I can't say how much I am grateful to you after this video. Thanks a lot, bro! 😊 Please cover other important libraries like pandas,matplotlib,scikit-learn,scipy etc. After this I would request you to deep dive into statistics part pls, else the knowledge gained through data science would be incomplete. Looking forward to your upcoming videos 👍.☺️
44:58, I feel that the argsort(axis=0) when applied to the 2D array, shows the indices sorted with respect to the vertical directions and the axis=1 shows induced sorted wrt the horizontal direction :)
07:03 Numpy gives efficient storage and ready-made functions for data analysis. 14:06 Using Jupyter Notebook and Numpy 21:09 Learned about creating numpy arrays using python objects 28:12 Creating numpy arrays from Python structures and using intrinsic numpy array creation objects 35:15 Numpy array creation methods and reshaping 42:18 Understanding numpy arrays and axis 49:21 Using arg sort and axis to sort 2D arrays 56:18 NumPy arrays take less space compared to Python arrays.
44:42 --> 'argsort()' may be used to find the index of all the elements of a sorted array of any dimension. for a 2d array, we have to specify the axis whose elements we want to sort
44:56 Function: myar.argsort(axis = 1) Display: array ([[0, 1, 2], [0, 1, 2], [2, 1, 0]], dtype=int64) Functionality: it will display the indexes of the values from the original array and sort the values
45:13 arg.sort() it works by default rows wise and if we give it axis =0 . then it work vertically every column and if we give axis = 0 then it work horizantally
44:54 We want an argsort of axis 1, so as we all know that axis one goes from left to right hence when the first whole column will be an axis of when axis 1 is concerned, hence if we try to sort it we will come to know that all the elements are already sorted, so it will yield and answer as [0,1,2].
Argsort arranges the indices of elements in increasing order of element value. Depending on the axis, it works like...for axis 0 it sorts every column individually and for axis 1 it sorts the rows individually.
I was not knowing that its so easy to handle NumPy and pandas...Kudos to COde Master Mr.Harry.....I know that you but still I wanna tell, you are providing occupation to a family.....which is so kind....HATTSSS OFFF to this master....We all love you....😍 Do we have notes of pandas and numpy?
Hey, thank you, Harry, I am started following your videos from the last 3-4 months What a content you created for free of cost you deserve a big Applause 👏 👏
44:58 in axis = 0 it tells us where the required elements are w.r.t vertical axis for them to be sorted in ascending order vertically In axis = 1 it is considered horizontally
Brother you're great. You're helping many who can't learn these concept easily. I watched your both videos on numpy and pandas, again thanks a ton. Harry 😊
There are 6 general mechanisms for creating arrays: -Conversion from other Python structures (i.e. lists and tuples) -Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.) -Replicating, joining, or mutating existing arrays -Reading arrays from disk, either from standard or custom formats -Creating arrays from raw bytes through the use of strings or buffers -Use of special library functions (e.g., random)
For 48:48 in np.where(ar>5) (array ([1,2]), array([2,0]) As I understood you mean there are 2 tuples (1,2) and (2,0) which shows the index of items greater that 5. But I think this two tuples are not for 2 index of 2 elements rather it is index of X and Y axis of all the elements greater than 5. Pls correct me if I am wrong.
ar.argsort(axis=1) # for axis =1 it will sort the array by the column ar.argsort(axis=0) # for axis = 0 it will give the index of the maximum element in each row
Argsort(axis=0)..this provide the sorted position of the value of axis 0...like if axis 0 has some numbers like 4,2,7..then argsort will provide the following position 1,0,2..
44:42 -> what argsort() does is it returns the indices of the array's elements in the ascending order depending upon the axis that has been assigned to it.
Hi Harry, Your videos are too good and easy to understand. Thanks for uploading the videos. I have one query. How can we download/extract the output data from Jupyter Notebook?
argsort(); Basically Sort the array according to indexes ... if you put (axis=0) then it will give you that indexes which are sort for top to bottom and if you put (axis=1) then it will give you that indexes which are sort for right to left respectively.
@44:58 about ar.argsort(axis=0) we know that argsort method sorts the elements in a way that we can make out which element is lower or greater in the manner of 0 1 2 indexes, so when we execute it for every 0 axis we will be getting columns sorted out, so eventually the output will be ([[0, 2, 2], [1, 0, 0], [2, 1, 1]]
array([[1, 2, 3], [4, 5, 6], [7, 1, 0]]) argsort(axis=0) shouldn't be like array([[0, 1, 1], [1, 2, 2], [2, 0, 0]]) because for the 2nd and 3 rd column the largest number are present at index 1 [2, 3], [5, 6], [ 1, 0]
.argsort() function gives the order in which a given array must be arranged for ascending order, it returns the index number of elements present in an array.
ar.argsort(axis=1) it is showing the indices of the horizontal elements (row elements) according to which if we would have arranged the elements we would have got a sorted array.
Argsort is used to arrange elements from ascending to descending order in 2D array and when axis = 0 , it is according to vertical direction and when axis = 1,it is according to horizontal direction.
argsort simply arrange the element indices in ascending order. like argsort(axis =0) means it will simply arrange element indices in ascending order column wise. argsort(axis = 1) works as row wise.
The argsort(axis=0) works on columns if we look at the column no1 then the smallest is at index 0, the middlest as at index 1 and the largest is at index 2 so it become 0,1,2 and in column 2 : the smallest is at index 2, the middlest is at index 0 and the largest is at index 1 so it become 2,0,1..so like this we have to write the index of the smallest number to largest in a column (from smallest to largest )
im getting after watching sort() is that argsort() is used for to arange element in ascending order with the help of index no.if we take axis=0 then we have to arrange in vertically as well if we take axis= 1 then its going horizontally ....
ar.argsort(axis=1) will return a matrix of same order with sorter indices on axis one. This will tell us the indices of min to max element of the row for example; [ [4,5,6], [8,22,1], [100,23,1] ] will result in [[0,1,2], [1,2,0], [2,1,0]]
44: 56 argsort -> return the index of element that should be for having the items in ascending order. suppose argsort(axis=1) , now it will see the index of items in the axis 1 i.e row ( because axis 0 is column and axis 1 is column ) so that we will get the elements of row in ascending order
argsort(axis=0) It tells the indexes of 2D array in sorted order for vertical matrices. But argsort(axis=1) tells the sorted indexes of arrays in horizontal matrices of an array.
ar.argsort(axis=1) - gives the element position to sort the array for column, horizontally in asc ar.argsort(axis=0) - gives the element position to sort the array for rows, vertically in asc I know its confusing since axis=0 is row and axis=1 is column
here axis =0 means move along the columns and now elements gets sorted according to indexes along columns and corresponding to maximum value in column 1 index will come firsst.
Hi, your vedio's are leaner friendly, but if you could prounce numerical numbers in English,then its more convenient for Non-hindi students,great going!
thank you harry bhai mene aapke python ke pure 123 video dheke and numpy bhi appne bahut achi and simple tareke se sikayeye hai. your way of teaching, so laveable :)
ar.argsort(axis=0) ....here axis=0 represents the row in a matrix ( array in matrix form) so....ar.argsort(axis=0) will show the INDEX having the max value in a particular row .
argsort(axis = 0) which find the lowest number in the array w.r.t rows and give the index then second lowest number and its index and so on... if we have three numbers ( 1, 4, 6) it finds index of 1 then index for 4 and then index for 6)
All the jobs that are linked to data use python quite vast so working and practicing every attribute of this language helpful and beneficial for all the novice students for instance we know how to manipulate basic data set with some python commands but we don't know all the working stages of data scientist or data analyst in the real-time industry and how a project is to be done and what possible outcomes does the industry make after when a data scientist completed their job on a given data set and what makes them worth to earn thousands of money believe me this gonna work like a big motivation for the students.all in all let me give an example take basic data set like UCI-iris and do all the necessary work on it up to maximum extent and what possible results come out from this .especially for what we looking in it and why and how to make final report of any particular data set after completing it and what decision could be made further for the companies point of view.
Makes sense!👌 This would lead us in knowing how a dataset makes real sense based on which decisions/predictions can be made. Bro, hope you will look into this pls.
ar.argsort(axis=1) is use to sort the elements row wise which is axis1 So, ar.argsort is sort the elements row wise 1st row then 2nd row and then 3rd row!
When you did sum(axis=0) it is adding the value vertically, but in graphic design you said that axis 0 is for rows and axis 1 for columns. Little confusion.
In data analysis sometimes ram ka issues aata hai toh ek video buffering par bi bana do coz UA-cam par koi accha tutorial nai hai uspe practical usage ke saath
Wow, I can't believe I understood numpy so easily in 1 day. Your teaching style is so amazing, Harry sir. Hats off to you. Going to learn pandas next. Keep up the good work you're doing, you don't know how many students you actually help everyday.
U r phenomenol bhaia thank you so much for doing such a hard work for us. Hit the like if like the video yaha nhi kroge to chalega pr upr jarur like👍 krna
Like karna mat bhoolna :)
Pandas In one video is coming soon.. Subscribe and hit the bell icon to stay tuned!
QNA
@@safety1610 Bro You are right.
QnA
I can't say how much I am grateful to you after this video. Thanks a lot, bro! 😊 Please cover other important libraries like pandas,matplotlib,scikit-learn,scipy etc.
After this I would request you to deep dive into statistics part pls, else the knowledge gained through data science would be incomplete.
Looking forward to your upcoming videos 👍.☺️
Bhai please make complete tutorial of matplotlib including seaborn please.....
44:58, I feel that the argsort(axis=0) when applied to the 2D array, shows the indices sorted with respect to the vertical directions and the axis=1 shows induced sorted wrt the horizontal direction :)
sahi hai, vo harry bhaiya kabhi kabhi galti se bol dete hai shayad. Mai bhi output dekh kar hi samjha
nahi vo sah =i bol rha hai, phir se check karo, axis =0 along the rows and across the columns
07:03 Numpy gives efficient storage and ready-made functions for data analysis.
14:06 Using Jupyter Notebook and Numpy
21:09 Learned about creating numpy arrays using python objects
28:12 Creating numpy arrays from Python structures and using intrinsic numpy array creation objects
35:15 Numpy array creation methods and reshaping
42:18 Understanding numpy arrays and axis
49:21 Using arg sort and axis to sort 2D arrays
56:18 NumPy arrays take less space compared to Python arrays.
Thanks, bro this is what I was looking for.
Please make video on tensorflow, keras, matplotlib, pandas, scipy modules.
bro jupiter ki jagah vs code use kar sakte hai
@@sumitrawat6493 Haa bhai kar sakte hain 👍
What is the difference between methods and attributes?
Also SymPy
@@amanshukla270 methods are functions and attributes are variables (in classes ) . You can think like that
44:42 --> 'argsort()' may be used to find the index of all the elements of a sorted array of any dimension. for a 2d array, we have to specify the axis whose elements we want to sort
no it's not necessary to specify axis for a 2d array when using argsoft()
44:56
Function: myar.argsort(axis = 1)
Display: array
([[0, 1, 2],
[0, 1, 2],
[2, 1, 0]], dtype=int64)
Functionality: it will display the indexes of the values from the original array and sort the values
45:13 arg.sort() it works by default rows wise and if we give it axis =0 . then it work vertically every column and if we give axis = 0 then it work horizantally
Please make a series on chatbot with AI and Deep learning
Hmm
Yes
hmmmmm...plzzz
plz sir from first to last
44:54
We want an argsort of axis 1, so as we all know that axis one goes from left to right hence when the first whole column will be an axis of when axis 1 is concerned, hence if we try to sort it we will come to know that all the elements are already sorted, so it will yield and answer as [0,1,2].
Argsort arranges the indices of elements in increasing order of element value. Depending on the axis, it works like...for axis 0 it sorts every column individually and for axis 1 it sorts the rows individually.
44:30
As you have given (axis=1) as argument for argsort it will sort elements with respect to in horizontal axis.
I was not knowing that its so easy to handle NumPy and pandas...Kudos to COde Master Mr.Harry.....I know that you but still I wanna tell, you are providing occupation to a family.....which is so kind....HATTSSS OFFF to this master....We all love you....😍
Do we have notes of pandas and numpy?
Hey, thank you, Harry, I am started following your videos from the last 3-4 months What a content you created for free of cost you deserve a big Applause 👏 👏
44:58 in axis = 0 it tells us where the required elements are w.r.t vertical axis for them to be sorted in ascending order vertically
In axis = 1 it is considered horizontally
Brother you're great. You're helping many who can't learn these concept easily. I watched your both videos on numpy and pandas, again thanks a ton. Harry 😊
Me toooo bhai
There are 6 general mechanisms for creating arrays:
-Conversion from other Python structures (i.e. lists and tuples)
-Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.)
-Replicating, joining, or mutating existing arrays
-Reading arrays from disk, either from standard or custom formats
-Creating arrays from raw bytes through the use of strings or buffers
-Use of special library functions (e.g., random)
there are only 5
Based on latest info it is 6 @@utkarshbharti7274
Extremely good work. Cut to cut & point to point tutorial. We liked it. Please make some videos on Panada and Scipy. Keep it up :-)
Pandas is already uploaded and scipy is coming soon. Thanks for your love and support!
right
@@ruchiagarwal4943hi
For 48:48 in np.where(ar>5)
(array ([1,2]), array([2,0])
As I understood you mean there are 2 tuples (1,2) and (2,0) which shows the index of items greater that 5.
But I think this two tuples are not for 2 index of 2 elements rather it is index of X and Y axis of all the elements greater than 5.
Pls correct me if I am wrong.
you are right bro . i was very confused but your comment gave me relief . thanks a lot
ar.argsort(axis=1) gives the given 2D array in ascending order for all rows. [ [1,2,3] , [4,5,6] , [0,1,7] ]
For 44:50 , ar.argsort(axis=1) will give us the indices by which we can sort our numpy 2d array elements column-wise
Rowise
ar.argsort(axis=0)
This will give us the index to sort the array column-wise, vertically.
ar.argsort(axis=1)
# for axis =1 it will sort the array by the column
ar.argsort(axis=0)
# for axis = 0 it will give the index of the maximum element in each row
Argsort(axis=0)..this provide the sorted position of the value of axis 0...like if axis 0 has some numbers like 4,2,7..then argsort will provide the following position 1,0,2..
44:42 -> what argsort() does is it returns the indices of the array's elements in the ascending order depending upon the axis that has been assigned to it.
Hi Harry,
Your videos are too good and easy to understand.
Thanks for uploading the videos.
I have one query. How can we download/extract the output data from Jupyter Notebook?
Such a great Explanation of Numpy, I have understood alots of things through this video.
So thanks alot♥️.
"hum ko sirf numpy hi nahi padhna hai , besically hum ko data science karna hai " ------love this line
argsort(); Basically Sort the array according to indexes ... if you put (axis=0) then it will give you that indexes which are sort for top to bottom and if you put (axis=1) then it will give you that indexes which are sort for right to left respectively.
Sir please Do QNA 🙏🙏🙏
GREAT WORK
@44:58 about ar.argsort(axis=0)
we know that argsort method sorts the elements in a way that we can make out which element is lower or greater in the manner of 0 1 2 indexes, so when we execute it for every 0 axis we will be getting columns sorted out, so eventually the output will be
([[0, 2, 2],
[1, 0, 0],
[2, 1, 1]]
array([[1, 2, 3],
[4, 5, 6],
[7, 1, 0]])
argsort(axis=0) shouldn't be like array([[0, 1, 1],
[1, 2, 2],
[2, 0, 0]])
because for the 2nd and 3 rd column the largest number are present at index 1 [2, 3],
[5, 6],
[ 1, 0]
ar.argsort(axis = 1)
array ([0,1,2]
[0,1,2]
[2,1,0])
2 = 3
2 = 6
0 = 7
it represents ascending value in every row (horizontally)...
.argsort() function gives the order in which a given array must be arranged for ascending order, it returns the index number of elements present in an array.
bro jupiter ki jagah vs code use kar sakte hai
ar.argsort(axis=1) it is showing the indices of the horizontal elements (row elements) according to which if we would have arranged the elements we would have got a sorted array.
Argsort is used to arrange elements from ascending to descending order in 2D array and when axis = 0 , it is according to vertical direction and when axis = 1,it is according to horizontal direction.
Thankyou so much Harry Bhaiya, ye video mere liye kaffi helpful thi😍😍😍😍
argsort simply arrange the element indices in ascending order.
like argsort(axis =0) means it will simply arrange element indices in ascending order column wise.
argsort(axis = 1) works as row wise.
The argsort(axis=0) works on columns if we look at the column no1 then the smallest is at index 0, the middlest as at index 1 and the largest is at index 2 so it become 0,1,2 and in column 2 : the smallest is at index 2, the middlest is at index 0 and the largest is at index 1 so it become 2,0,1..so like this we have to write the index of the smallest number to largest in a column (from smallest to largest )
im getting after watching sort() is that argsort() is used for to arange element in ascending order with the help of index no.if we take axis=0 then we have to arrange in vertically as well if we take axis= 1 then its going horizontally ....
ar.argsort(axis=1) will return a matrix of same order with sorter indices on axis one. This will tell us the indices of min to max element of the row
for example;
[
[4,5,6],
[8,22,1],
[100,23,1]
]
will result in
[[0,1,2],
[1,2,0],
[2,1,0]]
I am here now after 100 days of python course and now i move further to machine learning
argsort is symply giving indexes of a sorted array.
arr.argsort(axis=0) gives inces of sorted array for each row.
44: 56 argsort -> return the index of element that should be for having the items in ascending order.
suppose argsort(axis=1) , now it will see the index of items in the axis 1 i.e row ( because axis 0 is column and axis 1 is column ) so that we will get the elements of row in ascending order
Wow, numpy in 50 mins! Will look forward for pandas video.
argsort(axis=0)
It tells the indexes of 2D array in sorted order for vertical matrices. But argsort(axis=1) tells the sorted indexes of arrays in horizontal matrices of an array.
ar.argsort(axis=1) - gives the element position to sort the array for column, horizontally in asc
ar.argsort(axis=0) - gives the element position to sort the array for rows, vertically in asc
I know its confusing since axis=0 is row and axis=1 is column
at 45:30 in axis = 0 it gives matrix of indicies to sort the given matrix wrt to rows..... and vice versa for axis = 1
Your tutorials are just awesome it really helps us a lot
here axis =0 means move along the columns and now elements gets sorted according to indexes along columns and corresponding to maximum value in column 1 index will come firsst.
Hi, your vedio's are leaner friendly, but if you could prounce numerical numbers in English,then its more convenient for Non-hindi students,great going!
thank you harry bhai mene aapke python ke pure 123 video dheke and numpy bhi appne bahut achi and simple tareke se sikayeye hai. your way of teaching, so laveable :)
Thank you गुरु जी❤
Such a great introduction tutorial on numpy. Got a clarity finally. Keep up the hardwork harry❤
argsort() will give the indices in which we should place our numbers of array to make it in ascending order in respective axis.
Easy to understand for IT Or Non- IT people's. 😊
Thank you very much for this course!!!
its very helpful for me for my NumPy Quiz test
thanks bro
dear sir, this is only one video I proper clear numpy thanks sir for your videos and you are the best teacher on youtube.
ar.argsort(axis=0) ....here axis=0 represents the row in a matrix ( array in matrix form) so....ar.argsort(axis=0) will show the INDEX having the max value in a particular row .
Kya baat hein , Your basics are so clear . I always will refer to your videos in future for Python
thank u so much harry bhai...
it means a lot...
Hi Harry, could you please also upload a fully detailed Power BI tutorial? That would be a knockout for sure, thanks in advance!
[axis=1] , Its give the index of horizontally row of matrix on which it will sort
This is the best 56 minutes ever invested. Great vid awesome tutorial. 56:01
you have cleared all my doubts regarding numpy. thankyou!
You are just Amazing Harry........Thanks aton for teaching Numpy in simplest way possible......Learned a lot on Numpy.......Thanks bro
Proud to be an Indian!!!
28:30
Running np.empty(dimension) after np.zeros(dimension) gives an uninitialized array with 0.0 values, I am not sure if everyone facing the same.
argsort(axis = 0) which find the lowest number in the array w.r.t rows and give the index then second lowest number and its index and so on... if we have three numbers ( 1, 4, 6) it finds index of 1 then index for 4 and then index for 6)
wha bhaia maza aa gaya numpy sekh ke apke sath....
All the jobs that are linked to data use python quite vast so working and practicing every attribute of this language helpful and beneficial for all the novice students for instance we know how to manipulate basic data set with some python commands but we don't know all the working stages of data scientist or data analyst in the real-time industry and how a project is to be done and what possible outcomes does the industry make after when a data scientist completed their job on a given data set and what makes them worth to earn thousands of money believe me this gonna work like a big motivation for the students.all in all let me give an example take basic data set like UCI-iris and do all the necessary work on it up to maximum extent and what possible results come out from this .especially for what we looking in it and why and how to make final report of any particular data set after completing it and what decision could be made further for the companies point of view.
Makes sense!👌 This would lead us in knowing how a dataset makes real sense based on which decisions/predictions can be made. Bro, hope you will look into this pls.
in argsort(axis=0) it arange the axis=0 element with their indices and represent it incresing order
Thank you harry bro, for such a explanatory tutorial, please publish more content on Data science for self learning
14:35 numpy start here..
32:10 numpy axis
Great video. For ppl who know how to install jupyter and basics of it can skip the video to 15:00
QNA karo naa bro
Your teaching style is too good thus all beginner type stude prefer you ❤
Love from Satyam Rocker
45:00
ar.argsort(). By default ( axis=1 ) ke liye sorting index deta hai
Please tell me for data science this much numpy is enough?
ar.argsort(axis=1) is use to sort the elements row wise which is axis1 So, ar.argsort is sort the elements row wise 1st row then 2nd row and then 3rd row!
Harry Bhai thanks alot. Ur videos helped me alot, Dil se sukriya❤️
44:51 argsort orders numbers in ascending order for axis 1 which is rows
When you did sum(axis=0) it is adding the value vertically, but in graphic design you said that axis 0 is for rows and axis 1 for columns. Little confusion.
axis 0 spans rows. So if you sum axis=0, it will sum all elements from row 0 to last row in every column
Sir Thankyou very much your videos
helps so much to learn python.Numpy library aache se understand
ho gayi he.
argsort() basically sorts each and every line on the array and displays the sorted index
Bhai Thank You Thank You
.
Please make a series on chatbot with AI and Deep learning.............
thank u so much for teaching in a layman language
Thank you tumba help aytu Anna
In data analysis sometimes ram ka issues aata hai toh ek video buffering par bi bana do coz UA-cam par koi accha tutorial nai hai uspe practical usage ke saath
Wow, I can't believe I understood numpy so easily in 1 day. Your teaching style is so amazing, Harry sir. Hats off to you. Going to learn pandas next.
Keep up the good work you're doing, you don't know how many students you actually help everyday.
dont forget to practice
@@hum4424 from where to do ? do u have resources ?if yes pls do share it here
Thank Harry you have done very excellent work. I was waiting for this type of video. keep going
bro jupiter ki jagah vs code use kar sakte hai
@@sumitrawat6493 of course bro
#9th July 2023
Thank you Harry Bhai
I have completed ❤🎉
Harry Bhai Source code ya notes mil jaate to aur bhi jyada mza aa jaata
aapke sabhi vdeos full of content h.all video are liberary of knowledge.
Very well explained Harry bhai. You never disappoint us.
thank you harry vai
Arg is basically connected to index by argsort we can known the right index which have the best value according to sorting
You are the badshah of coding... resemblance to The rapper
Thank you yar harry bhai aap bahut achchha padhate ho❤️
U r phenomenol bhaia thank you so much for doing such a hard work for us. Hit the like if like the video yaha nhi kroge to chalega pr upr jarur like👍 krna
yes right