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Oh, I should mention that when I feed the string datetimes into pyculiarity I do get these warnings now:
/usr/local/lib/python2.7/site-packages/pyculiarity/detect_ts.py:179: FutureWarning: iget(i) is deprecated. Please use .iloc[i] or .iat[i]
last_date = df.timestamp.iget(-1)
/usr/local/lib/python2.7/site-packages/pyculiarity/detect_ts.py:184: FutureWarning: iget(i) is deprecated. Please use .iloc[i] or .iat[i]
start_date = df.timestamp.iget(j)
/usr/local/lib/python2.7/site-packages/pyculiarity/detect_ts.py:187: FutureWarning: iget(i) is deprecated. Please use .iloc[i] or .iat[i]
df.timestamp.iget(-1))
/usr/local/lib/python2.7/site-packages/pyculiarity/detect_anoms.py:81: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
'value': (decomp['trend'] + decomp['seasonal']).truncate().convert_objects(convert_numeric=True)
In date_util.py you have:
Unfortunately my column is type:
datetime64[ns, UTC]
So it doesn't match and we fall through to the regexes which choke on the data type.
The problem, I think, is that I have timezone aware datetime types. Simply converting the timestamp column to a string did not work.
So, I stripped the timezones off before converting it to a string and that seemed to do the trick:
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