vkalavri@bu.edu CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/06: Notions of time and progress Vasiliki Kalavri | Boston University 2020 Mobile game application • input stream: Vasiliki Kalavri | Boston University 2020 • Processing time • the time of the local clock where an event is being processed • a processing-time window wouldn’t account for game activity while the train Event time • the time when an event actually happened • an event-time window would give you the extra life • results are deterministic and independent of the processing speed Notions of time 5 Vasiliki

allow “M”, “y”, or “Y” for the “unit” argument (GH23264) • Removed the previously deprecated keyword “time_rule” from (non-public) offsets.generate_range, which has been moved to core.arrays._ranges.generate_range() toolkit, Release 1.0.0 • Bug in DataFrame.rolling() not allowing rolling on monotonic decreasing time indexes (GH19248). • Bug in DataFrame.groupby() not offering selection by column name when axis=1 contributed patches to this release. People with a “+” by their names contributed a patch for the first time. • Aaditya Panikath + • Abdullah ˙Ihsan Seçer • Abhijeet Krishnan + • Adam J. Stewart • Adam

argument to disambiguate DST transition times (GH25017) • DataFrame.at_time() and Series.at_time() now support datetime.time objects with time- zones (GH24043) • DataFrame.pivot_table() now accepts an observed (GH2936, GH2656, GH7739, GH10519, GH12155, GH20084, GH21417) Now every group is evaluated only a single time. In [20]: df = pd.DataFrame({"a": ["x", "y"], "b": [1, 2]}) In [21]: df Out[21]: a b 0 x 1 (continues queries. IntervalIndex methods previously matched on any overlapping Interval. Behavior with scalar points, e.g. querying with an integer, is unchanged (GH16316). In [42]: ii = pd.IntervalIndex.from_tuples([(0

argument to disambiguate DST transition times (GH25017) • DataFrame.at_time() and Series.at_time() now support datetime.time objects with time- zones (GH24043) • DataFrame.pivot_table() now accepts an observed (GH2936, GH2656, GH7739, GH10519, GH12155, GH20084, GH21417) Now every group is evaluated only a single time. In [20]: df = pd.DataFrame({"a": ["x", "y"], "b": [1, 2]}) In [21]: df Out[21]: a b 0 x 1 (continues queries. IntervalIndex methods previously matched on any overlapping Interval. Behavior with scalar points, e.g. querying with an integer, is unchanged (GH16316). In [42]: ii = pd.IntervalIndex.from_tuples([(0

changed. • --verbose (or -v): Normally, the webservice outputs only brief messages to the console each time a request is served. With this option, the webservice prints much more detailed data about every request not to an ordinary PC.) Also, two processes must not change settings at the same time, or start a machine at the same time. These requirements are implemented in the Main API by way of “sessions”, in particular eventually cause it to deny service. To reiterate: The underlying COM object, which the reference points to, is only freed if the managed object reference is released. It is therefore vital that web service

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 2.17 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734 2.17.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 2.17.3 Converting

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 2.17 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734 2.17.2 Timestamps vs. time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 2.17.3 Converting

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715 2.14 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 2.14.2 Timestamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 2.14.3 Converting to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 730 2.14.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 2.14

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718 2.14 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 2.14.2 Timestamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724 2.14.3 Converting to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733 2.14.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 2.14

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716 3.14 Time series / date functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 3.14.2 Timestamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 3.14.3 Converting to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731 3.14.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 3.14