NumPy Operations - Ultimate Guide to Methods and Functions for Beginners!
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- Опубликовано: 9 май 2024
- In this tutorial, we will dive much deeper into NumPy - focusing on important array methods, functions and of course - math! 🤓
We will begin with a quick recap of the previous tutorial and we will then move on with lots of detailed examples and handy tricks!
You can find links to my previous tutorial (and other NumPy-related videos of mine) in the related videos section below.
📝 quick note: at 24:18 - the floating point numbers originated from (b/a) and not from np.floor()
⭐ CLONE MY CODE ⭐
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* Sorry code is unavailable, RIP Wayscript 😭😭😭*
🎥 RELATED VIDEOS 🎥
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⭐ Ultimate Guide to NumPy Arrays (PART 1 of this tutorial):
• Ultimate Guide to NumP...
⭐ Python Learning Roadmap:
• Python Learning Roadma...
⭐ Train Basic Neural Network with NumPy and Pandas:
• Train Basic Neural Net...
⭐ Basic Guide to Pandas:
• Basic Guide to Pandas!...
⏰ TIME STAMPS ⏰
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00:00 - intro
--------------♻️ RECAP ♻️---------------
00:30 - create 2D demo arrays
01:56 - dtype attribute
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02:52 - fill array with values
03:41 - assignment operators
04:12 - NumPy is Python or C?
06:11 - sum of array
06:52 - sum of columns or rows only
08:36 - product of array
09:15 - average of array (mean)
09:31 - minimum and maximum values
10:02 - index of minimum and maximum values
10:33 - peak to peak method (ptp)
11:10 - size attribute
11:50 - flatten vs ravel methods
13:03 - repeat function
14:10 - non-flat repeat function
14:50 - unique function
15:20 - diagonal function
16:05 - convert array to list
16:38 - save array to file
17:12 - swap axes of array
17:56 - transpose method
18:27 - T attribute
19:02 - NumPy documentation
20:07 - simple operations on 2 matrices
21:12 - modulo
22:31 - floor division
24:26 - matrix multiplication
27:41 - thanks for watching! :)
🔗 LINKS AND CREDITS 🔗
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⭐ NumPy Documentation:
numpy.org/doc/stable/referenc...
⭐ Emoji from:
www.flaticon.com/
⭐ Text Effects from:
mixkit.co/free-premiere-pro-t... 
Наука
Thanks for another great video. You descriptions are always so clear and precise.
Thank you for taking the time to make these videos. It has been really helpful and empowering!
Thank you so much! It's the best, the most informative and the most emotional tutorial with excellent English pronunciation what I have watched before! You are the Best! Thanks.
I personally admire this great channel and this great tutor. I have learnt many things that I got stuck in. Thanks !!!
Can you please make a whole playlist on object detection algorithms? I love how you explain everything so easily!
Oh and python implementation of them as well 🤩 Please?
I agree!
Great video! I watch them before class all the time!
Have you done the video explaining matrix multiplication yet?
Very nice explanation of the built-in methods of NumPy. Really a great tutorial.
Amazing
That's what I was looking for
Thanks so much
Yey!!! Super happy to help, enjoy! :)
I really like that u explained np.dot method in 3 different ways 🙂
This is by far the best video i have found on numpy arrays basics n related operations... it's a very interesting and thorough video... ❤
Very nice tutorial, now I understand NumPy much better, thanks a lot!.
Found your vid on converting py to exe and loved your presentation! Subbed your chan to strengthen my skillz.👍
This numpy video serious is the only video i will send, if some one ask me how to use numpy. You have a great approach for teaching. Thank you
Thank you for the links! (and the video itself too) 😄
Perfect explanation and thanks for sharing the graphics.
Great videos ! 👍 welcome from Egypt 🇪🇬
Thank you so much !
I've been using this and found that the numpy operations are very handy.
And on the subject of C, I've learned that it's not too difficult to pass pointers to the arrays into C functions using the ctypes module and operate on them directly in C, which is very fast indeed. I've found that while the mass operations in the numpy module are fast and convenient, that the iterations through them in Python are actually slower than through a list. But pass them to a C function and it's several times faster.
Well explanation, easy to follow and learn, very informative.
Thanks for sharing this video, I will have to watch it in small chunks as my mind is blown 😄
Thank you, Maria!
You are awesome. Informative videos. Thanks Mariya
Thanks for your great content!
Thank you for this video. Easy to understand Numpy. Very usefull, i think.
Amazing video Maria!
A pickle here. In numpy sort method the axis is -1 for row and 0 for columns from your last video. Consistent, no more.
pronounce very understandable, thank you :)
That was great, but for the next Tutorial, can you cover" pyomo" ?
it's will be great if you cover some optimization problem😍
You are awesome. Thanks.
Love your videos!
Very, very usefull. Thanks! 😊
You should start a whole online course on Coursera or EdX. You are damn good. ❤
Great video!
heeeey there :D
Thank You MARIYA, because last time i make the SMARTHome about behaviour using C4.5 Algorithm and FUZZY Entrophy base ANDROID 👍🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥 about numpy or numeric pyhton
thanks for the subtitles
Try this:
import PIL.Image as im
import numpy as np
color = 200,100,255 #try different values 0-255
img = np.full((800,800,3), color, np.uint8)
img = im.fromarray(img, 'RGB')
img.save('sample.png', 'PNG')
So the "ravel" method creates a reference(array) to the original array?
Thanks for video. And I have question. You didn't mention about NaN. For me it's clear, but I didn't find and easy way to explain it to students (but INF is understandable for them). Do You have any idea?
Awesome!
благодарю Евгений! 😊
I keep clicking like RUclips won't acknowledge it.. Wtf.
Great video, thanks for the upload
Science is the sword that does not miss, ,which you strike whoever you want, increases with a lot of giving and decreases with a lack of giving,additionally in your case increase your beauty
Thank you.
You can watch mine too. The channel provides detailed tutorial playlists for Python and R, with downloadable source files (see description of the video).
what i want from you is English Course with support! i will still remind you this ideas !
It will take me some time to get there but it's definitely an option! 😉
I learned English when I was at school so I still have lots of notebooks of grammar rules and all kinds of goodies! 😊
@@PythonSimplified o yeah, English grammar is often a problem for me :) :) :)
@@PythonSimplified can u send us
Please tell me when to use (bracket), [square], _ underscore & .dot ... when we code on Python or numpy ?
very usefull one
Eres genial
That was fantastic
Thank you 🙂
🥑⭐ really good
I can't get numpy to run on my Visual Code. I tried pip install , search nothing works got any ideas ? it works on python idle just fine
You are amezing ❤️👍
you''re smart
I know python basic and advance but not understanding which one is good books or RUclips tutorial
For you we grow up
My default is int32 rather than 64, could be different version of python or numpy, not sure
I believe it depends on your computer hardware and operating system, Sinisa 😃
My AlmaLinux laptop also defaults to int32 :)
@@PythonSimplified os is win 10 in this case now, thanks for info Mariya 👍🏻
❤
❤❤
Nic mam I want this toter
Numerical python! that's what I wanted 😃
That's awesome, enjoy!! :)
GOOD
Plz create some video for pyspark
Beautiful, I mean both the teacher and the video.
Numpy wow ❤️😍
WowPy! 😉
You are so beautiful and smart! Thank you for the video
the background music is as nice as you
youre so nice ❤️
average and mean are two different things :)
is it just me or the sound is only on the right side
Please teach Coding with mobile
5:30 pu puffffffffftttttt :)))
Apparently my math is so bad I was lost from the beginning.. public School failed me
Man.....having a wife that programs and codes.....and Beautiful.......If Eve coded we would still be in the Garden of Eden......
I would really like to know why people use NumPy, I cant find any useful tutorials on how it is used in data science projects or other fields.
So cool video and simple to learn from it. Thank you Maria.
P.S. Probably it's nice to demonstrate also numpy.fill_diagonal
like here:
import numpy as np
a = np.zeros(9).reshape(3,3)
np.fill_diagonal(a,1)
print(a)
I think I'm fallen in love with you
beautiful girl give excellent python lectures
😈 666
Thanks!