将日期（excel格式）的数字表示转换为python日期和时间，然后将它们拆分为pandas中的两个单独的dataframe列

Question

I'm importing some data in a spreadsheet. It's in a dataframe, but the date is in a numerical representation or format

41516.43

First, I want to convert it to a date and time object

date_val = 30/08/2013  10:13:26 AM

Then I would like to split the date_val into date and time separately, and place them in seperate columns in my dataframe (df)

| **original date** | **date**     | **time** | 
41516.43              30/08/2013      10:13:26 AM

Answer 1

Piecing together from another question :

In [11]: s = pd.Series([41516.43])

In [12]: from xlrd.xldate import xldate_as_tuple

In [13]: from datetime import datetime

In [14]: s.apply(lambda x: datetime(*xldate_as_tuple(x, 0)))
Out[14]:
0   2013-08-30 10:19:12
dtype: datetime64[ns]

Note: presumably slight difference is due to rounding of the float you gave.

and the "bare-knuckle no-seat-belts use-at-own-risk" version:

In [21]: pd.Timestamp('1899-12-30') + (pd.offsets.Day(1).nanos * s).astype(np.timedelta64)
Out[21]:
0   2013-08-30 10:19:12
dtype: datetime64[ns]

I think it's generally preferable to do parse dates while using read_excel .