How to change the Timeseries data from one time period to 1 min time periods for below data
Time Series Data:
Open High Low Close
DateTime
2019-03-22 09:15:00 1342 1342 1342 1342
2019-03-22 09:15:09 1344 1344 1344 1344
2019-03-22 09:15:12 1344.4 1344.4 1344.4 1344.4
2019-03-22 09:15:17 1345 1345 1345 1345
2019-03-22 09:15:22 1344.4 1345.4 1344.4 1344.4
2019-03-22 09:15:24 1349 1349 1349 1349
2019-03-22 09:15:32 1346 1346 1346 1346
2019-03-22 09:15:36 1346 1346 1346 1346
2019-03-22 09:15:41 1346.25 1346.25 1346.25 1346.25
2019-03-22 09:15:43 1346.25 1346.25 1346.25 1346.25
2019-03-22 09:15:45 1346 1346 1346 1346
2019-03-22 09:15:55 1344.45 1344.45 1344.45 1344.45
2019-03-22 09:16:00 1344.4 1344.4 1344.4 1344.4
I would like to have as 1min time frame data. really confused with resample function, to_period...etc.
If you want to have correct OHLC values after the resampling, you need to apply proper aggregation functions (taking first
for Open, max
for High, min
for Low and last
for Close):
df.resample('1T').agg({
'Open': 'first',
'High': 'max',
'Low': 'min',
'Close': 'last'})
Output:
Open High Low Close
DateTime
2019-03-22 09:15:00 1342.0 1349.0 1342.0 1344.45
2019-03-22 09:16:00 1344.4 1344.4 1344.4 1344.40
Resample returns a Resampler object on which you apply the aggregate function,
df.resample('1T').last()
Open High Low Close
DateTime
2019-03-22 09:15:00 1344.45 1344.45 1344.45 1344.45
2019-03-22 09:16:00 1344.40 1344.40 1344.40 1344.40
If you only wish to change the period but not aggregate the values, use to_period
df.to_period('1T')
Open High Low Close
DateTime
2019-03-22 09:15 1342.00 1342.00 1342.00 1342.00
2019-03-22 09:15 1344.00 1344.00 1344.00 1344.00
2019-03-22 09:15 1344.40 1344.40 1344.40 1344.40
2019-03-22 09:15 1345.00 1345.00 1345.00 1345.00
2019-03-22 09:15 1344.40 1345.40 1344.40 1344.40
2019-03-22 09:15 1349.00 1349.00 1349.00 1349.00
2019-03-22 09:15 1346.00 1346.00 1346.00 1346.00
2019-03-22 09:15 1346.00 1346.00 1346.00 1346.00
2019-03-22 09:15 1346.25 1346.25 1346.25 1346.25
2019-03-22 09:15 1346.25 1346.25 1346.25 1346.25
2019-03-22 09:15 1346.00 1346.00 1346.00 1346.00
2019-03-22 09:15 1344.45 1344.45 1344.45 1344.45
2019-03-22 09:16 1344.40 1344.40 1344.40 1344.40
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