numpy tutorial: iterate numpy array using nditer | numpy nditer

watched now

I mean you just transpose the array and then square each value in the array via vectorized operations. The nditer seems to be extra.

finally finished the python tutorial, thanks for the easy explanations. im looking forward to the data structures and algorithms tutorial now

Is this enough because you don't teach random in numpy?

Thankyou

Great effort Sir

this tutorial is amazing

thank you sir

is this enough ???

use zip() function

for x,y in zip(a,b):
print(x,y)

Excellent

Ramana

Many thanks for giving code

This tutorial was very well explained, thank you!

it's really interesting, just done it with practice

how to use nditer() for iterating element-wise over an unequal nd array e-g: [ [1,2,3], [3,4], [4] ]

Good day codebasics,i wanted to ask you,if this video is the complete course for professionals on python or just for beginners course only? if not can you send me the link to a more advanced course on python, please?

I understand how using numpy is superior than using lists, but the weakness of numpy seems to be that the shapes of arrays are so important. I would need to make sure that the data need to be made "rectangular/squarish" first by using a calculator (Yes, I'm aware you can make the calculations first using Python).

Completed your Numpy playlist. Wonderful. Easy understandable. Please advise if this is enough. Please post more videos eagerly waiting

Good tutorials and very helpful. I just completed this whole Python3 programming for beginners.
What is your recommendation that i should take next?

sir I just want to ask that this tutorial is enough for data science.?

Cant thank you enough sir, really you are doing great job help others for free ,thats very rare nowadays, glad to see persons like you ,it make me to believe that humanity still exists.

nice one

Where to go from here?

Really , it's amazing . the way you're explaining every concept from fundamentals for beginners.

Hi, please I have a question, you said that the iteration through 2 arrays simultaneously is possible if the arrays have the same shape or if one of the dimentions in one of the arrays is 1 .
but it turns out that you have changed the dimentions of the array b of your example from (3,1) to a (4,1) but you had an error although we still have one as dimention

greetings from France, your videos are awesome

Sir , should i do Data Structures and algorithms or Data Science with Pandas first?

Best numpy tutorial 💯

Can you make a/some video on AWS ..

Great video 👍

sir can you please provide links for all quries you teach in lectures. i mean each and every. please

Your tutorials are Huge asset for the beginners and we are looking forward for more......and your style of mentoring is awesome. Great Job.

You are great sir , your videos are amazing and awesome way of teaching .

Thank u....

grate videos..... thanks

Helpful a lot, thanks much

It would be great if you show how to do iteration (for loop ) without using modules and built-it python functions, instead by applying logical and arithmetic ways.

Thank you. Good tutorial.

Could you please create a video for scikit-learn as well

well covered. very helpful. Thank you very much

If i have an array a = np.arange(9).reshape(3, 3) and I define a function such that:
def iter_by_row(array):
for x in np.nditer(array, order='F'):
print(x)

But when I do this:
print(iter_by_row(a))

My output is:
0
3
6
1
4
7
2
5
8
None

Why is the None being printed? Is this normal?

Sir can you please provide information on django framework

Good

thanks for the tutorials!

I have a specific question about Data frame (i went through your GitHub) library and could not find answer. How to reach you for some more clues on problem around iterating through dataframes and calculating values in 3 dimensional dataframe?

Awesome......

Make more videos on numpy to cover all functions....... your tutorials are really helpful for a beginner.❤

straight to the basics....

Amazing way of teaching 😍😍
Thanku so much for this all