#
Python 3, an illustrated tour Transcripts

Chapter: Numbers

Lecture: Statistics

Login or
purchase this course
to watch this video and the rest of the course contents.

0:01
In this video we're going to talk about the new statistics module

0:03
that came out in Python 3.4, this was introduced in pep 450.

0:07
From the pep we read, even simple statistical calculations

0:10
contain traps for the unwary,

0:12
this problem plagues users of many programming languages, not just Python

0:17
as coders reinvent the same numerically inaccurate code over and over again.

0:22
Here's an example of some of the issues that someone might run into

0:25
when trying to implement some numerical code.

0:28
This is a simple function for calculating the variance.

0:31
That's the change of values over a sequence of numbers

0:36
how much they vary and here we are just calculating

0:40
the sum of the squares minus the square of the sums

0:45
and dividing by the numbers

0:47
so down below here, after we've defined variance

0:49
we pass in a list of numbers and we get the variance

0:52
and we say it's 2.5. It seems to be fine.

0:55
The problem is when we add a large number to that

0:58
here we're adding 1e to the 13th

1:01
and we're getting numbers that still should have the same variance

1:06
because the difference between them is still between 1 and 5.

1:09
And when you run that into our calculation here

1:12
you get a large negative number

1:15
and this illustrates some of the floating-point issues

1:18
that you might run into with simple naive calculations.

1:21
And so the impetus of this pep is to help deal with some of these issues

1:26
and provide a pure Python implementation of some common statistical functions

1:30
that don't have these sorts of issues.

1:34
Here we're showing an example of using the library.

1:37
We simply import it, it's called statistics,

1:39
and inside of there, there are various functions.

1:41
One of them is variance.

1:43
We look at the variance of our same data

1:45
and we get 2.5, we add 1e to the 13th for each of those numbers

1:50
and we still get 2.5.

1:52
There are various functions included in here.

1:54
I'm not going to go over them, but you can look at the function

1:56
and if you're dealing with statistical problems,

1:59
you can use this code if you need to.

2:02
Other nice thing to do is just to use the code to look at it

2:05
and glean some insights on how you might do numerical processing code in Python

2:10
and deal with some of these issues.

2:13
This module is written in pure Python

2:15
and so you can simply load the module up and inspect it

2:19
and see what tools and techniques they're using.