AttributeError: 'Series' object has no attribute 'isoweekday'

AttributeError: 'Series' object has no attribute 'isoweekday'

我写了一个函数,将日期移动到周末和节假日的指定工作日数。当我将它与标量值一起使用时它工作正常但是我在尝试在 pandas DataFrame 上使用时遇到问题。 函数本身如下所示:

def move_date_by_days(init_date, roll=1, nwd_key=None, hol_key=None):
    '''
    moves date by n-number of working days forward or backward
    init_date: date, initial caluclation date
    roll: integer, number of days to move forward (+) or backward (-)
    nwd_key: string that stands for currency iso code, it is a key in non_working_days dictionary
    hol_key: string that stands for currency iso code, it is a key in holidays dictonary
    return: date
    '''
    nwd = non_working_days.get(nwd_key, [])
    hol = holidays.get(hol_key,[])
    moved_date = init_date + datetime.timedelta(days=roll)
    if (moved_date.isoweekday() in nwd) or (moved_date in hol):
        if roll >= 0:
            moved_date=move_date_by_days(init_date + datetime.timedelta(days=1), roll=roll, nwd_key=nwd_key, hol_key=hol_key)
        else:
            moved_date=move_date_by_days(init_date + datetime.timedelta(days=-1), roll=roll, nwd_key=nwd_key, hol_key=hol_key)
    return moved_date

现在,我有包含列 'start_date' 和 'end_date'

的 DataFrame 'df'
   start_date    end_date
0  2020-01-31  2020-04-30
1  2020-04-30  2020-07-31
2  2020-07-31  2020-10-31
3  2020-10-31  2020-11-28

我想创建名为 'fixing' 的第三列,该列将比 'start_date' 早 2 个工作日。我正在尝试这个:

dates_table['fixing'] = move_date_by_days(self.dates_table['start_date'], -2, self.ccy, self.ccy)

但是 returns AttributeError: 'Series' object has no attribute 'isoweekday'

请注意,当我引用 DataFrame 的标量数据时,该函数工作正常,例如

d1 = s1.dates_table.iat[0,0]
move_date_by_days(d1, -2, 'pln', 'pln')

它 returns 我所期望的:datetime.date(2020, 1, 29)

请问如何在数据框的整列上使用该函数?

IIUC,为什么不使用 offsets.BDay or offsets.CustomBusinessDay 列表 holidays:

df['fixing'] = df['start_date'] - pd.offsets.BDay(2)
df['fixing'] = df['start_date'] - pd.offsets.CustomBusinessDay(2, holidays=holidays['pln'])

[出局]

  start_date   end_date     fixing
0 2020-01-31 2020-04-30 2020-01-29
1 2020-04-30 2020-07-31 2020-04-28
2 2020-07-31 2020-10-31 2020-07-29
3 2020-10-31 2020-11-28 2020-10-29