Can we please invite Alex back to do more lectures? This is the BEST numpy tutorial I’ve found online!! This guy seriously needs his own TH-cam channel.
Absolutely amazing lecture. Much recommended for complete beginners. A chilled-out guy teaching calmly, explaining every concept properly, I just loved this!
It's seriously a great lecture and interesting too. I had some doubts before this lecture, now they are cleared. Thank You, Professor Alex Chabot-Leclerc.
This was very well done and very useful (despite the mishap with the left side of the screen 😊). Thank you also for including the part about memory allocation in numpy. It helped me finally understand when (and why) I can expect to get a view vs. a copy of an array. It didn't even seem like an advanced concept, but that's because Alex did a great job explaining it.
So you need someone who (probably understands the topic and) fills in the blanks for you? Or just someone who points you to the location of the tutorial materials, noted in the video summary. (Just to find that the link is dead...) Or some random Internet girl with google? github.com/enthought/Numpy-Tutorial-SciPyConf-2019 Have fun coding:)
Hi everyone, in the solution file, this line is for finding the identifier for each month: months = (wind_data[:, 0] - 61) * 12 + wind_data[:, 1] - 1, but what is the logic for this line? Thank you
How could he do the math of the difference of the blurred image and the original since they have different shapes? blurred dim = (459, 613) and original = (461, 615)
Its not evil 37.00 its simple you complicate it unnecessarily. Just explain because you used b=a... #whatever after a you asigned a to be so both are the same
Could you help me to solve this type of issue....your valuable reply help me a lot..... Traceback (most recent call last): File "/home/PASTA_v1.0/Code/pasta.py", line 37, in from NEB import pot_frc ## NEB/CI-NEB step File "/home/PASTA_v1.0/Code/NEB/pot_frc.py", line 41, in frc_img = tag.read_force(file_out,n_atm) File "/home/PASTA_v1.0/Code/Interface/interface_siesta.py", line 217, in read_force tmp.append(eval(w[2])) IndexError: list index out of range
Just in case someone's reading this one year later ;-) The element with index 3 is 13, but it's not included in a[1:3], or in a[1,-2]. Remember that the right hand side boundary of the slice isn't included, so a[1:3] means the elements with indices 1 and 2. The same goes for a[1:-2], it's an array consisting of the elements starting at a[1] and ending at the element *before* a[-2]. Alex did mention this early on in the talk, and it's consistent with the behaviour of ranges in python. It might be easiest to think of [1:3] as "really" being [1,3), in the spirit of a half-closed, half open interval.
Starts with simple things in numpy explaining every detail of every function, sign, letter... 46.00 Jumps to image processing with plenty of new data and relations in-between asking to do processing work lmao
AutoCAD is better at reshaping the envirnment, with something other than numbers to look at As an incentive design engineer I do this often while recreating something never done before
44.00 lol complicating again. Python was made for simplicity in use so simply because there are square brackets it skips need for naming funcions you would need to call otherwise :)
This guy is a perfect professor. I really enjoyed his lecture. Thank you Alex.
Can we please invite Alex back to do more lectures? This is the BEST numpy tutorial I’ve found online!! This guy seriously needs his own TH-cam channel.
Absolutely amazing lecture. Much recommended for complete beginners. A chilled-out guy teaching calmly, explaining every concept properly, I just loved this!
Any ideas if there are any advanced one to this ? Thanks !
I absolutely love this lecture. This guy is a perfect teacher.
This channel should be national treasure
One of the most sort-out tutorial on numpy so far.
"sought out", not "sort out". But I completely agree with your assessment.
This is miles better than the video that I used to first learn numpy.
What he figured out after 13 years of experience I figured out on-the-fly while watching this presentation and testing things in a Jupyter notebook.
U forgot the 'Let that sink in...' expression.
Totally agree.
The part of multi-dimension array is awesome. Thanks your lecture.
I just enjoyed listening to every part of this lecture. Absolutely fabulous.
This guy with great pronunciation gave me a great insight of Scipy.
How lucky I am to find such an informative video and smart lecturer. So proud to know you Alex.
I truly enjoyed this video. Alex explained frequently used concepts I figured out on my own by trial and error! I wish I knew these concepts before.
It's seriously a great lecture and interesting too. I had some doubts before this lecture, now they are cleared. Thank You, Professor Alex Chabot-Leclerc.
Thank you, Alex !!
so many mental cobwebs cleared after watching this lecture
I came via following the website for NumPy.
Clear, fluent, explanatory, practicing, visually satisfying, beautiful lecture (except chooped 2 chars of the terminal :) ) Thanks
35:00 # If an element is changed in the child array, then the element also changes in the parent array.
important note
This is pure gold. Thank you
Great Tutorial. Thank you Alex and Enthought!
The best tutorial on numpy 😅
Very good presentation, excellent teaching skills. Thank you.
"Fluent Python" book is just amazing as suggested here.....🤗
This was very well done and very useful (despite the mishap with the left side of the screen 😊).
Thank you also for including the part about memory allocation in numpy. It helped me finally understand when (and why) I can expect to get a view vs. a copy of an array. It didn't even seem like an advanced concept, but that's because Alex did a great job explaining it.
saved to my fav playlist. Thank you so much.
The clipping of console screen image is unfortunate. Especially when much of the screen is open for use.
So you need someone who (probably understands the topic and) fills in the blanks for you?
Or just someone who points you to the location of the tutorial materials, noted in the video summary.
(Just to find that the link is dead...)
Or some random Internet girl with google? github.com/enthought/Numpy-Tutorial-SciPyConf-2019
Have fun coding:)
Thanks for the link!
its very helpful for me...thank you so much you are like such a great teacher. Again Thank you so much Alex Sir
Can you please share the ppt which is being used in session?
Thanks! Really nice NumPy tutorial!
great but where is the right side of the screem ??
Absolutely amazing lecture.
Where can I get the pdf that he was showing in the class?
Unfotunatelly, some of the screenshots are cropped too much so that the beginning of the code cannot be seen.
That's true, but we can consider it as a homework to figure it out. (smile)
This is such a good treatment that correcting the missing left side of the screen is well worth the effort for scipy.
Yep. Thats boring a lot.
numpy.shape reports the shape of 3D arrays in the order layers, rows, columns.
Its been a year I am working with Python, but I really regret why didnt I found this earlier
Can you tell me about your work? I'm excited to know
If I want to multiply a number with a specific row or coloumn in 3x3 matrix, then how should I do that?
It can be done by using a loop. (or simply make your own function for that task)
@@autotechshreveport6483 can you tell me how I should use loop for that.
2:02:00 memory
thank you so much for this content but how can i get the exercise codes?
Thank you man, you are a genius.
A wonderful teacher!
So, I am done with this part now what's the next stop after this....
Can we have a copy of the slide deck.
I get the feeling that during the entire tutorial "Rows" (which is horizontal) and "Columns" mixed. Am I wrong here or is this the normal in NumPy ??
thanks God, I know there 9 yrs more to go to remember axis direction.
Can we solve hourglass problem using numpy?
Hi everyone, in the solution file, this line is for finding the identifier for each month: months = (wind_data[:, 0] - 61) * 12 + wind_data[:, 1] - 1, but what is the logic for this line? Thank you
Does anyone know or have the location of the presentation file?
Can we calculate inv of a matrix with some of the elements are complex exponential?
How could he do the math of the difference of the blurred image and the original since they have different shapes? blurred dim = (459, 613) and original = (461, 615)
Great tutorial! Thank you for sharing this video!
Thank you very much for sharing your knowledge. God bless you.
I am not able to download dc_metro.png . Can someone help me?
Amazing class! thank you.
please kindly provide url for slides
Excellent - best prof Merci
superbly explained.
hi guys where can i find the file
Slicing: 17:40
A great professor
great lecture very clear.
Thanks a million for this!
1:22:00 so happy I can be at this point
Thanks sir! Nice tutorial
Excellent 👌
Simply Great
Numpy is a lot like Matlab or GNU/Octave
you know the shits about to get serious when he starts to roll up his sleeves 8:18
really love it😍
25:17 strided .. it should be.. a = [2: : 2 , 4 : : 2]
Its not evil 37.00 its simple you complicate it unnecessarily. Just explain because you used b=a... #whatever after a you asigned a to be so both are the same
Could you help me to solve this type of issue....your valuable reply help me a lot.....
Traceback (most recent call last):
File "/home/PASTA_v1.0/Code/pasta.py", line 37, in
from NEB import pot_frc ## NEB/CI-NEB step
File "/home/PASTA_v1.0/Code/NEB/pot_frc.py", line 41, in
frc_img = tag.read_force(file_out,n_atm)
File "/home/PASTA_v1.0/Code/Interface/interface_siesta.py", line 217, in read_force
tmp.append(eval(w[2]))
IndexError: list index out of range
This is awesome
22:20 here you can see the mistake 3rd element is 13 and -2 th element is also 13
Then why output is 12 ?
Just in case someone's reading this one year later ;-) The element with index 3 is 13, but it's not included in a[1:3], or in a[1,-2]. Remember that the right hand side boundary of the slice isn't included, so a[1:3] means the elements with indices 1 and 2. The same goes for a[1:-2], it's an array consisting of the elements starting at a[1] and ending at the element *before* a[-2]. Alex did mention this early on in the talk, and it's consistent with the behaviour of ranges in python. It might be easiest to think of [1:3] as "really" being [1,3), in the spirit of a half-closed, half open interval.
@@pjmmccann yes actually I was beginner before one year, but now i am pro 🤣👍
@@PANDURANG99 Haha: good to hear! Onward and upward... 😜
Alex trust me you are really the best thanks I like you really you help me to understand hahaha
Starts with simple things in numpy explaining every detail of every function, sign, letter... 46.00 Jumps to image processing with plenty of new data and relations in-between asking to do processing work lmao
Numeric computing is unfimiliar to me, as Computer Numeric Control is
CNC is awesome, on five access
AutoCAD is better at reshaping the envirnment, with something other than numbers to look at
As an incentive design engineer I do this often while recreating something never done before
Inventive design engineer, dumb phone
Presidential Candidate quite educated in SciPy
it is good video thanks sir...
Thanks Bill Hader
If he did NLP, would his name be Alex Chatbot Leclrec? Jk, it was a damn good lecture.
as bookmaker i am at 1:46:00
Thx
44.00 lol complicating again. Python was made for simplicity in use so simply because there are square brackets it skips need for naming funcions you would need to call otherwise :)
@11:10 haha this guy. God bless him and preserve him.
1:07:40
do pandas tutorial please
23:00 for me to know where i am stop
if you are logged in youtube remembers that for you
@@grigoriskitsos5856 hahahah
Something wrong with people in the class. They are not interactive. When Boss asks a question, wrong or right answer it. Don't be silent.
53:35 wow I have no idea what he's doing since 48:25 lmao
Bluefilmii
Cant believe this video has 91K views, while someone bobs their head and gets billions of views on tik tok....
Ладно, 2 часа не так уж и много
I came here from official NumPy site.
The material is no longer available from the provided link🥲