+-----+| Inv-001 | A | 2 |+-----------+----------+-----+| Inv-001 | B | 3 |+-----------+----------+-----+| Inv-001 | C | 1 |+-----------+----------+-----+| Inv-002 | B | 3 |+-----------+----------+-----+| Inv-002 | D | 4 |+-----------+----------+-----+| Inv-003 | C | 3 |+-----------+----------+-----+| Inv-003 | D | 9 |+-----------+----------+-----+| Inv-004 | D | 5 |+-----------+----------+-----+| Inv-004 | E | 8 |+-----------+----------+-----+| Inv-005 | X | 2 |+-----------+----------+-----+Type我的任務是根據項目出現的優先級創建一個附加列。例如：ItemCode A有1st優先權。然后B有2nd優先權，C有3rd優先權。其余項目有least優先級和分類有Other。因此，如果任何 Invoice 包含 item A，則類型應該Type - A與其他項目無關。來自余額發票如果項目B包含，則類型應該是Type - B。同樣的C。如果 none ofA, B or C不存在于任何發票中，則類型應為Type - Other。下面是我想要的輸出。+-----------+----------+-----+--------------+| InvoiceNo | ItemCode | Qty | Type |+-----------+----------+-----+--------------+| Inv-001 | A | 2 | Type - A |+-----------+----------+-----+--------------+| Inv-001 | B | 3 | Type - A |+-----------+----------+-----+--------------+| Inv-001 | C | 1 | Type - A |+-----------+----------+-----+--------------+| Inv-002 | B | 3 | Type - B |+-----------+----------+-----+--------------+| Inv-002 | D | 4 | Type - B |+-----------+----------+-----+--------------+| Inv-003 | C | 3 | Type - C |+-----------+----------+-----+--------------+| Inv-003 | D | 9 | Type - C |+-----------+----------+-----+--------------+| Inv-004 | D | 5 | Type - Other |+-----------+----------+-----+--------------+| Inv-004 | E | 8 | Type - Other |+-----------+----------+-----+--------------+| Inv-005 | X | 2 | Type - Other |+-----------+----------+-----+--------------+現在，最重要efficient的pythonic方法是什么？
1 回答
江戶川亂折騰
TA貢獻1851條經驗 獲得超5個贊
我覺得我們可以Categorical做到transform
df['Type']=pd.Categorical(df.ItemCode,['A','B','C'],ordered=True)
df['Type']='Type_'+df.groupby('InvoiceNo')['Type'].transform('min').fillna('other')
更新
df['Type']=pd.Categorical(df.ItemCode,['A','B','C'],ordered=True)
df=df.sort_values('Type')
df['Type']='Type_'+df.groupby('InvoiceNo')['Type'].transform('first').fillna('other')
df=df.sort_index()
df
Out[32]:
InvoiceNo ItemCode Qty Type
0 Inv-001 A 2 Type_A
1 Inv-001 B 3 Type_A
2 Inv-001 C 1 Type_A
3 Inv-002 B 3 Type_B
4 Inv-002 D 4 Type_B
5 Inv-003 C 3 Type_C
6 Inv-003 D 9 Type_C
7 Inv-004 D 5 Type_other
8 Inv-004 E 8 Type_other
9 Inv-005 X 2 Type_other
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