pd DataFrame, need to add columns, parsing text string date_time into pandas year, dayOfWeek, etc in one pass

Need Statement: I have performed a cursor.fetchall Select from a SQLite database, returning 'id' and 'date_time', the later of which is text. I want to create additional columns using pd.to_date of year, dayOfWeek, dayOfYear, hourOfDay

Issue: Following the example of a no-loop column add and population approach, I've tried multiple call combinations, none of which work.

I first tested a series of calls to confirm I could split the test date correctly;

sr = pd.Series(['2015-02-08 20:00:00']) 
sr = pd.to_datetime(sr) 

#Year: Series.dt.year The year of the datetime
#Day of week: Series.dt.dayofweek The day of the week with Monday=0, Sunday=6 
#Day of year: Series.dt.dayofyear The ordinal day of the year
#Hour: Series.dt.hour The hours of the datetime

print(sr)
print(sr.dt.year )
print(sr.dt.dayofweek )
print(sr.dt.dayofyear )
print(sr.dt.hour )

Everything came out as expected;

0 2015-02-08 20:00:00
dtype: datetime64[ns]
0 2015 dtype: int64
0 6
dtype: int64
0 39
dtype: int64
0 20
dtype: int64

The code I've tried works perfectly through the lines below, returning 105,861 rows x 2 columns;

def splitDateTime():
    try:
            sqliteConnection = sqlite3.connect('TestElecConsump.db')
            cursor = sqliteConnection.cursor()
            print("Connected to SQLite")
    
            sqlite_select_query = """SELECT id, date_time from WeatherRecord;"""
            cursor.execute(sqlite_select_query)
            records = cursor.fetchall()
            
            print("Total rows are:  ", len(records))
            print("Printing first row:", records[0])
            
            splitDatepd = pd.DataFrame(records, columns=['id','date_time']) 
            print("Dataframe shape:", splitDatepd.shape)
            print("Dataframe : " , splitDatepd, sep='\n')
        
            print ('records: ' + str(type(records)))
            print ('splitDatepd: ' + str(type(splitDatepd)))

However, the next lines execute with no output whatsoever;

#Add new column of Pandas datetime year

splitDatepd["pd-datetime"] = splitDatepd.to-datetime["date_time"].dt.year

print("Dataframe shape:", splitDatepd.shape)
print("Dataframe : " , splitDatepd, sep='\n')

So I decided to simplfy matters by repeated the above by leaving off the .year parsing;

splitDatepd["pd-datetime"] = splitDatepd.to-datetime["date_time"]

Still there was no change to splitDatepd.

When the def finalizes and returns the dataframe, a printout of it looks exactly like the original dataframe from the Select statement.

What am I doing wrong?

You could try with the pd.to_datetime function in a single column, for example:

splitDatepd["pd_datetime"] = pd.to_datetime(splitDatepd["date_time"])

PS: Remember that functions names use underscode, I mean, it is pd.to_datetime not pd.to-datetime.