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Defined observance rules are: Pandas has a simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data).
Naively upsampling a sparse series can potentially generate lots of intermediate values.
When you don’t want to use a method to fill these values, e.g.
Note that since we converted to an annual frequency that ends the year in November, the monthly period of December 2011 is actually in the 2012 A-NOV period.
Period conversions with anchored frequencies are particularly useful for working with various quarterly data common to economics, business, and other fields.
with a partial string depends on the “accuracy” of the period, in other words how specific the interval is in relation to the frequency of the index.
In contrast, indexing with datetime objects is exact, because the objects have exact meaning.
These also follow the semantics of class which can be used to create customized business day calendars which account for local holidays and local weekend conventions.
As an interesting example, let’s look at Egypt where a Friday-Saturday weekend is observed.
Note The frequency string ‘C’ is used to indicate that a Custom Business Day Date Offset is used, it is important to note that since Custom Business Day is a parameterised type, instances of Custom Business Day may differ and this is not detectable from the ‘C’ frequency string.
The user therefore needs to ensure that the ‘C’ frequency string is used consistently within the user’s application. This is because one day’s business hour end is equal to next day’s business hour start.
For example, under the default business hours ( - ), there is no gap (0 minutes) between is the only calendar that exists and primarily serves as an example for developing other calendars.
For holidays that occur on fixed dates (e.g., US Memorial Day or July 4th) an observance rule determines when that holiday is observed if it falls on a weekend or some other non-observed day.