
Kiran Gadhave
Okay, so we either do a few debrief with questioners up. So you know, first, I just want to start with your high level first thoughts on your experience using like interactive data analysis in Jupyter. Notebook.

M2  4:12  
So yeah, it was a lot easier as compared to the code and everything was visible, the buttons of all the things that we need to do and it didn't take much time. Also,

Kiran Gadhave  4:25  
have you done interactive analysis? Before? Like, not just Jupiter, but anywhere? No, I, I couldn't think of Tableau or

M2  4:36  
I did. I have used AWS QuickSight. Okay, so yeah, in that basically, we form graphs using the column names, and we decide what kind of crap we need. But,

Kiran Gadhave  4:49  
so there's that second up thinking AWS site, quick site, so does that also allow you to manipulate the data Oh,

M2  4:59  
No, actually, no, we have the data frame. And we usually select what columns we used we want to use, and what kind of draft we need, but we cannot change the column names.

Kiran Gadhave  5:14  
Okay. Okay. That's good to know. Okay, so the next question would be, how was the? So I mean, this was fairly short tutorial. And kind of like, really, you know, you didn't really have a lot of time to play around with it. But overall in this, how easy was it to quick understand which feature is fair? How do you use it for your task?

M2  5:40  
So first of all, I haven't used it before. So you gave me the tutorial, and I used it. So it was very easy to understand and then implemented, just strike up tutorial. And all the things were visible that we need to require, and whatever I worked on, was easy to implement. So

Kiran Gadhave  6:01  
okay, so another feedback would be, you know, like in terms of UI and user friendliness? What would you say that, you know, we should change to have some input on, you know, this thing wasn't great. It would be really helpful if it was that clear. I mean, and it can be, you know, like anything, you don't really have to be very specific with it.

M2  6:29  
I'm not really sure. Like, whatever I used was easy to understand and implement, okay, and it didn't face any issues and that investors are good. But maybe if I use it more and more, maybe I could find something. But right now, I don't have anything.

Kiran Gadhave  6:45  
Okay. So we'll come back to this point. But now, I want to basically, I want you to think about the high level tasks. And think about your experience in both pandas versus person. And just give me some thoughts on that, do you feel you're more efficient videography. You know, this is great, but I would still like to read code for this. So first was first type of task, but it was like manipulating the column. So no, renaming the column, deleting the columns. So

M2  7:20  
I have done data analysis during my work experience, but I haven't done it like for the past three, four months. And it was difficult to remember the syntax while writing the code. So I knew what I have to do. But I have to, like look up to the syntax, doing it tight or not. And it was taking time. And using persist it as it was interactive, I don't have to look for syntax, I don't have to write the code, I have to just like, click few buttons. So it was easier, and also took a much less time than writing the code. So yeah, so process is much easier, more interactive, than writing the code. Yes, like few, like some times you'd like to use few traditional methods, like while I was using value counts to look for I've done it correctly or not, then I was using all obviously the code in the traditional method. But yeah, vs Two is better than writing code for all these small, small tasks. And also it helps like these, instead of spending time on just renaming or dropping the columns, the small tasks, we would like to spend time are more efficient task like further task and more important tasks. So this saves time also. Yep.

Kiran Gadhave  8:37  
So actually, so you mentioned you know, like you wrote some code another person. So what are your thoughts on a workflow like that, where you get to go from an interactive table or interactive chart to a data frame, you write some pandas code, and then you again go back to a table or so, you know, kind of mixing your interactions with your code.

M2  9:02  
It was not an issue as we know what the data frame name is, I think unless I have the data frame name and I have I know what code I have to write I can use it, it was not very difficult to switch.

Kiran Gadhave  9:16  
But how was your you know, what I want to mostly know is do you think like workflow like this is better than either just having something like Tableau where it's all interactions or having something like Jupiter when it's all right good

M2  9:31  
job, this is better because whenever, like, whatever introduction we did to make the changes we did if we want to understand something or make few changes, maybe there are some features which are not present in this and we might want to use the code. So it is helpful to have both the things Okay, good.

Kiran Gadhave  9:54  
So, okay, again, coming back to comparison, so when you had to find added values like you have to fix some values. So in the first data set, it was like removing the quotes. And in the second one it was removing the piece. So, what are your thoughts like which approach was better, which are previous basically more easier and intuitive.

M2  10:16  
So interact interactive, it would be a problem, if there are many rows, we have to replace the value of. In file writing the code is done in. Like, overall, it is done once. But here, if we have to do it manually, for each row, it would be difficult in this case, it was just one or two rows, but usually we will have many values like that. And it will be difficult to like, replace, like this. So that is one thing that we could change. Yeah, that's always good to have the replace thing, like we could add a feature we, like we used. In general, we use Ctrl, F and Ctrl. R for replace, find, find and replace in the browser. So kind of like that we have two values, one that needs to be replaced, and one which we can replace with. So

Kiran Gadhave  11:09  
that's actually a pretty good suggestion. That's, that's actually pretty useful. Yeah.

M2  11:13  
Yeah. And in the search option, as I was saying, we could or we could use if I want to search in one column, like your it was searching for in the whole data set, basically. So that could be shown for

Kiran Gadhave  11:26  
two column based search. Okay, yeah. Okay, so the other kinds of tasks predictors were actually removing or like subsetting the data. So you know, you removed some outliers, you removed like, some old data using like, years and stuff. So now, what are your thoughts on the SEC easier to do it persisted was a digit with pandas. So and also was intuitive, right? Or just easier?

M2  11:52  
It was, obviously easier with persist, but the problem I saw was like, we were doing it in a scatterplot, we might miss a few points. And the selection is always not correct, right? In fact, writing the code, we can give exact values that we want to remove, and it would remove that without any remaining values, versus this could be a problem. Okay?

Kiran Gadhave  12:18  
How do you think that can be fixed? So what? So for example, when you're selecting a scatterplot, you made a brush? What do you think would have helped you refine the brush and, you know, take it to like the exact values?

M2  12:37  
Okay, maybe if we are talking about the graph, then we can hovered around the graph, and we could see which out, we could see kind of the coordinates at each point. And according to that, first we do the highest level selection. And if some points are left, and we want to know if they are in the outliers or not, then we can select using that coordinate, we can select and remove those points, but I think it would also take might take a lot of time if we have a lot of data set. Okay. If not graph, maybe we could input values that want to do this, select this column to this category, like this, like that. Yeah.

Kiran Gadhave  13:26  
And your object that's basically like quite similar to what we do for like what we do with pandas, right, we have column name and values, but maybe like a simpler interface for that. Okay. Then the last major task we did was the assignment of new categories. So we essentially added a new column based on existing one. So you have to do that, again, with both the pandas and persist and basically, which was more intuitive.

M2  13:59  
Intuitive, it was purchased. But two, we were assigning category, again, based on the selection, right. So which, which is kind of, I think, random, like, we don't know exactly which points would if you want exact point like this should fall into this category, then I think it's not the best solution to just do it through the graph, right? And otherwise, if we know, okay, like, we can see the values in the x axis and the Y axis, but to it might always not give, I mean, it might take more time, you know, looking at those values, and then assigning the category and you know, by selecting it, so, yeah, I think code is better in those terms for if you want to get the exact values. If it's random selection, then you might use

Kiran Gadhave  14:49  
and so your previous point was, you know, take something for like the graph or basically a way to input the exact value of the brush. Oops, had? Do some data could help with this? And what about the visual feedback? During assignments? Basically? Sorry. So the purchase, you get some visual feedback when you do assignments. So that versus pick Panthers approach,

M2  15:18  
as in

Kiran Gadhave  15:20  
outputs. So yeah, so when you assign the category, the color changes to that, you know that the category has been assigned.

M2  15:29  
Yeah, that is, like, a good because we get to, we basically could visualize the graph. And the colors are also assigned, we could see the categories which are assigned to each category. But I think you can do it through my plotting person. Yeah. But we can interact with

Kiran Gadhave  15:48  
that. Yes. So you will basically have to write code to assign values and run the plot, then basically keep repeating. Okay, so last question would be, do you use something like persist? And you know, your own data analysis? Well, of course,

M2  16:10  
I would use persist, because it makes the task easier, it saves time. And also, we have Furthermore, important tasks. For example, data analysis is part of, for example, I'm creating a project, I have to take in a model, I have to do evaluation, I have to do data analysis first. So I would like to save some time in the data analysis part. And these are the things that we have to do generally in a dataset. Right, it would be nice to do it like in few clicks. Okay.

Kiran Gadhave  16:40  
That's, I think that was all I have here today. Let me just quickly stop the recording. Thank you very much. Other words?

