Python Chapter 11 time series

The time series （time series） Data is an important form of structured data , It can be used in many fields , Including finance 、 economics 、 Ecology 、 neuroscience 、 Physics, etc . Anything observed or measured at multiple time points can form a time series . A lot of time series are fixed frequency , in other words , Data points appear regularly according to some rule （ Like every 15 second 、 Every time 5 minute 、 Once a month ）. Time series can also be irregular , There are no fixed time units or offsets between units . The significance of time series data depends on the specific application scenario , There are mainly the following ：

• Time stamp （timestamp）, Specific moments .
• Fixed period （period）, Such as 2007 year 1 Month or 2010 In the year .
• The time interval （interval）, Represented by start and end timestamps . period （period） It can be seen as a gap （interval） The special case of .
• Experiment or process time , Each point in time is a measure relative to a specific start time . for example , From the time you put it in the oven , The diameter of the biscuit per second .

This chapter is mainly about 3 Time series . Many techniques can be used to deal with experimental time series , Its index may be an integer or floating point number （ Indicates the time elapsed since the beginning of the experiment ）. The simplest and most common time series are indexed with time stamps .

Tips ：pandas Also support based on timedeltas The index of , It can effectively represent the experiment or elapsed time . This book does not cover timedelta Index , But you can learn pandas Documents （http://pandas.pydata.org/）.

pandas It provides many built-in time series processing tools and data algorithms . therefore , You can efficiently handle very large time series , Slice easily / cutting 、 polymerization 、 On a regular basis / Irregular time series