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

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

Snakes and ladders

I've started on my Python course. So far, the code has been familiar because the first few basic codes are similar to Javascript. And then modules happened. Confusion and despair! What is the world is 'if __name__ == "__main__": ' and why must I reach this section of my course on a public holiday when none of the instructors are in :( Stack overflow to the rescue, providing me a lifeline while I was drowning in a pit of serpents. I feel eternally indebted to a particular Mr Fooz. Picture from here From my understanding, when the Python interpreter reads a source file, it first sets the variable __name__ and then it executes all the code in the file. If that particular file that you are running(i.e. your module) is the main program, the interpreter will assign '__name__ = "__main__" '. Thereafter, any code in the aforementioned 'if' statement is run. If you have, instead, imported a module, the interpreter assigns '__name__ ...

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 😄

I do it for my cat

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