Changing timeseries column into a date-CodePudding

I have a timeseries with 2 columns, the first being hours after 1 Jan 1970. In this column, a year is only 360 days, with 12 months of 30 days. I need to convert this column into a usable date so that I can analyse the other column based on month, year etc (e.g 1997-Jan-1-1 being year-month-day-hour).

I need to make an array with modulo, to convert the each row of the hours column into hour_of_day, day_of_month, year etc, so that the column is instead a year, month, day and hour. But I don't know how to do this. Appreciate it might be confusing. Any help on doing this would be very helpful.

Input: 233280.5 (in hours)
Output: 1997-01-01-01 (year-day-month-hour)

CodePudding user response：

you can calculate the number of years and add it to the reference date like e.g.

import pandas as pd
import numpy as np
from pandas.tseries.offsets import DateOffset

refdate = pd.Timestamp('1970-01-01')
df = pd.DataFrame({'360d_year_hours': [233280.5]})

# we calculate the number of years and fractional years as helper Series
y_frac, y = np.modf(df['360d_year_hours'] / (24*360))

# now we can calculate the new date's year:
df['datetime'] = pd.Series(refdate   DateOffset(years=i) for i in y)

# we need the days in the given year to be able to use y_frac
daysinyear = np.where(df['datetime'].dt.is_leap_year, 366, 365)

# ...so we can update the datetime and round to the hour:
df['datetime'] = (df['datetime']   pd.to_timedelta(y_frac*daysinyear, unit='d')).dt.round('h')

# df['datetime']
# 0   1997-01-01 01:00:00
# Name: datetime, dtype: datetime64[ns]