如何从熊猫数据框中的列中的每一行中删除重复值

我遇到了一个问题，如果两者中都有重复的日期，date_x并且date_yHoliday 列的输出似乎为具有相同确切日期的行的每次出现添加了一个假日实例。下面是我使用的小样本数据集来说明我的问题的代码。

from pandas.tseries.holiday import USFederalHolidayCalendar
from datetime import datetime
import pandas as pd

cal = USFederalHolidayCalendar()
holidays = (pd.DataFrame(cal.holidays(return_name=True), columns=['Holiday'])
            .reset_index()
            .rename({'index': 'Date'}, axis=1))
holidays['Date'] = pd.to_datetime(holidays['Date'])
df= pd.DataFrame({'Date_x': {0: '2020-12-22', 1: '2020-06-20', 2: '2020-02-11', 3: '2020-05-22', 4: '2020-12-22', 5: '2020-12-20', 6: '2020-12-20', 7: '2020-12-22'},
                  'Date_y': {0: '2021-01-01', 1: '2020-07-11', 2: '2020-03-27', 3: '2020-06-27', 4: '2021-01-01', 5: '2020-12-26', 6: '2020-12-27', 7: '2021-01-01'}})
df['Date_x'] = pd.to_datetime(df['Date_x'])
df['Date_y'] = pd.to_datetime(df['Date_y'])

Y = 2000 # dummy leap year to allow input X-02-29 (leap day)
seasons = [('Winter', (date(Y,  1,  1),  date(Y,  3, 20))),
           ('Spring', (date(Y,  3, 21),  date(Y,  6, 20))),
           ('Summer', (date(Y,  6, 21),  date(Y,  9, 22))),
           ('Fall', (date(Y,  9, 23),  date(Y, 12, 20))),
           ('Winter', (date(Y, 12, 21),  date(Y, 12, 31)))]

def get_season(x):
    x = x.replace(year=Y)
    return next(season for season, (start, end) in seasons
                if start <= x <= end)


def get_holiday():
    return pd.DataFrame([(h,y,z) for (h,d) in zip(holidays['Holiday'], holidays['Date'])
     for (y, z) in zip(df['Date_x'], df['Date_y']) if y.date() <= d.date() if d.date() <= z.date()], columns=['Holiday', 'Date_x', 'Date_y'])


s1 = df['Date_x'].apply(lambda x: get_season(x))
s2 = df['Date_y'].apply(lambda x: get_season(x))
df['Season']= [', '.join(list(set([x,y]))) for (x,y) in zip(s1,s2)]
dft = get_holiday()
dft = dft.groupby(['Date_x', 'Date_y'])['Holiday'].apply(lambda x: ', '.join(list(x)))
df = pd.merge(df, dft, how='left', on=['Date_x', 'Date_y'])

所以运行上面的代码我得到这个输出：

我不希望每个字段中所有这些重复的假日值，我的实际文件会变得更糟，我也会在这里添加那个 csv，以防万一。

这是我正在使用的全套数据的谷歌表格链接。