Here is a sample showing two failure cases and one success case. The failures occur on half days when start and end dates are the same.
>>> import pandas_market_calendars as mcal
>>> from datetime import datetime
>>> calendar = mcal.get_calendar('NYSE')
>>> dt = datetime(2022, 11, 25)
>>> print(calendar.schedule(start_date=dt, end_date=dt, tz='America/New_York'))
<stdin>:1: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
market_open market_close
2022-11-25 2022-11-25 09:30:00-05:00 2022-11-25 13:00:00-05:00
>>> dt = datetime(2023, 7, 3)
>>> print(calendar.schedule(start_date=dt, end_date=dt, tz='America/New_York'))
<stdin>:1: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
market_open market_close
2023-07-03 2023-07-03 09:30:00-04:00 2023-07-03 13:00:00-04:00
>>> dt = datetime(2023, 7, 5)
>>> print(calendar.schedule(start_date=dt, end_date=dt, tz='America/New_York'))
market_open market_close
2023-07-05 2023-07-05 09:30:00-04:00 2023-07-05 16:00:00-04:00
My system is running MacOS Monterey, python 3.10, and pandas market calendars 4.1.1
Here is a sample showing two failure cases and one success case. The failures occur on half days when start and end dates are the same.
My system is running MacOS Monterey, python 3.10, and pandas market calendars 4.1.1