在Python / Pandas中如何将Century-months转换为DateTimeIndex？

Question

I am working with a dataset that encodes dates as the integer number of months since December 1899, so month 1 is January 1900 and month 1165 is January 1997. I would like to convert to a pandas DateTimeIndex. So far the best I've come up with is:

month0 = np.datetime64('1899-12-15')
one_month = np.timedelta64(30, 'D') + np.timedelta64(10.5, 'h')
birthdates = pandas.DatetimeIndex(month0 + one_month * resp.cmbirth)

The start date is the 15th of the month, and the timedelta is 30 days 10.5 hours, the average length of a calendar month. So the date within the month drifts by a day or two.

So this seems a little hacky and I wondered if there's a better way.

Answer 1

You can use built-in pandas date-time functionality.

import pandas as pd
import numpy as np

indexed_months = np.random.random_integers(0, high=1165, size=100)
month0 = pd.to_datetime('1899-12-01')
date_list = [month0 + pd.DateOffset(months=mnt) for mnt in indexed_months]
birthdates = pd.DatetimeIndex(date_list) 

I've made an assumption that your resp.cmbirth object looks like an array of integers between 0 and 1165.

I'm not quite clear on why you want the bin edges of the indices to be offset from the start or end of the month. This can be done:

shifted_birthdates = birthdates.shift(15, freq=pd.datetools.day)

and similarly for hours if you want. There is also useful info in the answers to this SO question and the related pandas github issue .