Question

这是我的数据框

+--------+-------------+----------+---------------+------------+-------------+-----------+
|        | Customer ID | Quantity | Invoice Value |       Date | InvoiceDate | UnitPrice |
+--------+-------------+----------+---------------+------------+-------------+-----------+
|    0   |   500249347 |      0.0 |         0.000 | 2018-01-02 |  2018-01-02 |     0.000 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
|    1   |   500006647 |      1.0 |        33.715 | 2018-01-02 |  2018-01-02 |    33.715 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
|    2   |   500407469 |      1.0 |        33.715 | 2018-01-02 |  2018-01-02 |    33.715 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
|    3   |   500642846 |      0.0 |         0.000 | 2018-01-02 |  2018-01-02 |     0.000 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
|    4   |   500005450 |      1.0 |        33.715 | 2018-01-02 |  2018-01-02 |    33.715 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
|   ...  |         ... |      ... |           ... |        ... |         ... |       ... |
+--------+-------------+----------+---------------+------------+-------------+-----------+
| 429545 |   500717072 |      1.0 |        45.620 | 2019-03-31 |  2019-03-31 |    45.620 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
| 429546 |   500105174 |      0.0 |         0.000 | 2019-03-31 |  2019-03-31 |     0.000 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
| 429547 |   500069720 |      0.0 |         0.000 | 2019-03-31 |  2019-03-31 |     0.000 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
| 429548 |   500105528 |      0.0 |         0.000 | 2019-03-31 |  2019-03-31 |     0.000 |
+--------+-------------+----------+---------------+------------+-------------+-----------+
| 429549 |   500732322 |      0.0 |         0.000 | 2019-03-31 |  2019-03-31 |     0.000 |
+--------+-------------+----------+---------------+------------+-------------+-----------+

我想提取功能（新列），例如每位客户自上次访问以来的天（每行的wrt ..快照日期），上次计费金额，上次非零计费金额，数量和自上次购买以来的天数等信息，可以使用一些自定义的累积聚合函数来完成，或者是否可以使用更简单的方法来实现？

Answer 1

我建议这样：

import pandas as pd
df = pd.DataFrame({'customer_id': [13, 16, 13, 13, 16, 16, 13],
                   'Date': ['2018-01-02', '2019-03-31', '2019-03-31', '2018-01-02', '2018-01-02', '2019-04-31',
                            '2018-01-02'],
                   'Invoice_value': [920, 920, 920, 920, 921, 921, 921],
                   'Unit_price': [1, 2, 3, 4, 6, 7, 8]})

append_data = [df[(df['customer_id'] == ac)].sort_values(by=['Date']).iloc[-1] for ac in df.customer_id.unique()]

Answer 2

自上次访问以来，我一直想这样的事情：

df['last_visited']=df.groupby('Customer ID')['Date'].diff()

有没有一种方法可以为大熊猫数据框编写带有groupby子句的自定义累积聚合函数？

2 个答案: