Users of the statistics module, how often do you use it with
heterogeneous data (mixed numeric types)?
Currently most of the functions try hard to honour homogeneous data,
e.g. if your data is Decimal or Fraction, you will (usually) get Decimal
or Fraction results:
>>> statistics.variance([Dec
Hi Steve
Today's XKCD is on 'Selection Bias' and it is set in a statistics
conference: https://xkcd.com/2618/
According to its PEP the statistics module provides "common statistics
functions such as mean, median, variance and standard deviation".
You ask "if you are a user of statistics, how imp
IMHO, mixing custom types in this context is usually not required, as long
as at least int-to-anything-else typecast is possible. Currently it's done
only when there is at least one non-int and when the result can't be
represented as int, that is:
>>> statistics.mean([1, 2, 3, 6])
3
>>> statistics
On Fri, 13 May 2022 at 00:20, Steven D'Aprano wrote:
>
> If you are a user of statistics, how important to you is the ability to
> **mix** numeric types, in the same data set?
>
> Which combinations do you care about?
>
I'm only a very small-time user of it, but the only combination I use
is int
On 13May2022 00:17, Steven D'Aprano wrote:
>Users of the statistics module, how often do you use it with
>heterogeneous data (mixed numeric types)?
Disclaimer: I am not yet a user of the statistics module.
>With mixed types, the functions usually try to coerce the values into a
>sensible common
I would like to propose a new type of pattern for structural pattern
matching. A motivation will be described later below.
The pattern may be written in a form like
{}
(with braces), where is either a literal pattern
or an identifier (or "NAME" in the structural pattern matching
terminology
