Pandas: How to create a datetime object from Week and Year?
There is something fishy going on with weeks starting from 2019. The ISO-8601 standard assigns the 31st December 2018 to the week 1 of year 2019. The other approaches based on:
pd.to_datetime(df.Week.astype(str)+
df.Year.astype(str).add('-2') ,format='%W%Y-%w')
will give shifted results starting from 2019.
In order to be compliant with the ISO-8601 standard you would have to do the following:
import pandas as pd
import datetime
L1 = [52,53,1,2,5,52]
L2 = [2018,2018,2019,2019,2019,2019]
df = pd.DataFrame({"Week":L1,"Year":L2})
df['ISO'] = df['Year'].astype(str) + '-W' + df['Week'].astype(str) + '-1'
df['DT'] = df['ISO'].map(lambda x: datetime.datetime.strptime(x, "%G-W%V-%u"))
print(df)
It prints:
Week Year ISO DT
0 52 2018 2018-W52-1 2018-12-24
1 53 2018 2018-W53-1 2018-12-31
2 1 2019 2019-W1-1 2018-12-31
3 2 2019 2019-W2-1 2019-01-07
4 5 2019 2019-W5-1 2019-01-28
5 52 2019 2019-W52-1 2019-12-23
The week 53 of 2018 is ignored and mapped to the week 1 of 2019.
Please verify yourself on https://www.epochconverter.com/weeks/2019.
Like @Gianmario Spacagna mentioned for datetimes higher like 2018 use %V with %G:
L1 = [43,44,51,2,5,12,52,53,1,2,5,52]
L2 = [2016,2016,2016,2017,2017,2017,2018,2018,2019,2019,2019,2019]
df = pd.DataFrame({"Week":L1,"Year":L2})
df['new'] = pd.to_datetime(df.Week.astype(str)+
df.Year.astype(str).add('-1') ,format='%V%G-%u')
print (df)
Week Year new
0 43 2016 2016-10-24
1 44 2016 2016-10-31
2 51 2016 2016-12-19
3 2 2017 2017-01-09
4 5 2017 2017-01-30
5 12 2017 2017-03-20
6 52 2018 2018-12-24
7 53 2018 2018-12-31
8 1 2019 2018-12-31
9 2 2019 2019-01-07
10 5 2019 2019-01-28
11 52 2019 2019-12-23
Try this:
In [19]: pd.to_datetime(df.Year.astype(str), format='%Y') + \
pd.to_timedelta(df.Week.mul(7).astype(str) + ' days')
Out[19]:
0 2016-10-28
1 2016-11-04
2 2016-12-23
3 2017-01-15
4 2017-02-05
5 2017-03-26
dtype: datetime64[ns]
Initially I have timestamps in
s
It's much easier to parse it from UNIX epoch timestamp:
df['Date'] = pd.to_datetime(df['UNIX_Time'], unit='s')
Timing for 10M rows DF:
Setup:
In [26]: df = pd.DataFrame(pd.date_range('1970-01-01', freq='1T', periods=10**7), columns=['date'])
In [27]: df.shape
Out[27]: (10000000, 1)
In [28]: df['unix_ts'] = df['date'].astype(np.int64)//10**9
In [30]: df
Out[30]:
date unix_ts
0 1970-01-01 00:00:00 0
1 1970-01-01 00:01:00 60
2 1970-01-01 00:02:00 120
3 1970-01-01 00:03:00 180
4 1970-01-01 00:04:00 240
5 1970-01-01 00:05:00 300
6 1970-01-01 00:06:00 360
7 1970-01-01 00:07:00 420
8 1970-01-01 00:08:00 480
9 1970-01-01 00:09:00 540
... ... ...
9999990 1989-01-05 10:30:00 599999400
9999991 1989-01-05 10:31:00 599999460
9999992 1989-01-05 10:32:00 599999520
9999993 1989-01-05 10:33:00 599999580
9999994 1989-01-05 10:34:00 599999640
9999995 1989-01-05 10:35:00 599999700
9999996 1989-01-05 10:36:00 599999760
9999997 1989-01-05 10:37:00 599999820
9999998 1989-01-05 10:38:00 599999880
9999999 1989-01-05 10:39:00 599999940
[10000000 rows x 2 columns]
Check:
In [31]: pd.to_datetime(df.unix_ts, unit='s')
Out[31]:
0 1970-01-01 00:00:00
1 1970-01-01 00:01:00
2 1970-01-01 00:02:00
3 1970-01-01 00:03:00
4 1970-01-01 00:04:00
5 1970-01-01 00:05:00
6 1970-01-01 00:06:00
7 1970-01-01 00:07:00
8 1970-01-01 00:08:00
9 1970-01-01 00:09:00
...
9999990 1989-01-05 10:30:00
9999991 1989-01-05 10:31:00
9999992 1989-01-05 10:32:00
9999993 1989-01-05 10:33:00
9999994 1989-01-05 10:34:00
9999995 1989-01-05 10:35:00
9999996 1989-01-05 10:36:00
9999997 1989-01-05 10:37:00
9999998 1989-01-05 10:38:00
9999999 1989-01-05 10:39:00
Name: unix_ts, Length: 10000000, dtype: datetime64[ns]
Timing:
In [32]: %timeit pd.to_datetime(df.unix_ts, unit='s')
10 loops, best of 3: 156 ms per loop
Conclusion: I think 156 milliseconds for converting 10.000.000 rows is not that slow