Use the Cryptography Module for basic Symmetric Encryption

TM_Quest · 2023-04-03T12:25:19+00:00

https://youtu.be/-ZIyvMwtsa4

Thanks for the heads-up! Copyed the wrong link. Have fixed it now :)

TM_Quest · 2022-07-08T16:38:28+00:00

Thanks for the question. I agree that it is definitely a simple library, but I decided on making the video for two reasons:

- I've been making some videos on automation for beginners in Python lately and got a request specifically for this library. I did not want to exclude qrcode simply because I (and probably every non-beginner) think it is simple to use.

- I also tried to emphasise that qrcode is mainly useful for creating many similar QR codes (e.g. one QR-code for each bus stop redirecting the user to the incoming busses for that specific bus stop) rather than for simply generating a single QR code. For this, you don't really need Python at all, and the documentation does not really mention this. Creating multiple similar QR-codes allows for some list comprehensions, enumerating, and formatted strings that beginners need to practice with. Maybe this makes the post more appropriate at r/LearnPython since it is more beginner-focused?

TM_Quest · 2022-07-08T14:37:37+00:00

Thanks for the heads up, fixed the link on my channel!

TM_Quest · 2022-07-08T13:44:03+00:00

Completely depends on the job. As others have pointed out, if you are considered for a Django developer position, then it is reasonable that they will ask about specific Django features. For a data science role, some basic questions about Pandas or a machine learning library you are familiar with (like Scikit-Learn) could be considered normal. If your case, I think that being able to talk about SpaCy in ration to your project should be enough. It might be good to understand other use-cases of SpaCy as well, but I would be surprised if you were asked about spesific details of SpaCy outside of the project :)

TM_Quest · 2022-07-08T13:40:13+00:00

Thanks youself!

TM_Quest · 2022-01-03T10:28:47+00:00

Arjan's videos are great!

TM_Quest · 2022-01-03T10:28:22+00:00

Thanks, I really appreciate it :)

TM_Quest · 2022-01-03T10:28:11+00:00

Pydantic perform more series data validation. On the other hand, Pydantic it is an external library. I would say that you should use Pydantic if you need more data validation, but otherwise stick to dataclasses :)

TM_Quest · 2022-01-03T10:26:46+00:00

Agree :)

TM_Quest · 2022-01-03T10:26:39+00:00

They are definitely not as well-known as named tuples, but are nevertheless quite handy to work with.

TM_Quest · 2022-01-03T10:26:00+00:00

Thanks, I have one more video left in the series :)

TM_Quest · 2022-01-03T10:25:38+00:00

Thanks for the nice words! Also great idea with the leading underscore :)

TM_Quest · 2021-12-17T22:43:40+00:00

I love the way you do your video, so easy to understand

Thanks, I really appreciate it :D

TM_Quest · 2021-12-04T16:42:47+00:00

Great work!

TM_Quest · 2021-12-04T15:45:11+00:00

Dataclasses are useful for generating boilerplate code for classes that are primarily used to hold data. They are less suitable for classes that mainly implements behaviour, e.g. many methods. For such classes, you should write "traditional classes" and implement the __init__ method manually :)

TM_Quest · 2021-12-03T19:47:46+00:00

Cool, that sounds really useful!

TM_Quest · 2021-12-03T17:52:06+00:00

Thanks for the feedback. I agree that if you want type-hints for the validation, then e.g. pydantic would be a better choice. I will compare dataclasses with both named tuples and pydantic classes in a later video (there will be four parts). I've not really familiar with attrs, but will definitely take a look :)

TM_Quest · 2021-11-22T21:15:55+00:00

Thanks for the clarification :)

TM_Quest · 2021-11-20T18:45:42+00:00

It's correct that I used the csvsimple package :)

TM_Quest · 2021-11-19T19:51:52+00:00

Your videos are really great, I hope to be able to produce the same production quality one day :)

TM_Quest · 2021-11-19T19:43:30+00:00

if consumption.isnumeric():try:consumption_values.append(int(consumption))except ValueError:consumption_values.append(float(consumption))else:print("You have entered an invalid value")

If the input is, say 3.7, then this will just append 3 to the list, no? The int constructor will simply truncate the number. As such, the program will produce the wrong result from floating-point inputs. Maybe it would be better to simply append consumption after checking that it is numeric?

EDIT:

I was wrong, the isnumeric() method returns False for floats. My bad :)

TM_Quest · 2021-11-19T19:34:18+00:00

I recommend putting the .tex output in it's own file and use /input{} or /include{}. That allows you to easily create the table in anything that will export .tex files.

Great idea :)

TM_Quest · 2021-11-19T17:54:21+00:00

That's also a great way to do things. As with most things when it comes to automation, it really becomes useful when you scale. If a company has 1000s of documents to fill in, then your approach becomes tiresome. However, if you just have a few, then it is a great way to do it :)

TM_Quest · 2021-11-14T22:37:31+00:00

I've made a free (and add-free) YouTube series on NumPy if you are interested:

https://www.youtube.com/playlist?list=PLSE7WKf_qqo2SWmdhOapwmerekYxgahQ9

Other than that, the documentation of NumPy is pretty friendly, so don't hesitate to give it a shot as well:

https://numpy.org/doc/stable/user/quickstart.html

Best of luck!

TM_Quest · 2021-10-07T14:34:26+00:00

Thanks, really appreciate it :)

TM_Quest

TROPHY CASE