If you need the result to be timezone-aware, you can use the replace() method of datetime objects. This preserves timezone, so you can do

>>> from django.utils import timezone
>>> now = timezone.now()
>>> now
datetime.datetime(2018, 8, 30, 14, 15, 43, 726252, tzinfo=<UTC>)
>>> now.replace(hour=0, minute=0, second=0, microsecond=0)
datetime.datetime(2018, 8, 30, 0, 0, tzinfo=<UTC>)

Note that this returns a new datetime object -- now remains unchanged.

You're calling datetime.datetime.strftime, which requires as its first argument a datetime.datetime instance, because it's an unbound method; but you're passing it a string instead of a datetime instance, whence the obvious error.

You can work purely at a string level if that's the result you want; with the data you give as an example, date_str.split()[0] for example would be exactly the 2015-02-21 string you appear to require.

Or, you can use datetime, but then you need to parse the string first, not format it -- hence, strptime, not strftime:

dt = datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S')
date = dt.date()

if it's a datetime.date object you want (but if all you want is the string form of the date, such an approach might be "overkill":-).

Once you have a datetime object just use strftime

import datetime
d = datetime.datetime.now()  # Some datetime object.
print(d.strftime('%Y-%m-%d'))

which gives

2020-02-20

You can maintain the datetime functionality and set the time portion to 00:00:00 with normalize.

df.index = df.index.normalize()

# For non-Index datetime64[ns] dtype columns you use the `.dt` accessor:
# df['column'] = df['column'].dt.normalize()

import pandas as pd
df = pd.DataFrame([1, 2, 3, 4], index=pd.date_range('2018', periods=4, freq='H'))

df.index = df.index.normalize()

print(df)
#            0
#2018-01-01  1
#2018-01-01  2
#2018-01-01  3
#2018-01-01  4

Looking at the index:

df.index
#DatetimeIndex(['2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01'], dtype='datetime64[ns]', freq=None)

And the values are Timestamps:

df.index[0]
#Timestamp('2018-01-01 00:00:00')

The following will convert what you have to datetime.time() objects:

dataset['TimeStamp'] = pd.Series([val.time() for val in dataset['TimeStamp']])

Output

  TimeStamp
0  05:15:00
1  05:28:00
2  06:15:00