Reputation: 2027
I have a column in an Excel file that is filled with dates, with the mm/dd/yyyy
format.
I import the column into a list in Python using this code:
first_excel_file = pd.read_excel('test.xlsx')
item_end_date = first_excel_file['Item End Date'].values.tolist()
But I get this:
[1478476800000000000, 1476921600000000000, 1488240000000000000, 1488240000000000000, 1488240000000000000, 1488326400000000000, 1489622400000000000, 1489622400000000000, 1489968000000000000, 1494288000000000000, 1454198400000000000, 1454198400000000000, 1490918400000000000, 1490918400000000000, 1490918400000000000, 1491955200000000000, 1491955200000000000, 1446249600000000000, 1509408000000000000, 1509408000000000000, 1509408000000000000, 1364688000000000000, 1391126400000000000, 1398816000000000000, 1422662400000000000, 1418428800000000000, 1419292800000000000, 1422662400000000000, 1422662400000000000, 1422662400000000000, 1423612800000000000, 1426291200000000000, 1438300800000000000]
How can I import these dates and keep their original formatting instead of getting these numeric values?
Upvotes: 0
Views: 908
Reputation: 609
Are these timestamps? If so, you can convert them into dates. This may help:
from datetime import datetime
item_end_date = [datetime.fromtimestamp(adt//1000000000).strftime("%m/%d/%Y")
for adt in item_end_date]
You will get:
['11/06/2016', '10/19/2016', '02/27/2017', '02/27/2017', '02/27/2017',
'02/28/2017', '03/15/2017', '03/15/2017', '03/19/2017', '05/08/2017',
'01/30/2016', '01/30/2016', '03/30/2017', '03/30/2017', '03/30/2017',
'04/11/2017', '04/11/2017', '10/30/2015', '10/30/2017', '10/30/2017',
'10/30/2017', '03/30/2013', '01/30/2014', '04/29/2014', '01/30/2015',
'12/12/2014', '12/22/2014', '01/30/2015', '01/30/2015', '01/30/2015',
'02/10/2015', '03/13/2015', '07/30/2015']
Upvotes: 1