Time series data

Time series data is a first-class entity within pandas. Time adds an important, extra dimension to samples of variables within pandas. Often variables are independent of the time they were sampled at; that is, the time at which they are sampled is not important. But in many cases they are. A time series forms a sample of a discrete variable at specific time intervals, where the observations have a natural temporal ordering.

A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations that are further apart. Time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values rather than from future values.

A common scenario with pandas is financial data where a variable represents the value of a stock as it changes at regular intervals throughout the day. We often want to determine changes in the rate of change of the price at specific intervals. We may also want to correlate the price of multiple stocks across specific intervals of time.

This is such an important and robust capability in pandas that we will spend an entire chapter examining the concept.