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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.9 Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 8 Intro to Data Structures 147 8.1 Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 16 Time Series / Date functionality 377 16.1 Time Stamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

of pd.NA can still change without warning. For example, creating a Series using the nullable integer dtype: In [3]: s = pd.Series([1, 2, None], dtype="Int64") In [4]: s Out[4]: 0 1 1 2 2 Length: string. In [9]: pd.Series(['abc', None, 'def'], dtype=pd.StringDtype()) Out[9]: 0 abc 1 2 def Length: 3, dtype: string You can use the alias "string" as well. In [10]: s = pd.Series(['abc', None, dtype: string The usual string accessor methods work. Where appropriate, the return type of the Series or columns of a DataFrame will also have string dtype. In [12]: s.str.upper() Out[12]: 0 ABC (continues

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 5.9 Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 8 Intro to Data Structures 175 8.1 Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 16 Time Series / Date functionality 413 16.1 Time Stamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 CONTENTS CHAPTER ONE WHAT’S NEW IN 0.25.0 (JULY 18, 2019) Warning: Starting with the 0.25.x series of releases, pandas only supports Python 3.5.3 and higher. See Dropping Python 2.7 for more details (Deprecate groupby.agg() with a dictionary when renaming). A similar approach is now available for Series groupby objects as well. Because there’s no need for column selection, the values can just be the of aggregation is the recommended alternative to the deprecated behavior when passing a dict to a Series groupby aggregation (Deprecate groupby.agg() with a dictionary when renaming). See Named aggregation

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 6.9 Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 9 Intro to Data Structures 307 9.1 Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588 20 Time Series / Date functionality 589 20.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590 20.2 Time Stamps vs. Time Spans . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 2.1 structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 2.2.1 Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 . . . . . . . . . . . . . . . . . . . . . . . . . . 436 2.7.2 Database-style DataFrame or named Series joining/merging . . . . . . . . . . . . . . . . . 448 2.7.3 Timeseries friendly merging . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.1.9 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 2.1 structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 2.2.1 Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 . . . . . . . . . . . . . . . . . . . . . . . . . . 436 2.7.2 Database-style DataFrame or named Series joining/merging . . . . . . . . . . . . . . . . . 448 2.7.3 Timeseries friendly merging . . . .

2 CONTENTS CHAPTER ONE WHAT’S NEW IN 0.25.0 (JULY 18, 2019) Warning: Starting with the 0.25.x series of releases, pandas only supports Python 3.5.3 and higher. See Plan for dropping Python 2.7 for more (Deprecate groupby.agg() with a dictionary when renaming). A similar approach is now available for Series groupby objects as well. Because there’s no need for column selection, the values can just be the of aggregation is the recommended alternative to the deprecated behavior when passing a dict to a Series 4 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: powerful Python data analysis toolkit

Release 0.24.0 2 CONTENTS CHAPTER ONE WHAT’S NEW IN 0.24.0 (JANUARY 25, 2019) Warning: The 0.24.x series of releases will be the last to support Python 2. Future feature releases will support Python 3 only • New APIs for accessing the array backing a Series or Index • A new top-level method for creating arrays • Store Interval and Period data in a Series or DataFrame • Support for joining on two MultiIndexes currently experimental. Its API or implementation may change without warning. We can construct a Series with the specified dtype. The dtype string Int64 is a pandas ExtensionDtype. Specifying a list or