Skip to main content

The 3 Ms

It's been quite a while since I last had to calculate the mean, median or mode of any set of numbers. But here I am, giving myself a refresher on statistical calculations. I'm learning some basics with the help of W3 schools. They always break concepts down so well :) 

Calculating Mean
We can do this the hard way, which sucks and I'm lazy and is ridiculous when we're looking at incredible large sets of numbers anyway. So instead, we're going to do it the easy way using the NumPy module in Python.

import numpy
numSet = [ *lots of numbers *]
numSetMean = numpy.mean(numSet)

Calculating Median
This is even more annoying to calculate manually. You have to sort all the values from smallest to largest and search for the value in the middle. No one has time for that. Numpy can do this too.

import numpy
numSet = [ *lots of numbers* ]
numSetMedian = numpy.median(numSet)

Mode
This is just as troublesome to calculate as the median. You're trying to get the value that appears with the highest frequency. Meh. You have to use SciPy for this though.

from scipy import stats
numSet = [ *lots of numbers* ]
numSetMode = stats.mode(numSet)


Labels:

Comments

Post a Comment

Popular posts from this blog

Deviants in a normal world

It's definitely been a bit since I've seen this graphy. Anyone who has learnt about standard deviation knows this graph. Standard Deviation Standard deviation shows us how spread out all the values in a set are from the mean. The higher the standard deviation, the more spread out the values are over a wider range and the flatter this curve. In a normal distribution, most values are within 1 standard deviation from the mean(the green part of the graph). Apparently NumPy can calculate standard deviation too! import numpy numSet = [ *lots of numbers* ] numSetStdDev = numpy.std(numSet) Variance The variance also indicates how spread out the values in a set are. It measures the average degree to which each value differs from the mean. variance = standard deviation ^2 import numpy numSet = [ *lots of numbers * ] numSetVar = numpy.var(numSet) Source:  https://www.w3schools.com/python/python_ml_standard_deviation.asp
Post a Comment
Read more

I gotta feeling...

I've been helping a colleague with his portfolio site. He's making it retro video game themed at my suggestion. He found an interesting pixelated font called arcade classic  and used it for the headings on his page. Unfortunately, some of the letters almost overlapped, making it not quite readable. Before letter spacing I looked into typography ages ago and learnt about letter and word spacing and wondered if that was something that I could fiddle with using CSS. Turns out it is a property you can customise. I opened up Chrome Dev tools and added 3 pixels of letter spacing and it looked so much better. And there's letter spacing too, so that's pretty neat.  After letter spacing Can't say CSS is my favourite thing ever but it's always nice to learn something new in unexpected ways.
Post a Comment
Read more

Fizzbuzz

I was today years old when I found out what fizzbuzz was. Yes, I'm late to the party. I was in an interview where the interviewer mentioned that ordinarily they would ask interviewees in for a round of fizzbuzz challenges, as I know. Actually sir, no, I don't know 👀 But he sounded so certain that I must surely know what it is that I was afraid to say anything so I did what I always do when I panic. Look right back saying not a word. I googled this mysterious fizzbuzz problem:  It looks pretty easy. I don't think he meant this actual problem, but problems like this. Because this problem is way too easy to be an actual problem someone asks in an interview. I decided to work on it for fun:  Yup. Super easy. I wish this is all I were asked in an interview 😄 
Post a Comment
Read more