You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Would be happy if I could input a pandas series with datetime64 format / datetime index and get the settlement period pandas series as an output. Perhaps there could be a more efficient way to handle a long time series other than running .apply to every row.
With the current implementation, such effect can be (inefficiently) achieved like so:
from sp2ts import ts2sp, to_unixtime
df["datetime"] = pd.to_datetime(
df.index, utc=True
).tz_convert("Europe/London")
# Convert the datetime column to Unix time using a vectorized operation
unix_times = df["datetime"].apply(to_unixtime)
# Apply the ts2sp function to the entire Unix time series at once
dates, settlement_periods = zip(*unix_times.apply(ts2sp))
df["date"] = dates
df["sp"] = settlement_periods
The text was updated successfully, but these errors were encountered:
Hello,
Thank you for this!
Would be happy if I could input a pandas series with datetime64 format / datetime index and get the settlement period pandas series as an output. Perhaps there could be a more efficient way to handle a long time series other than running
.apply
to every row.With the current implementation, such effect can be (inefficiently) achieved like so:
The text was updated successfully, but these errors were encountered: