import pandas as pd
import datetime

def state(statedate):
    # 週日期對應
    # 往前取365天
    df = pd.DataFrame([str(d)[:10] for d in pd.date_range(end=statedate, periods=365)],columns=['date'])
    # 生成周
    df['week'] = df['date'].apply(lambda x: int(datetime.datetime.strptime(x,"%Y-%m-%d").strftime("%W"
 
)))
    df['week'] = df['week'].apply(lambda x:'%s%s' % ('week', str(x)))
    # 取年月
    df['new_date'] = df['date'].apply(lambda x: x[-5:])
    # 排序
    df = df.sort_values(by=['new_date'],ascending=[True])
    # 周開始日期
    df_first = df.drop_duplicates(subset=['week'], keep='first')
    df_first = df_first.
 
rename(columns={'new_date':'start_date'})
    # 周結束日期
    df_last = df.drop_duplicates(subset=['week'], keep='last')
    df_last = df_last.rename(columns={'new_date':'end_date'})
    df_date = pd.merge(df_first[['start_date','week']],df_last[['end_date','week']],on='week')
    tt = datetime.datetime.
 
strptime(statedate, '%Y-%m-%d')
    this_year = tt.year
    df_date['start_date'] = df_date['start_date'].apply(lambda x:'%s-%s' % (str(this_year), str(x)))
    df_date['end_date'] = df_date['end_date'].apply(lambda x: '%s-%s' % (str(this_year), str(x)))
    return df_date

print(state('2018-12-31'))

    start_date    week    end_date
0   2018-01-01   week1  2018-01-07
1   2018-01-08   week2  2018-01-14
2   2018-01-15   week3  2018-01-21
3   2018-01-22   week4  2018-01-28
4   2018-01-29   week5  2018-02-04
5   2018-02-05   week6  2018-02-11
6   2018-02-12   week7  2018-02-18
7   2018-02-19   week8  2018-02-25
8   2018-02-26   week9  2018-03-04
9   2018-03-05  week10  2018-03-11
10  2018-03-12  week11  2018-03-18
11  2018-03-19  week12  2018-03-25
12  2018-03-26  week13  2018-04-01
13  2018-04-02  week14  2018-04-08
14  2018-04-09  week15  2018-04-15
15  2018-04-16  week16  2018-04-22
16  2018-04-23  week17  2018-04-29
17  2018-04-30  week18  2018-05-06
18  2018-05-07  week19  2018-05-13
19  2018-05-14  week20  2018-05-20
20  2018-05-21  week21  2018-05-27
21  2018-05-28  week22  2018-06-03
22  2018-06-04  week23  2018-06-10
23  2018-06-11  week24  2018-06-17
24  2018-06-18  week25  2018-06-24
25  2018-06-25  week26  2018-07-01
26  2018-07-02  week27  2018-07-08
27  2018-07-09  week28  2018-07-15
28  2018-07-16  week29  2018-07-22
29  2018-07-23  week30  2018-07-29
30  2018-07-30  week31  2018-08-05
31  2018-08-06  week32  2018-08-12
32  2018-08-13  week33  2018-08-19
33  2018-08-20  week34  2018-08-26
34  2018-08-27  week35  2018-09-02
35  2018-09-03  week36  2018-09-09
36  2018-09-10  week37  2018-09-16
37  2018-09-17  week38  2018-09-23
38  2018-09-24  week39  2018-09-30
39  2018-10-01  week40  2018-10-07
40  2018-10-08  week41  2018-10-14
41  2018-10-15  week42  2018-10-21
42  2018-10-22  week43  2018-10-28
43  2018-10-29  week44  2018-11-04
44  2018-11-05  week45  2018-11-11
45  2018-11-12  week46  2018-11-18
46  2018-11-19  week47  2018-11-25
47  2018-11-26  week48  2018-12-02
48  2018-12-03  week49  2018-12-09
49  2018-12-10  week50  2018-12-16
50  2018-12-17  week51  2018-12-23
51  2018-12-24  week52  2018-12-30
52  2018-12-31  week53  2018-12-31