Unexpected result of resampling time series dataframe

Question

I have a time series dataframe (1 min intraday stock price) and would like to convert it to higher timeframe. But the result of this transformation is not what I expected and I can't figure out why.

The time series dataframe:

index	Date	Time	Open	High	Low	Close
0	2022-06-16	09:30:00	14.570	14.62	14.500	14.61
1	2022-06-16	09:31:00	14.630	14.85	14.620	14.79
2	2022-06-16	09:32:00	14.800	14.93	14.760	14.82
3	2022-06-16	09:33:00	14.820	14.82	14.650	14.70
4	2022-06-16	09:34:00	14.670	14.68	14.610	14.67
...	...	...	...	...	...	...
265	2022-06-16	13:55:00	14.090	14.10	14.080	14.09
266	2022-06-16	13:56:00	14.085	14.10	14.065	14.09
267	2022-06-16	13:57:00	14.090	14.12	14.090	14.10
268	2022-06-16	13:58:00	14.105	14.11	14.075	14.08
269	2022-06-16	13:59:00	14.070	14.09	14.060	14.06

I would like to convert the above data to 9 minutes timeframe. For this task I use the next function:

def convert_to_higher_timeframe(data, timeframe):  # data - dataframe, timeframe - desirable timeframe
    # Compute timestamp of each row
    try:
        datetime_col = pd.to_datetime(data['Date'] + ' ' + data['Time'].apply(lambda x: x.strftime('%H:%M:%S')))
    except AttributeError:
        datetime_col = pd.to_datetime(data['Date'] + ' ' + data['Time'])
    # Set datetime column as index and resample to higher timeframe
    data = data.set_index(datetime_col)    
    data = data.resample(f"{timeframe}T").agg({"Open": "first",
                                               "High": "max",
                                               "Low": "min",
                                               "Close": "last"})
    data.reset_index(inplace=True)
    data['Date'] = data['index'].dt.strftime('%Y-%m-%d')
    data['Time'] = data['index'].dt.time
    data.drop(columns=['index'])
    data = data[['Date', 'Time', 'Open', 'High', 'Low', 'Close']]
    data.dropna(inplace=True)    
    return data 

As result I expected to get the new dataframe first row of which represents the data for interval started at 09:30:00, the next one at 09:39:00 and so on. But insted I got the dataframe first row of which represents the data for interval started at 09:27:00 and each next one is plus 9 minutes. For other intervals (5, 6 minutes) all works great and the result is as expected. But why for 9 minutes timeframe the time shifts from 09:30 to 09:27 I can't figure out. Can anyone suggest what's going on here and why the time shifts when desirable timeframe is 9? Thanks in advance

Answer 1

I should have used the parameter origin in the function and set its value to 'start'. This is how:

data = data.resample(f"{timeframe}T", origin='start').agg({"Open": "first",
                                               "High": "max",
                                               "Low": "min",
                                               "Close": "last"})