python - Pandas 首次发货后 3 天内累计发货的单位数

Question

有点难以解释，但我会尽力而为，请多多包涵。

我有一个包含 ID、发货日期和单位的 pd。我想计算 3 天时间范围内发货的单位，并且计数不应重叠，例如我的数据框如下。

ID          Shipping Date Units Expected output
153131151007    20180801    1   1
153131151007    20180828    1   2
153131151007    20180829    1   0
153131151007    20180904    1   1
153131151007    20181226    2   4
153131151007    20181227    1   0
153131151007    20181228    1   0
153131151007    20190110    1   1
153131151007    20190115    2   3
153131151007    20190116    1   0
153131151011*   20180510    1   2
153131151011*   20180511    1   0
153131151011*   20180513    1   2
153131151011*   20180515    1   0
153131151011*   20180813    1   1
153131151011*   20180822    1   2
153131151011*   20180824    1   0
153131151011*   20190103    1   1

代码应该检查日期，看看接下来的 3 天内是否有任何发货，如果有发货，它应该在当前日期列中对其进行汇总，并确保它不考虑下一个日期的总计数计算。

因此对于第一个 ID 发货日期 20181226，它会检查 1226、1227、1228 并将它们加在一起并在 1226 中显示结果，并在接下来的 2 个单元格中显示 0。

与第 2 个 ID 20180510 类似，0510 是该系列发货的第一个日期。它检查 0510,0511 和 0512 并在 0510 中求和并将其余部分归零，这就是为什么 0511 不考虑 0513 并且它是其他装运组的一部分。

data = pd.DataFrame({'ID':['153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*'],
'Date':[20180801,20180828,20180829,20180904,20181226,20181227,20181228,20190110,20190115,20190116,20180510,20180511,20180513,20180515,20180813,20180822,20180824,20190103],
'Units':[1,1,1,1,2,1,1,1,2,1,1,1,1,1,1,1,1,1]})

Answer 1

这有效，但结果是宽格式:

import pandas as pd
import numpy as np
from dateutil.parser import parse
from datetime import timedelta

data = pd.DataFrame({'ID':['153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151007','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*','153131151011*'],
'Date':[20180801,20180828,20180829,20180904,20181226,20181227,20181228,20190110,20190115,20190116,20180510,20180511,20180513,20180515,20180813,20180822,20180824,20190103],
'Units':[1,1,1,1,2,1,1,1,2,1,1,1,1,1,1,1,1,1]})

def keep_first(ser):
    ixs = []
    ts = ser.dropna().index[0]
    while ts <= ser.dropna().index.max():
        if ts in ser.dropna().index:
            ixs.append(ts)
            ts+=timedelta(3)
        else:
            ts+=timedelta(1)
    return np.where(ser.index.isin(ixs), ser, 0)

data['Date'] = data['Date'].map(lambda x: parse(str(x))) # parse dates

units = data.groupby(['ID', 'Date']).sum().unstack(0).resample('D').sum() # create resampled units df

units = units.sort_index(ascending=False).rolling(3, min_periods=1).sum().sort_index() # calculate forward-rolling sum

grouped_ix = data.groupby(['ID', 'Date']).sum().unstack(0).index # get indices for actual data

units.loc[grouped_ix].apply(keep_first) # get sums for actual data indices, keep only first