我有一个像这样的 `df`：
```python
date	    time	value
2021-08	    0.0 	22.50
2021-08	    5.0 	6600.00
2021-09	    0.0 	1057.62
2021-09	    1.0 	646.35
2021-09	    2.0 	311.76
2021-09	    3.0 	3982.50
2021-09	    4.0 	900.00
2021-09	    7.0 	546.00
2021-09	    9.0 	1471.50
2021-09	    11.0    1535.16

new_df = pd.DataFrame(pd.date_range(start='2021-08', end=datetime.datetime.now(), freq='M'), columns=['value'])
new_df['commission'] = 0

leased	value
2021-08	22.50 + 6600
2021-09	6600 + 1057.62 + 646.35 + 311.76 + 3982.50 + 900.00 + 546.00 + 1471.50 + 1535.16
2021-10	6600 + 646.35 + 311.76 + 3982.50 + 900.00 + 546.00 + 1471.50 + 1535.16
2021-11	6600 + 3982.50 + 900.00 + 546.00 + 1471.50 + 1535.16
...

<details>
<summary>英文:</summary>
I have a `df` like this :

The `time` column represent for how many months the `value` is being payed from the start of `date`. So for example the first row remains the same, the second row remains the same as there is nothing to add, but the third row would be the `value` + `6600` because from the second row, the value of `6600` is being payed from `2021-08` to `2022-02`
I am unsure how can I achieve this, my idea was to create a new data frame:

And fill it somehow while iterating through the main `df` so that the end result should look like this:

</details>
# 答案1
**得分**: 2
以下是您要翻译的内容：
您可以使用一个月的周期，[`repeat`](https://pandas.pydata.org/docs/reference/api/pandas.Index.repeat.html)行，使用 [`groupby.cumcount`](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.cumcount.html) 增加周期，以及 [`groupby.agg`](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.aggregate.html) 进行聚合：
```python
# 生成周期
df['date'] = pd.to_datetime(df['date']).dt.to_period('M')
# 重复值，进行聚合
out = (
 df.loc[df.index.repeat(df['time'].add(1))]
   .assign(date=lambda d: d.groupby(level=0).cumcount().add(d['date']))
   .groupby('date', as_index=False)['value'].sum()
)

       date     value
0   2021-08   6622.50
1   2021-09  17050.89
2   2021-10  15993.27
3   2021-11  15346.92
4   2021-12  15035.16
5   2022-01  11052.66
6   2022-02   3552.66
7   2022-03   3552.66
8   2022-04   3552.66
9   2022-05   3006.66
10  2022-06   3006.66
11  2022-07   1535.16
12  2022-08   1535.16

这里是另一种选项：
这里我们使用 `map()` 和 `np.arange()` 来创建一个我们将使用 `explode()` 的列表。然后我们将月份添加到每个日期并使用 `groupby()` 按日期分组。
df['date'] = pd.to_datetime(df['date'])
(df.assign(time = df['time'].add(1).map(lambda x: np.arange(1, x+1)))
.explode('time').assign(date = lambda x: x.apply(lambda r: r['date']+pd.offsets.DateOffset(months = r['time']-1), axis=1))
.groupby('date')['value'].sum())
输出：
日期
2021-08-01     6622.50
2021-09-01    17050.89
2021-10-01    15993.27
2021-11-01    15346.92
2021-12-01    15035.16
2022-01-01    11052.66
2022-02-01     3552.66
2022-03-01     3552.66
2022-04-01     3552.66
2022-05-01     3006.66
2022-06-01     3006.66
2022-07-01     1535.16
2022-08-01     1535.16