我想创建一个列，显示部门 99 中发生的先前购买(每个客户)的累积计数(滚动总和)
我的数据框看起来像这样；其中每一行都是一个单独的事务。
id chain dept category company brand date productsize productmeasure purchasequantity purchaseamount sale
0 86246 205 7 707 1078778070 12564 2012-03-02 12.00 OZ 1 7.59 268.90
1 86246 205 63 6319 107654575 17876 2012-03-02 64.00 OZ 1 1.59 268.90
2 86246 205 97 9753 1022027929 0 2012-03-02 1.00 CT 1 5.99 268.90
3 86246 205 25 2509 107996777 31373 2012-03-02 16.00 OZ 1 1.99 268.90
4 86246 205 55 5555 107684070 32094 2012-03-02 16.00 OZ 2 10.38 268.90
5 86246 205 97 9753 1021015020 0 2012-03-02 1.00 CT 1 7.80 268.90
6 86246 205 99 9909 104538848 15343 2012-03-02 16.00 OZ 1 2.49 268.90
7 86246 205 59 5907 102900020 2012 2012-03-02 16.00 OZ 1 1.39 268.90
8 86246 205 9 921 101128414 9209 2012-03-02 4.00 OZ 2 1.50 268.90
我这样做了:
shopdata6['transactions_99'] = 0
shopdata6['transactions_99'] = shopdata6[shopdata6['dept'] == 99].groupby(['id', 'dept'])['transaction_99'].cumsum()
更新:
id dept date purchase purchase_count_dept99(desired)
id1 199 date1 $10 0
id1 99 date1 $10 1
id1 100 date1 $50 1
id1 99 date2 $30 2
id2 100 date1 $10 0
id2 99 date1 $10 1
id3 99 date3 $10 1
应用此:
shopdata6['transaction_99'] = np.where(shopdata6['dept']==99, 1, 0)
shopdata6['transaction_99'] = shopdata6.groupby(['id'])['transaction_99'].transform('cumsum')
结果看起来不错，但是正确吗？
最佳答案
你的代码应该简化:
s = (shopdata6['dept']==99).astype(int)
shopdata6['transaction_99'] = s.groupby(shopdata6['id']).cumsum()
print (shopdata6)
id dept date purchase purchase_count_dept99(desired) transaction_99
0 id1 199 date1 $10 0 0
1 id1 99 date1 $10 1 1
2 id1 100 date1 $50 1 1
3 id1 99 date2 $30 2 2
4 id2 100 date1 $10 0 0
5 id2 99 date1 $10 1 1
6 id3 99 date3 $10 1 1
关于python - 根据其他列获取 Pandas 累积总和，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/55668238/