Solving Equations Symbolically And Using LaTeX In Python! 

As you can see in your code, you defined it but didn’t even use it?

I’ve not noticed any slower speed impacts whilst using it.

Just import the necessary objects, in that case

i have the same problem with spyder. Now i have to find out how to do it there....

Perfection is unrealistic But he's nice

i am looking for the same thing;do you know something about this now?

Same! Did yoy guys find a solution or something??

It’s been a year since you asked this question so you’re probably a tkinter master now. I have a project I’m working on and could use a tkinter expert. If you’re interested, you can shoot me an email at programmer3.7@outlook.com

You may install Pylatex for creating.tex file.

I finally started learning to code in python: 7 months later 👍🏾

@@umaoio312 You might like to take a look at scipy lectures, here: www.scipy-lectures.org/ . Those helped me a lot. Section for bare-bones beginners isn't much, but the overview of numpy, scipy and plotting modules helped me so much it's not even funny.

I learned during my first internship.

or just press '1' :)

There are actually a lot of videos you can watch for resume help, (quite a few that I've seen recently myself) but if it's really specific to a certain field, especially if that field is physics or math, I think asking Andrew is your best bet

Timothy Basaldua I appreciate the feedback!

For me, python is a lot more intuitive than Matlab. If u hate the abbreviations like np, sp and want it matlab style, u can simply say "from numpy import * ". Then u can directly say pi, exp, sin...etc. But those abbreviations are a feature, not a bug. They are namespaces used to seperate different packages used for different purposes. It's super weird to have everything in one namespace, like matlab (or from numpy import *) Matlab, i need to keep the docs open and read them for 15 mins before applying some new function. There's zero consistency. No objects, everything in same namespace, stateful programs, god I hate it... I think it was built in a time where no other way was there to do it. And scientific people hated learning programming properly. So, matlab just gave them a bunch of functions they can use. Say they made a function for a 2d vector. Now they want the same thing for a 3d vector. So they slapped *3d behind the functions name and created a new function. Doing the same with numpy is a bliss. You guess like "this might work" and type a new syntax (like a[a[...,5]>5] ) and it works. It's so intuitive. Numpy extends python's design philosophy. I'm in love with that API. We were given an assignment on signal estimation, not that hard. A transmitter sends some signals (a bit vector) to the receiver. That gets corrupted by gaussian noise. We need to create a bunch of estimators and guess what was sent. It's all on matlab. Need to repeat the experiment changing vector size (N), signal vocabulary....etc. like 4,5 parameters. Friends ended up doing it with a nested for loop 5 steps deep in matlab. For every parameter, for every other parameter. Because matlab natively supports only 2d arrays. And most functions work only on 2d arrays. I decided to do this in numpy. In numpy, i created a 6 dimensional tensor (6d array), with each parameter on each dimension. I added noise and stuff to different dimensions using broadcasting, crunched certain dimensions and like 20 lines later i have a 2D array that I can plot using matplotlib to get the performance of estimator changing with vector size and all. It was a breeze. And it's fast even when u increase repetitions of experiment to a million or something. The whole thing is readable as well... It's elegant.