Do you want to learn python from me with a lot of interactive quizzes, and exercises? Here is my project-based python learning course: codebasics.io/courses/python-for-beginner-and-intermediate-learners
this is awesome dhaval sir, You covered the entire important topics of numpy in just 10 minutes live without any editor and with simply idle. Keep uploading the videos sir.
watch at speed:1.5x. The video is really cool at that speed. Thanks codebasics for the wonderful video. I was searching for one of these types of videos for quite a while.
Step by step roadmap to learn data science in 6 months: ruclips.net/video/H4YcqULY1-Q/видео.html Learn data science with pandas: ruclips.net/video/CmorAWRsCAw/видео.html Machine learning tutorials with exercises: ruclips.net/video/gmvvaobm7eQ/видео.html
ahaha i like your language style "You Obviously Don't Remember. But If You Open Your Calculator You'll Find The Square Root It's Gonna Be This!". haha i'll tell that to my elementary teacher one day. Best Quote dank af
If you need to create the Python lists to initialize the numpy arrays, i.e if you have to do np.array( *[1,2,3]* ) then you have to make the python list in memory first before passing it to numpy array method, if I'm not mistaken. How does then numpy manage to be faster then??
You are right Vivek that it makes python list first but that is just to initialize numpy array. After it is initialized the list will go out of scope and garbage collected. After that it is pure native numpy array with all it's optimizations such as strict data type, contiguous memory locations etc. Hence it is going to be faster.
@@codebasics sir i learn all basic of python datascience with your videos and other source but sir i have no certifiacte of any kind of like python or data scienece how i get job sir pls help..
Hi Can you pl explain what is a difference between flatten and ravel ? Secondly a=np.array([1,2,3,4]) t=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]]) print(a) print(t) a.itemsize a.ndim When it runs in jupyter notebook in single cell it shows output of a.ndim only. Can you pl explain me why ?
where as in reshapping a nparray you forgot to tell onething the shape whatever we gave & and the product of x&y must be eqals to the total noof elements
Hi, I have a query, i am having following code a=np.array([1,2,3,4]) t=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]]) print(a) print(t) a.itemsize a.ndim When it runs in jupyter notebook it shows output of a.ndim only. Can you pl explain me why ? and Thanks for great simple to understand videos
flatten always returns a copy. ravel returns a view of the original array whenever possible. This isn't visible in the printed output, but if you modify the array returned by ravel, it may modify the entries in the original array. Source: stackoverflow.com/questions/28930465/what-is-the-difference-between-flatten-and-ravel-functions-in-numpy
lets say you have 2D array, a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) To access column # 1 you can do a[:,1] this gives [2,5,8] To access row #2 you can do a[2, :]
Hello sir, I went through this tutorial and I was trying some exercises. I have a question. I have a 3 dimensional array defined as below: a3 = np.array([ [[1, 2, 3], [4, 5, 6], [7, 8, 9], [20, 21, 22]], [[10, 12, 13], [14, 15, 16], [17, 18, 19], [7, 8, 9]] ]) now, when i do a3.sum(axis =0) it does a row wise addition of the above structures and returns array([[11, 14, 16], [18, 20, 22], [24, 26, 28], [27, 29, 31]]) and when i do a3.sum(axis =1) it does a column wise addition and returns : array([[32, 36, 40], [48, 53, 57]]) While for a 2 dimensional array it does the opposite i.e. column wise addition for axis =0 and row wise for axis =1. Can you please tell me why is it so? is the logic different for even and odd dimensional arrays?
Sir basically now translation of our voice is generating on youtube videos due to which we could not be able to see proper codes.If you have some solution for it kindly remove it from your coding videos.
i hope you know by now that Numpy is spoken "num pie" and not "num pi y", it´s hard to take your course seriously because of this misspelling. carpe diem
I am confused here why array is taking so much space and so much time to process import numpy as np import time import sys SIZE = 1000 # CODE FOR LIST l1 = range(SIZE) l2 = range(SIZE) start = time.time() result = ((x+y) for x, y in zip(l1, l2)) print("SIZE of list is: ", sys.getsizeof(result)) print("Time taken by list processing : ", (time.time()-start)*1000) # CODE FOR ARRAY a1 = np.arange(SIZE) a2 = np.arange(SIZE) start = time.time() result = a1 + a2 print("Time Taken by array processing : ", (time.time()-start)*1000) print("SIZE of array is: ", sys.getsizeof(result)) Results: SIZE of list is: 112 Time taken by list processing : 0.028133392333984375 SIZE of array is: 8112 Time Taken by array processing : 0.030040740966796875
Do you want to learn python from me with a lot of interactive quizzes, and exercises? Here is my project-based python learning course: codebasics.io/courses/python-for-beginner-and-intermediate-learners
this is awesome dhaval sir, You covered the entire important topics of numpy in just 10 minutes live without any editor and with simply idle. Keep uploading the videos sir.
watch at speed:1.5x. The video is really cool at that speed. Thanks codebasics for the wonderful video. I was searching for one of these types of videos for quite a while.
Lol I watch all videos at 1.5.I get bored at any speed lower than that
You might have some sort of attention disorder. That might not work out so well in real life.
Neither does being dumb
vignesh ramesh You mad bro?
Marc D no you?
hello mister pajeet I want to thank you from the bottom of my heart for this wonderful tutorial
Step by step roadmap to learn data science in 6 months: ruclips.net/video/H4YcqULY1-Q/видео.html
Learn data science with pandas: ruclips.net/video/CmorAWRsCAw/видео.html
Machine learning tutorials with exercises:
ruclips.net/video/gmvvaobm7eQ/видео.html
bro u vids are dank af
Thanks to codebasic, thanks to dhaval sir, super easy to understand and practice..❤
Thank you man you are a lifesaver for real
print('very nice teaching
ahaha i like your language style "You Obviously Don't Remember. But If You Open Your Calculator You'll Find The Square Root It's Gonna Be This!". haha i'll tell that to my elementary teacher one day. Best Quote dank af
nice and useful video. thank you
If you need to create the Python lists to initialize the numpy arrays, i.e if you have to do np.array( *[1,2,3]* ) then you have to make the python list in memory first before passing it to numpy array method, if I'm not mistaken. How does then numpy manage to be faster then??
You are right Vivek that it makes python list first but that is just to initialize numpy array. After it is initialized the list will go out of scope and garbage collected. After that it is pure native numpy array with all it's optimizations such as strict data type, contiguous memory locations etc. Hence it is going to be faster.
thankyou sir learn python with your helpful video
Glad it was helpful!
@@codebasics sir i learn all basic of python datascience with your videos and other source but sir i have no certifiacte of any kind of like python or data scienece how i get job sir pls help..
Thanks a lot sir ,for such great lecture😇
Does linearly spaced array include the upper bound ?
Excellent, to the point lecture
Thank You !! In 10:52 , You said sqrt is not a function. it's a generic function. what does that mean ?
can you elaborate ?
Thank You !!
np.sqrt() is a class method and not an instance method, well explained here: realpython.com/blog/python/instance-class-and-static-methods-demystified/
Thanks for your effort
Excellent video.Lots of love.
Why there are no jupyter notebook links in the any video of numpy playlist? @codebasics
My array.itemsize shows 8 bytes for both int64 and float64 data types?
Thx. It was a great tutorial. I made a help module out of it so I would remember it all. 😃
I miss doing your exercises!!! Good lecture tho.
same as always this course is very useful as well !!! Thanks BRO...
Glad to hear that
Great tutorial
Glad it was helpful!
Good one. Thank you, Sir.
a=np.zeros((2,3,4)) what is the third number 4 do the array ?
Thankyou sir ❤️❤️
How to load google sheets csv file using Numpy?
very helpful tutorials , thanks
👍😊😊🙏
thanks a lot :)...really clear explanations
this is wonderfull
What's linspace I didn't got it can someone help me with that pls.
Love you man #noHomo.
thanks man. it was great
Excellent
great vid man thanks!
Hi Can you pl explain what is a difference between flatten and ravel ? Secondly a=np.array([1,2,3,4])
t=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
print(a)
print(t)
a.itemsize
a.ndim When it runs in jupyter notebook in single cell it shows output of a.ndim only. Can you pl explain me why ?
Thanks. Very helpuful
Thank you again sir!
What is the significance between data types like ‘uint8’ and ‘uint32’ other than the amount of memory they take up?
Black Schroeder they determine maximum value or precision on the case if floats
How can I import two CSV files, called a.CSV and b.CSV, each includes a 64 by 64 matrice and then do the calculations on them? for example a-b ?
where as in reshapping a nparray you forgot to tell onething the shape whatever we gave & and the product of x&y must be eqals to the total noof elements
From where can I find the practice examples?
really helpful
Great lecture
greate demo. thanks
Can numpy be used on FASTA files to create arrays?
I think for better understanding we have to study numpy with pandas.
Just because there are a lot of things that are the same.
💯💯
can we add two arrays with different shapes ?
thank u💙
how can we segment an image(or a matrix) into sub-images (in square form), and thanks for helping me
I think a seven year old playlist is a bit bit old...........
my vscode says np is no longer a keyword.
please update it
Hi, I have a query, i am having following code a=np.array([1,2,3,4])
t=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
print(a)
print(t)
a.itemsize
a.ndim When it runs in jupyter notebook it shows output of a.ndim only. Can you pl explain me why ? and Thanks for great simple to understand videos
Just to clarify : running above code into single cell, if running a.itemsize and a.ndim in different cells it is showing correct result
Thank you a lot!
Can you pl explain what is a difference between flatten and ravel ?
flatten always returns a copy. ravel returns a view of the original array whenever possible. This isn't visible in the printed output, but if you modify the array returned by ravel, it may modify the entries in the original array.
Source: stackoverflow.com/questions/28930465/what-is-the-difference-between-flatten-and-ravel-functions-in-numpy
thanks sir
Sir where is the part two of this video
sir how to extract rows and columns
lets say you have 2D array,
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
To access column # 1 you can do a[:,1] this gives [2,5,8]
To access row #2 you can do a[2, :]
@@codebasics thank u sir. How to read text files and extracts it's columns ..
nice
Hello sir, I went through this tutorial and I was trying some exercises. I have a question.
I have a 3 dimensional array defined as below:
a3 = np.array([
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[20, 21, 22]],
[[10, 12, 13],
[14, 15, 16],
[17, 18, 19],
[7, 8, 9]]
])
now, when i do a3.sum(axis =0) it does a row wise addition of the above structures and returns array([[11, 14, 16],
[18, 20, 22],
[24, 26, 28],
[27, 29, 31]])
and when i do a3.sum(axis =1) it does a column wise addition and returns :
array([[32, 36, 40],
[48, 53, 57]])
While for a 2 dimensional array it does the opposite i.e. column wise addition for axis =0 and row wise for axis =1.
Can you please tell me why is it so? is the logic different for even and odd dimensional arrays?
you got that answer?
Pythons range(x) does not create a list/array cause it is a generator and cannot be compared with numpys arange() method which creates an array/list
great
Sir basically now translation of our voice is generating on youtube videos due to which we could not be able to see proper codes.If you have some solution for it kindly remove it from your coding videos.
Good
Jason
i hope you know by now that Numpy is spoken "num pie" and not "num pi y", it´s hard to take your course seriously because of this misspelling. carpe diem
I am confused here why array is taking so much space and so much time to process
import numpy as np
import time
import sys
SIZE = 1000
# CODE FOR LIST
l1 = range(SIZE)
l2 = range(SIZE)
start = time.time()
result = ((x+y) for x, y in zip(l1, l2))
print("SIZE of list is: ", sys.getsizeof(result))
print("Time taken by list processing : ", (time.time()-start)*1000)
# CODE FOR ARRAY
a1 = np.arange(SIZE)
a2 = np.arange(SIZE)
start = time.time()
result = a1 + a2
print("Time Taken by array processing : ", (time.time()-start)*1000)
print("SIZE of array is: ", sys.getsizeof(result))
Results:
SIZE of list is: 112
Time taken by list processing : 0.028133392333984375
SIZE of array is: 8112
Time Taken by array processing : 0.030040740966796875