Skip to main content

Processing user input before saving

Post bootcamp, I didn't even think I could get back into programming. I wasn't confident getting a real programming job, but thankfully, a position opened up at my school for an instructor. I spend a lot of time nagging students about completing their work, but I also get to see their submissions and help them out when they're stuck. This has given me the opportunity to play around with their code without the stress of deadlines. Especially now that I'm handling the Python project. I feel extremely grateful for this opportunity, but I am well aware that I have a long way to go. 

A student was looking into using the timefield for his model and wanted to calculate the difference between the 2 user inputs and save it. He later changed his mind about it, but it gave me the opportunity to look into how this can be done.  I took a long time to figure this out, but I'm pleased I did :) 

form = MyForm(request.POST or None)     
if form.is_valid():
updatedForm = form.save(commit=False)
#allows us to make changes to the user input
starttime = updatedForm.Start_Time
endtime = updatedForm.End_Time
date = updatedForm.Date

startdatetime = datetime.datetime.combine(date
, starttime)
enddatetime = datetime.datetime.combine(date
, endtime)
delta_timediff = (enddatetime - startdatetime)
#creates a DateTimeDelta instance
timediff = datetime.datetime.strptime(str(delta_timediff), "%H:%M:%S")
updatedForm.Time_Taken = timediff
updatedForm.save()



Comments

Popular posts from this blog

Certified CSS Surgeon

I've just been certified as a top notch CSS Surgeon 😹😹😹. Codepip has just officially launched and they're offering CSS Surgeon for free until the 1st of September. I've already finished Flexbox Froggy(Free) and Grid Garden(Also free). I really do enjoy the CSS games on this website. It has helped me better understand CSS. There is still so much to learn. I've been trying to create a simple language learning website and it has been a struggle. I'm using the most simple website layout and yet it's just kicking my butt(I'm looking at you Nav bar). I hope to add some Javascript to make it more interactive in the future. At one point I really wanted to throw in the towel because it felt like I would never get this. It's hard not to give up when there are so many hurdles to cross. Can I really be confident at coding after just a 6 month bootcamp?

Portfolio on GitHub

As part of the bootcamp, I've been building a portfolio website based on a template provided by the school. Honestly, I hate the template. It is hideous. But I guess it's a great starting point for someone who is unsure about what to do. It was also an opportunity for me to practice publishing a website online. Unfortunately, I had to pay for a webhosting service. After submitting my third update to my portfolio website, one of the instructors suggested that I could use GitHub Pages instead. *mindblown* This is amaziiiiing. And it's so simple. When I finally get down to creating my own portfolio from scratch, I am definitely going to put it up on GitHub instead. Also, speaking of GitHub, I'm slowly understanding how GitHub functions. It is taking me awhile. Especially because I don't use many of the functions that are available. I look forward to learning more and becoming a GitHub extraordinaire :D JK

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