使用日期、时间和时区解析 object 索引

Question

Python Q. How to parse an object index in a data frame into its date, time, and time zone?

The format is "YYY-MM-DD HH:MM:SS-HH:MM" where the right "HH:MM" is the timezone.

Example: Midnight Jan 1st, 2020 in Mountain Time:

2020-01-01 00:00:00-07:00

I'm trying to convert this into seven columns in the data frame:

YYYY, MM, DD, HH, MM, SS, TZ

Answer 1

Use pd.to_datetime to parse a string column into a datetime array

datetimes = pd.to_datetime(column)

once you have this, you can access elements of the datetime object with the .dt datetime accessor :

final = pd.DataFrame({
    "year": datetimes.dt.year,
    "month": datetimes.dt.month,
    "day": datetimes.dt.day,
    "hour": datetimes.dt.hour,
    "minute": datetimes.dt.minute,
    "second": datetimes.dt.second,
    "timezone": datetimes.dt.tz,
})

See the pandas user guide section on date/time functionality for more info

Answer 2

df

    Date
0   2022-05-01 01:10:04+07:00
1   2022-05-02 05:09:10+07:00
2   2022-05-02 11:22:05+07:00
3   2022-05-02 10:00:30+07:00

df['Date'] = pd.to_datetime(df['Date'])

df['tz']= df['Date'].dt.tz
df['year']= df['Date'].dt.year
df['month']= df['Date'].dt.month
df['month_n']= df['Date'].dt.month_name()
df['day']= df['Date'].dt.day
df['day_n']= df['Date'].dt.day_name()
df['h']= df['Date'].dt.hour
df['mn']= df['Date'].dt.minute
df['s']= df['Date'].dt.second

df['T']= df['Date'].dt.time 
df['D']= df['Date'].dt.date  

    Date                        tz                      year    month   month_n day     day_n   h   mn  s   T           D
0   2022-05-01 01:10:04+07:00   pytz.FixedOffset(420)   2022    5       May     1       Sunday  1   10  4   01:10:04    2022-05-01
1   2022-05-02 05:09:10+07:00   pytz.FixedOffset(420)   2022    5       May     2       Monday  5   9   10  05:09:10    2022-05-02
2   2022-05-02 11:22:05+07:00   pytz.FixedOffset(420)   2022    5       May     2       Monday  11  22  5   11:22:05    2022-05-02
3   2022-05-02 10:00:30+07:00   pytz.FixedOffset(420)   2022    5       May     2       Monday  10  0   30  10:00:30    2022-05-02