I have stock ticker data in the following format:
40289.65972
40289.66319
40289.66667
and Excel is able to magically convert them to:
4/22/14 3:50 PM
4/22/14 3:55 PM
4/22/14 4:00 PM
via "Format Cells"
How do I do the same conversion in pandas?
The solution mentioned in the link above works, so I will just repost the snippet here. Thanks!
import datetime
def minimalist_xldate_as_datetime(xldate, datemode):
# datemode: 0 for 1900-based, 1 for 1904-based
return (
datetime.datetime(1899, 12, 30)
+ datetime.timedelta(days=xldate + 1462 * datemode)
)
To stay within pandas (which is wicked fast), use to_timedelta()
import pandas as pd
# should get 7/7/1988 1:26:24 a.m. (https://support.microsoft.com/en-us/kb/214094)
pd.to_datetime('1899-12-30') + pd.to_timedelta(32331.06, 'D')
produces
Timestamp('1988-07-07 01:26:24')
If you have a dataframe full of excel-float-dates you can convert the whole thing:
df['BetterDT'] = pd.to_datetime('1899-12-30') + pd.to_timedelta(df.ExecDate, 'D')
Excel considers 1900 a leap year, so be careful with exactly what you want to translate: http://spreadsheetpage.com/index.php/oddity/the_intentional_date_bug/
floatsinto instances ofdatetime.datetimewhich you can use in Pandas.