When I create a series object, I specify the type without time: 'datetime64[D]'. But when I look at the data type of this series in the debugger, I see the presence of time: 'datetime64[ns]'.
Things get even worse when I start plotting: the empty time is reflected in the chart as zeros: 2020-04-05 00:00:00
How to get rid of the time and leave only the date?
shorten ?
my_date = "2020-04-05 00:00:00"[:10]
print(my_date)
(Feb-03-2021, 01:54 PM)Axel_Erfurt Wrote: [ -> ]shorten ?
my_date = "2020-04-05 00:00:00"[:10]
print(my_date)
How do you propose to apply this to datetime64 data types stored in a dataframe?
I've tried to reproduce your issue, but in almost all my cases I do not get the time at all if I don't want to. I usually prefer to work with the 'standard' datetime object, which works fine as an object as dtype. But when I set the dtype to datetime64 it also works as expected. So datetime64[D] will return a date and datetime[ns] will return date and time.
Do you have an example of your code and an example of the DateFrame or Series you're using?
Can use
dt.normalize().
Example.
import pandas as pd
url = 'http://bit.ly/uforeports'
df = pd.read_csv(url)
df['Time'] = pd.to_datetime(df.Time)
print(df.head())
Output:
City Colors Reported ... State Time
0 Ithaca NaN ... NY 1930-06-01 22:00:00
1 Willingboro NaN ... NJ 1930-06-30 20:00:00
2 Holyoke NaN ... CO 1931-02-15 14:00:00
3 Abilene NaN ... KS 1931-06-01 13:00:00
4 New York Worlds Fair NaN ... NY 1933-04-18 19:00:00
>>> df['Time'] = df['Time'].dt.normalize()
>>> df.head()
City Colors Reported Shape Reported State Time
0 Ithaca NaN TRIANGLE NY 1930-06-01
1 Willingboro NaN OTHER NJ 1930-06-30
2 Holyoke NaN OVAL CO 1931-02-15
3 Abilene NaN DISK KS 1931-06-01
4 New York Worlds Fair NaN LIGHT NY 1933-04-18
There is
dt which has
date:
>>> import pandas as pd
>>> df = pd.DataFrame(pd.date_range("2021-01-01", periods=3, freq="s"))
>>> df
0
0 2021-01-01 00:00:00
1 2021-01-01 00:00:01
2 2021-01-01 00:00:02
>>> df[0].dt.date
0 2021-01-01
1 2021-01-01
2 2021-01-01
Name: 0, dtype: object
>>> df[0].dt.year
0 2021
1 2021
2 2021
Name: 0, dtype: int64
>>> df[0].dt.day
0 1
1 1
2 1
Name: 0, dtype: int64
Please go through the following links at stackoverflow, I hope it will help you out:
/questions/16176996/keep-only-date-part-when-using-pandas-to-datetime
/questions/29310116/removing-time-from-datetime-variable-in-pandas