Working with dates in pandas

[snippsat]

Sorry one more quick question, can I do this or is it bad practice:

Use dt.normalize() then you don't mess up types,which should be datetime64[ns].
Remember the first thing to always with a dataframe is to check dtypes.

import pandas as pd
from io import StringIO

data = StringIO(
"""OBJECTID,Start Date
1,2018-01-11 12:00:00
2,2018-11-01 12:00:00
3,2019-04-11 12:00:00
4,2021-11-03 12:00:00""")

df = pd.read_csv(data, parse_dates=["Start Date"], index_col="OBJECTID")
df["Start Date"] = df["Start Date"].dt.normalize()
#df["Start Date"] = df["Start Date"].dt.date
print(df)
print(df.dtypes)
print('-' * 25)
print(df.info())

Output:
         Start Date
OBJECTID           
1        2018-01-11
2        2018-11-01
3        2019-04-11
4        2021-11-03
Start Date    datetime64[ns]
dtype: object
-------------------------
<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, 1 to 4
Data columns (total 1 columns):
 #   Column      Non-Null Count  Dtype         
---  ------      --------------  -----         
 0   Start Date  4 non-null      datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 64.0 bytes
None