有点难以解释,但请尽我所能,请忍受我。

我有一个ID,发货日期和单位的Pd。
我想计算3天内的发货量,并且该计数不应重叠,例如我的数据帧如下。

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


该代码应检查日期,看看接下来3天内是否有货,如果有货,则应在其当前日期栏中将其加总,并确保不考虑用于下一个日期计算的总计数。

因此,对于第一个ID运送日期20181226,它将检查1226、1227、1228并将它们加在一起并在1226中显示结果,然后在接下来的2个单元格中显示0。

同样,对于第二个ID 20180510,0510是该系列中发货的第一个日期。它检查0510、0511和0512并将其求和到0510中,然后将其余部分清零,这就是为什么0511不考虑0513并将其作为其他装运组的一部分的原因。

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]})

最佳答案

这可以工作,但是结果采用宽格式:

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

关于python - 自从 Pandas 第一次发货以来的3天内累计/累计发货数量,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/56226784/

10-12 18:10