databroker.Header.table¶
-
Header.table(stream_name='primary', fields=None, fill=False, timezone=None, convert_times=True, localize_times=True)[source]¶ Load the data from one event stream as a table (
pandas.DataFrame).- Parameters
- stream_namestr, optional
Get events from only “event stream” with this name.
Default is ‘primary’
- fieldsList[str], optional
whitelist of field names of interest; if None, all are returned
Default is None
- fillbool or Iterable[str], optional
Which fields to fill. If True, fill all possible fields.
Each event will have the data filled for the intersection of it’s external keys and the fields requested filled.
Default is False
- handler_registrydict, optional
mapping filestore specs (strings) to handlers (callable classes)
- convert_timesbool, optional
Whether to convert times from float (seconds since 1970) to numpy datetime64, using pandas. True by default.
- timezonestr, optional
e.g., ‘US/Eastern’; if None, use metadatastore configuration in self.mds.config[‘timezone’]
- localize_timesbool, optional
If the times should be localized to the ‘local’ time zone. If True (the default) the time stamps are converted to the localtime zone (as configure in mds).
This is problematic for several reasons:
apparent gaps or duplicate times around DST transitions
incompatibility with every other time stamp (which is in UTC)
however, this makes the dataframe repr look nicer
This implies convert_times.
Defaults to True to preserve back-compatibility.
- Returns
- tablepandas.DataFrame
Examples
Load the ‘primary’ data stream from the most recent run into a table.
>>> h = db[-1] >>> h.table()
This is equivalent. (The default stream_name is ‘primary’.)
>>> h.table(stream_name='primary') time intensity 0 2017-07-16 12:12:37.239582345 102 1 2017-07-16 12:12:39.958385283 103
Load the ‘baseline’ data stream.
>>> h.table(stream_name='baseline') time temperature 0 2017-07-16 12:12:35.128515999 273 1 2017-07-16 12:12:40.128515999 274