Dropping Time From Datetime <[m8] In Pandas

So I have a 'Date' column in my data frame where the dates have the format like this 0 1998-08-26 04:00:00 If I only want the Year month and day how do I drop the trivial hour

Solution 1:

The quickest way is to use DatetimeIndex's normalize (you first need to make the column a DatetimeIndex):

In [11]:df=pd.DataFrame({"t":pd.date_range('2014-01-01',periods=5,freq='H')})In [12]:dfOut[12]:t02014-01-01 00:00:0012014-01-01 01:00:0022014-01-01 02:00:0032014-01-01 03:00:0042014-01-01 04:00:00In [13]:pd.DatetimeIndex(df.t).normalize()Out[13]:<class'pandas.tseries.index.DatetimeIndex'>
[2014-01-01, ..., 2014-01-01]
Length:5,Freq:None,Timezone:NoneIn [14]:df['date']=pd.DatetimeIndex(df.t).normalize()In [15]:dfOut[15]:tdate02014-01-01 00:00:00 2014-01-0112014-01-01 01:00:00 2014-01-0122014-01-01 02:00:00 2014-01-0132014-01-01 03:00:00 2014-01-0142014-01-01 04:00:00 2014-01-01

DatetimeIndex also has some other useful attributes, e.g. .year, .month, .day.

---

From 0.15 they'll be a dt attribute, so you can access this (and other methods) with:

df.t.dt.normalize()
# equivalent to
pd.DatetimeIndex(df.t).normalize()

Solution 2:

Another option

df['my_date_column'].dt.date

Would give

02019-06-1512019-06-1522019-06-1532019-06-1542019-06-15

Solution 3:

Another Possibility is using str.split

df['Date'] = df['Date'].str.split(' ',expand=True)[0]

This should split the 'Date' column into two columns marked 0 and 1. Using the whitespace in between the date and time as the split indicator.

Column 0 of the returned dataframe then includes the date, and column 1 includes the time. Then it sets the 'Date' column of your original dataframe to column [0] which should be just the date.

Solution 4:

At read_csv with date_parser

to_date = lambda times : [t[0:10] for t in times]

df = pd.read_csv('input.csv', 
                  parse_dates={date: ['time']},
                  date_parser=to_date,
                  index_col='date')