Thanks for this awesome tutorial. It's just what I needed to jump start my use of NumPy and I like how you followed a general flow with the NumPy : the Absolute Basics for Beginners documentation. I just learn better through visuals so this was perfect. I especially appreciated when you gave some insights as to when you'd use a certain method, like with indexing and filtering. I'm new to DS and ML so that added context is awesome! More of that please! Thank you!!!
I liked this video a lot. I followed it by coding with you and also using GPT4 to fill my mathematical gaps. I especially liked the linear system example.
Excellent tutorial, thanks! Just one comment: you refer to some resources that should be linked in the descriptions (e.g., data loading tutorial), but are not there. Maybe you could add a comment saying where they can be found now.
Wonderful lecture. Just have one doubt, I dont understand the use of newaxis, since we already have reshape that can achieve the same results.Is there an example maybe where newaxis is used and reshape cant be used to achieve the same results?
the more i learn, the more i am appreciate about learning, the more i can put learning to good use.... and the more quality of life and ongoing strengthening and growth of the individual i experience. it is almost as if my time is worth nowhere near a fraction of yours, like some kind of silly goose ... i consider myself now dumber than 6 years ago. that is how much growth ive experienced.
At 18:10, it would have been nice if you explained what exactly the (2,) stood for when we have just one dimension: it seems like it stands for two rows and no columns, if you go by the syntax for the shape of multidimensional arrays. But that would mean it's an array "standing up", which is the transpose of what we typed in. So i suspect it means no rows and two columns, i.e an array "lying down", i.e. exactly what we typed in. Also, any idea why the transpose is just an attribute of the object i.e. a.T, whereas the diagonal is a full numpy function called on the object a i.e np.diag(a). To a beginner, or even intermediate user, it seems totally random and inconsistent when one uses methods and when one use functions, and also whether those functions are directly from the module np. e.g. np.diag or from an intermediate object e.g. np.linalg.det etc. It amounts to committing everything to memory. I thought the whole point of coding was that it was more elegant and consistent than this.
At 41:20, thanks for pointing out, as an aside, how something like a.max() can also be used as a function np.max(a). For the life of me, though, I can't understand why there are two ways to do operations like these in python especially since they do exactly the same thing. I mean, both leave the a untouched (they don't change it in place) so I don't know why np.max(a) with reassignment isn't the unique way of doing it.
Hello, thank you so much for the video; really useful and helpful! Quick question please: at 21:21, why is the determinant not exactly equal to -2? I am thinking it has to do with the int64 property, but I am not sure.
Excellent video! I learnt so much. I did have a question thought. In the section for data types you gave us an example with a 1D array. Suppose we need to force the datatype for a 2D array, what would be the syntax in that scenario? I tried yours but it did not take.
@@Kig_Ama check if pycharm is 32-bit. I checked numpy documentation and it said "The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence." You can always force to int64 though.
@@mohsinansari8271 My pycharm should be 64 bit, at least thats what it says when I look it up by clicking About in the menu Help. Could it be that this is some weird windows issue? I found this here at stackoverflow: _"Default integer type __np.int__ is C long....But C long is int32 in win64."_
@@patloeber hey, hope you didn't take this as offensive. Coincidentally, I was bored and I started watching Conan's(comedian) videos of berlin. From there I got the idea. BTW, I love Germany, its a dream destination for me. Love and peace from India. And I apologize if you find it offensive.
i think this is a fantastic use of python. the learning has been learned, no need for anybody else to be impressed with learning. we need more coal miners :)
Topics not that clear - slicing of 2d arrays, np.newaxis, np.random(random vS randn - especially the 'meaning of distribution'), linalg part(involving eigenvalues and allclose) module
If you want to play around with the code, you can find my notebook here: github.com/patrickloeber/python-engineer-notebooks
Thank you so much
School is a waste of time when youtubers like him exist!
hehe thanks :)
school is school cant compare, how can u get organized lesson on youtube for all subs, how you know which youtuber does it all?
Lol, that anger 😂
@@patloeber Sir, the handbook is not available.
Spent some hours to watch all content. It really helps a lot!!! Thank you!!
thank you for this video that was a good tutorial for those who have never worked with this library before
Thanks for this awesome tutorial. It's just what I needed to jump start my use of NumPy and I like how you followed a general flow with the NumPy : the Absolute Basics for Beginners documentation. I just learn better through visuals so this was perfect. I especially appreciated when you gave some insights as to when you'd use a certain method, like with indexing and filtering. I'm new to DS and ML so that added context is awesome! More of that please!
Thank you!!!
Thanks! Really glad you like it :)
I liked this video a lot. I followed it by coding with you and also using GPT4 to fill my mathematical gaps.
I especially liked the linear system example.
I have seen lots of tutorials of Numpy but I must say this one is just Amazing. Keep it up :)
Thanks! That was clear and to the point! Good crash course!
Thank you :)
Amazing tutorial. Best use of my time. I was always apprehensive about getting started with python. This is exactly what I needed.
I love the dark background, easy to stare at the screen and concentrate.
glad you like it!
this is the best u can get in an hour !!! thanks man for letting your heart out.
Love it. Feels like Señor chang is teaching!
thanks to fantastic use of technology and resources... now the entire planet feels really good about whats to come. good for you
thank you. watched this video in one take , felt great!
Many thanks for this. Absolutely amazing coverage and explanation.
Glad you enjoyed it!
28:58 needs better explanation
Man, that's just wonderful to find this linear system notation, and numpy linear functions
glad you like it!
Very helpful! Thank you for making this!
Excellent tutorial, thanks! Just one comment: you refer to some resources that should be linked in the descriptions (e.g., data loading tutorial), but are not there. Maybe you could add a comment saying where they can be found now.
Thank you very much. This tutorial is perfect for a matlab programmer to learn numpy very quickly.
first time enjoyed a tutorial this much
Really glad to hear that!
Thanks a lot for this free video!!
Very practical and to the point explaination ! thanks a lot
Wonderful lecture. Just have one doubt, I dont understand the use of newaxis, since we already have reshape that can achieve the same results.Is there an example maybe where newaxis is used and reshape cant be used to achieve the same results?
Huge thanks for such a concise video.
the more i learn, the more i am appreciate about learning, the more i can put learning to good use.... and the more quality of life and ongoing strengthening and growth of the individual i experience. it is almost as if my time is worth nowhere near a fraction of yours, like some kind of silly goose ... i consider myself now dumber than 6 years ago. that is how much growth ive experienced.
Thank you Patrick, it is a very well made video. Learned a lot of useful numpy actions.
This is brilliantly explained! Thanks alot!! 👍🏼
thanks :)
great summary, thank you!
Very welcome!
At 18:10, it would have been nice if you explained what exactly the (2,) stood for when we have just one dimension: it seems like it stands for two rows and no columns, if you go by the syntax for the shape of multidimensional arrays. But that would mean it's an array "standing up", which is the transpose of what we typed in. So i suspect it means no rows and two columns, i.e an array "lying down", i.e. exactly what we typed in.
Also, any idea why the transpose is just an attribute of the object i.e. a.T, whereas the diagonal is a full numpy function called on the object a i.e np.diag(a).
To a beginner, or even intermediate user, it seems totally random and inconsistent when one uses methods and when one use functions, and also whether those functions are directly from the module np. e.g. np.diag or from an intermediate object e.g. np.linalg.det etc.
It amounts to committing everything to memory. I thought the whole point of coding was that it was more elegant and consistent than this.
At 41:20, thanks for pointing out, as an aside, how something like a.max() can also be used as a function np.max(a). For the life of me, though, I can't understand why there are two ways to do operations like these in python especially since they do exactly the same thing. I mean, both leave the a untouched (they don't change it in place) so I don't know why np.max(a) with reassignment isn't the unique way of doing it.
What interface is he using for this? I am using jupyter notes but it doesn't show drop down list for all the commands when you type.
exponentially valued content! 🎉
Awesome course..must to go with🔥🫶🫶
You saved me ! Thanks a lot.
Liebe Grüße aus Magdeburg
Danke :) Grüße zurück
Thank you so much for the crash course👌🏻
Sure! Glad you enjoyed it
Thanks
Enjoyed numpy crash course, any possible to get a torch crash course ?
thanks! For now I have a full beginner course. Maybe I add a little crash course, too...
28:58 couod explain step by step what .argwhere outputs, and what .flatten dies to that output
Thank you a lot my friend. May God lead you to the right path.
9:21 the error was because of the numpy, am I right?
Great tutorial, still watching it!
What application is he using to write the code?
Thanks for this Numpy Crash Course Sir.
It's really great, and you have explained very well in this tutorial.😊
You are awesome 🌟
Thanks for your fantastic video. You have an immense talent for teaching.
Hello, thank you so much for the video; really useful and helpful! Quick question please: at 21:21, why is the determinant not exactly equal to -2? I am thinking it has to do with the int64 property, but I am not sure.
Excellent video! I learnt so much. I did have a question thought. In the section for data types you gave us an example with a 1D array. Suppose we need to force the datatype for a 2D array, what would be the syntax in that scenario? I tried yours but it did not take.
it would be like this:
import numpy as np
a = np.array([[1,2,3],[4,5,6]],dtype=np.int32)
print(a.dtype)
thank you for your awesome video bro 😘
I have Numpy version 1.25.1. So why do I get int32 for dtype. I am just starting numpy
many thanks buddy.
from my end. instead of appending, a + np.array(4) adds 4 to each element in my NumPy array. Why not using np.append(a,4)?
it seems you are very kindhearted you make subject very love able thanks please upload pandas tutorial too.
Thanks 😊
Great video! I was wondering what was the difference between the fancy indexing and using np.argwhere? it seemed that both get the considered indices
fancy indexing can take a list of all indices. argwhere takes a condition..
(base) -MBP ~ % import numpy as np
zsh: command not found: import
I am getting this massage. How to slove?
Thanks. Why autocomplete does not work on objects returned from Numpy method?
Very helpful. Thanks a lot. All concepts in a nutshell. God Bless yoo.
thanks :)
Great tutorial. By the is there a reason why u don't use pycharm as IDE?
both are great :)
Thank You!
thanks for your time for us
Sure :)
Greetings! Why use hstack or vstack instead of concatenate? Concatenate seems to do the job for both of those.
yes indeed ! I guess programmers like simpler functions, same with np.zeros and np.ones that could be replaced with np.full
This feels like excel but with more steps. Noob here, Any reason we cant do these things in Excel?
At 29:29 , why not simply do print(a[a%2==0])
Thank you sir ..
13:22 Sir, why we place print(dot) outside for loop?
Which notebook is he using?
thank you sir
you're welcome
Nice tutorial. Can we get the code used in crash course (maybe some github link)?
there you go :) github.com/python-engineer/python-engineer-notebooks
@@patloeber thank you!
19:02 : why do the rows not display as 2 and not '3'. Not sure how that make sense but the column number shows as 3. Anyone care to explain?
a.shape gives the output (2,3). where 2 is the number of rows and 3 is the number of columns in a,
[ 1 2 3 ] row 1
[ 3 4 8 ] row 2
Do u have a video for pandas?
5:40 I made it exactly the same way u did, but I get int32 instead of int64. My version of numpy is 1.19.3. Why did u get int64 and I get int32?
possibly because you are either using 32-bit OS or installed 32-bit version of software running python script
@@mohsinansari8271 Ty mohsin. Actually I have a Win 10 64-bit PC. What do u mean by software? I am using Python 3.9, my ide is pycharm.
@@Kig_Ama check if pycharm is 32-bit. I checked numpy documentation and it said "The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence." You can always force to int64 though.
@@mohsinansari8271 My pycharm should be 64 bit, at least thats what it says when I look it up by clicking About in the menu Help. Could it be that this is some weird windows issue? I found this here at stackoverflow: _"Default integer type __np.int__ is C long....But C long is int32 in win64."_
@@Kig_Ama Yes probably some OS related issue. Dont bother too much. Its not an actual "issue" though.
thank youuu
13:40 using range + index to loop iterables is not pythonic... in this case zip() function allows looping two lists in the same time.
الف شكر صراحه انت مبدع
thank you very much
You are welcome
so in indexing slicing,
we never take the first element into account?
of course you can take the first element into account. x[0:4] gives you elements 0, 1, 2, and 3
Genius!
Thanks!!
Please! What extensions or plugins you use in visual studio code for python?
I'll make a video about this in the next few days :)
Kindly do a video for Pandas also like this.
Will definitely be added in the future :)
@@patloeber Thanks
I almost complete the practicing today.....
thanks. 😍😍
Are you German cause Your accent is kind of one. Well thanks for this amazing crash course.
Yep I’m German. Can’t hide my accent 😅
@@patloeber hey, hope you didn't take this as offensive. Coincidentally, I was bored and I started watching Conan's(comedian) videos of berlin. From there I got the idea. BTW, I love Germany, its a dream destination for me. Love and peace from India. And I apologize if you find it offensive.
No worries :) yeah you should come visit
generating arrays
why didn't i know this guy before....
Thanku SO mUCh..Please add Pandas too..
yes this is on the list
Why wasn't list in Python developed the way Numpy works?
Both work differently
also list is quick when dealing with small data whereas NumPy is quick at dealing large data
@@hallo-xp2wh ty!
i think this is a fantastic use of python. the learning has been learned, no need for anybody else to be impressed with learning. we need more coal miners :)
32:55 😀
Pandas Tutorial plz
Will definitely come in the future!
Good but you could explain these complicated math things
I'm practicing .......
Great content. No offense but u sound like male Janice.
This course is amazing or not
Hey, how can I talk to you? Can you give your Telegram?
not yet. I have Twitter. But I play to add another chat messenger in the future.
У канала selfedu в разы качественнее курс по NumPy
Topics not that clear - slicing of 2d arrays, np.newaxis, np.random(random vS randn - especially the 'meaning of distribution'), linalg part(involving eigenvalues and allclose) module
Thank you!