Below data is in the interval of 5 mins
Dataframe names as df:
| script_id | date_time | open | high | low | close | volume | |
|---|---|---|---|---|---|---|---|
| 0 | 201 | 2019-02-04 14:55:00 | 1408.05 | 1408.05 | 1407 | 1408 | 2384 |
| 1 | 201 | 2019-02-04 15:00:00 | 1408 | 1410.6 | 1407.2 | 1408.85 | 12621 |
| 2 | 201 | 2019-02-04 15:05:00 | 1408.85 | 1410.45 | 1407.05 | 1407.05 | 3880 |
| 3 | 201 | 2019-02-04 15:10:00 | 1407.05 | 1409.4 | 1404.85 | 1404.85 | 12992 |
| 4 | 201 | 2019-02-04 15:15:00 | 1404.85 | 1408.7 | 1403.5 | 1404.25 | 30803 |
| 5 | 201 | 2019-02-04 15:20:00 | 1404.25 | 1405 | 1402.7 | 1404.8 | 14624 |
| 6 | 201 | 2019-02-04 15:25:00 | 1404.8 | 1405 | 1402.05 | 1403.8 | 8407 |
| 7 | 201 | 2019-02-05 09:15:00 | 1400 | 1416.05 | 1400 | 1410.75 | 17473 |
trying to group it in 10 mins by executing below code:
df_f = df.groupby(['script_id', pd.Grouper(key='date_time', freq='10T', origin='start')])\
.agg(open=pd.NamedAgg(column='open', aggfunc='first'),
high=pd.NamedAgg(column='high', aggfunc='max'),
low=pd.NamedAgg(column='low', aggfunc='min'),
close=pd.NamedAgg(column='close', aggfunc='last'),
volume=pd.NamedAgg(column='volume', aggfunc='sum'))\
.reset_index()
print(df_f)
Result:
Expected Result:- 0,1,2 are as expected below should be for 3 and there should not be 4.
| script_id | date_time | open | high | low | close | volume | |
|---|---|---|---|---|---|---|---|
| 3 | 201 | 2019-02-04 15:25:00 | 1404.8 (value of 6) | 1416.05 (highest among 6 & 7) | 400 (lowest among 6 & 7) | 1410.75 (value of 7) | 25880 (sum of 6 & 7) |
How can we combine last two 5min tf to one 10min tf?
Note:- There are possibilities to have holiday gap as well between two days
