如何从熊猫日期时间索引中删除时间值？

Question

Reading from cvs file.

df = pd.read_csv(filename, sep=";", names=['DateTime','Open','High','Low','Close','Vol'],
                 parse_dates = [0], index_col = 'DateTime')

Output data:

                            Open     High      Low    Close
DateTime                                                     
2016-01-03 00:00:00+00:00  1.08701  1.08723  1.08451  1.08515
2016-01-04 00:00:00+00:00  1.08701  1.09464  1.07811  1.08239
2016-01-05 00:00:00+00:00  1.08238  1.08388  1.07106  1.07502
2016-01-06 00:00:00+00:00  1.07504  1.07994  1.07185  1.07766
2016-01-07 00:00:00+00:00  1.07767  1.09401  1.07710  1.09256
2016-01-08 00:00:00+00:00  1.09255  1.09300  1.08030  1.09218

How do I delete time values from above DateTime Index?

Another question, how do I truncate values in Open/High/Low/Close to 6 decimals?

Answer 1

To truncate theo datetime, first get the objects into a DateTime object as it does the appropriate handling for this. For example, while printing it will only print the date values as time is always 0.

>>> c = pd.read_csv("a.csv", sep=",", names=['DateTime','Open','High','Low','Close','Vol'])
>>> c
                    DateTime     Open     High      Low    Close  Vol
0  2016-01-03 00:00:00+00:00  1.08701  1.08723  1.08451  1.08515  NaN
1  2016-01-04 00:00:00+00:00  1.08701  1.09464  1.07811  1.08239  NaN
2  2016-01-05 00:00:00+00:00  1.08238  1.08388  1.07106  1.07502  NaN
3  2016-01-06 00:00:00+00:00  1.07504  1.07994  1.07185  1.07766  NaN
4  2016-01-07 00:00:00+00:00  1.07767  1.09401  1.07710  1.09256  NaN
5  2016-01-08 00:00:00+00:00  1.09255  1.09300  1.08030  1.09218  NaN
>>> c.DateTime = pd.to_datetime(c.DateTime)
>>> c
    DateTime     Open     High      Low    Close  Vol
0 2016-01-03  1.08701  1.08723  1.08451  1.08515  NaN
1 2016-01-04  1.08701  1.09464  1.07811  1.08239  NaN
2 2016-01-05  1.08238  1.08388  1.07106  1.07502  NaN
3 2016-01-06  1.07504  1.07994  1.07185  1.07766  NaN
4 2016-01-07  1.07767  1.09401  1.07710  1.09256  NaN
5 2016-01-08  1.09255  1.09300  1.08030  1.09218  NaN

This would normally save the time also if it is present, it just doesn't show it here because the time values are 0. To only get the date, do:

>>> c.DateTime = c.DateTime.dt.date
>>> c.DateTime[0]
datetime.date(2016, 1, 3)