
Now, the last step that is left is basically we do a few questions about your experience. And then then I think we can stop the study. Okay, so. Okay, so first quarter, your what are the first thoughts on the interactive data analysis that you're doing?

M3  17:22  
Yeah, so even if we don't know anything about, like, data analysis, just seeing the data, we can filter out and rename, change and modify the existing data frame easily. So even if a person is like, quite new to data analysis, you would be able to easily figure out like, what needs to be done, right.

Kiran Gadhave  17:46  
So how was your so you kind of had like, you know, take a really short tutorial, and that you, basically are expected to do the tasks, right. So how was the experience in tact of like, how easy or hard it was to pick up the different features in a short time and then actually apply them? Yeah,

M3  18:05  
basically, the tutorial was really helpful. So if I was, if I was the first person to figure out, like, what needs to be done without the tutorial, I think it would have taken like, some more time to figure out like, what is the worst option that I need to choose? So with the tutorial, I think it's like, pretty straightforward. We can do the jobs faster.

Kiran Gadhave  18:28  
Okay. Anything that you find from confusing or hard in, you know, like, in the actual tool?

M3  18:37  
I didn't feel anything like, very hard, but I feel so when person tries to change something like numbers and the text field, he can he can, he feels that he can press Enter, it needs to be it will be updated. But sometimes I feel like user interaction is slightly different than what we expect. So that could be improved, I guess. Okay. So, like selection in the map. Generally, we try to select we don't know that we need to select inside the map that we are plotting, right. So I'm not sure if that can be improved, somehow. Yeah,

Kiran Gadhave  19:20  
I mean, that's basically the standard way like you can have a selection from outside the brush cannot go outside. Yeah,

M3  19:28  
like showing some like, exit symbol or something like when they placed the point routes and trying to select it will be useful, because the person might not know. Yeah, and like how accurately a person can like drag and select the points. Yeah, I felt there is a gap. Okay. When I select the columns of the bar chart, and I thought I'm missing on some points, you half the points are on a joke. Yeah.

Kiran Gadhave  20:06  
Okay, so now, last few, a few. lecture, we'll go through a few of the higher level tasks you did. And I basically want to get your thoughts on, you know, where did you feel more efficient? Or what did you feel was more intuitive in doing the task, eat the interactions or the code. So the first is the all the column interactions, like renaming the column, deleting, changing category.

M3  20:33  
Those are like really simple, I think interactive. Playing with him back to data frame is like, really straightforward and much faster than actual coding. Yeah, and And what about the second question?

Kiran Gadhave  20:49  
Yeah. So yeah, like that was first part like, manipulating the volumes. Yeah, man,

M3  20:54  
greeting, everything was simple, like feeling, in fact.

Kiran Gadhave  20:59  
Then what about the tasks where you had to edit specific values directly in a so yeah, even

M3  21:06  
that I felt a person has done a great job.

Kiran Gadhave  21:11  
And then for tasks. So going back to like the task you mentioned, where you know, you have to select a few points to remove them. So that you have to remove the outliers, either in the scatterplot, or bar chart. So how was that? Compared to pandas, where you could basically write a query like greater than or less than?

M3  21:29  
Yeah, like, for this dataset, I feel like purses is like, really easy. Like, we can select the doubt laid out layers, if we have like two distinct. It's out layers or like, too far away from concentrated wines. But if we want to exactly select the point, where there is like, much less gap. Yeah, I think it I would slightly miss out on some points, I guess. Yeah. But definitely, yeah, interactive was much easier.

Kiran Gadhave  22:04  
What about the tasks where you had to add a new column? Like visually add a new category column based on the existing ones?

M3  22:16  
Yeah, adding a new column is like, easy on both both of them. It's, it's easy for both interactive and normal as well. Yep.

Kiran Gadhave  22:27  
So. So what are your thoughts on it also, with purchases? After you're done with the interaction, you can get better data frames? So what are your thoughts on that where, you know, you can go from interactions to code? And then if you want, you can use the data? Great.

M3  22:50  
Yeah, that's actually a really good feature is what I feel because sometimes, we want to go back and have the actual data frame that we are working on, right? The history is like really useful. Whenever we want to use that data frame, we can just get it using the tool and work on that. And yeah, and come up with new analysis as well. I think that's a really good feature.

Kiran Gadhave  23:18  
Okay. So do you think you'd use something like, persist in your own analysis? To do analysis?

M3  23:27  
Yes, I would definitely use it. Because I felt it's like really? I'm a basically a UI guy. So whenever I see this type of feature, I think, great.

Kiran Gadhave  23:41  
Okay, so anything else you want to comment?

M3  23:46  
Yeah, nothing like I think many more features. Come up, is what I'm expecting. This is like a great start is what I feel it's new. Yeah.

