
Kiran Gadhave  31:57  
So, you know, like, to start this part with, what are your first impressions of basically this interactive way to analyze data in order go?

M5  32:10  
So my first interaction for the persist was like, it was helpful. Okay, it's sorted data, okay, is like with how the LDA was doing, but the thing that it differentiated was that I could able to, like, select the data, updated data, okay, whichever I select, which was also reflected in the data. So that was something unique. And like, after doing the code, like, I think, like, it's a very useful tool, okay. Because like, without knowing the code, I was able to create a data from the, like, user interest interaction only. Okay.

Kiran Gadhave  32:44  
So, you know, like, so on the similar line. So, you basically saw purses for the first time today, you I gave you a mini tutorial on how to use it. And from there, you did the tasks. So how was the learning curve? Yeah,

M5  33:00  
so I had to I saw the process for the first time, okay. And the learning curve was very easy. Okay. Like, it was just one one times to see like how the things are, okay. And you're able to pick up because each of these symbols like, are good, okay. Like, the symbols describe, like, our pictorial, pictorial description is good. Okay, I could see like, what this desk like symbol could do. And like each of the place, things are placed neatly. So it was helpful and easier to learn. Okay. Anything that

Kiran Gadhave  33:34  
was confusing or hard to understand? or something's you know, which didn't make sense to you immediately.

M5  33:43  
Yeah, for my point of view, like it provided too much thing. Okay, so yeah, it's so like, when I was selecting something, okay. It should like also that was Altair. Okay, we're not able to select things properly.

Kiran Gadhave  34:00  
So, like better selections,

M5  34:01  
yeah. Better selection, like how we normally select something in the windows. Okay, something that would be a better thing.

Kiran Gadhave  34:08  
Okay. So basically clicking to select Yeah, okay. Yeah, that makes sense. Okay, so, you know, like, I will ask, like, now we'll go through like some of the higher level tasks. And I want you to think about when you did the task between versus to sit within pandas like, so, well, did you feed more efficient where to deviate? I would struggle stick to code or you know, I will just do this interactively. So first type of password column manipulations, you know, you rename the columns, you deleted some columns, you change the architect, you change categories of the column.

M5  34:49  
Yeah. So, the task I did like on the position is be effective. Okay. Like I struggled in the Panthers thing. Okay. But I also think like, it's also important, like, the procession made it really easy. But it also added any information which I want to be able to know, like, from a panda's data like, because I struggled in Panda, I tried to print it different data types. Okay. Which, if not, I wouldn't have known the data types of different columns, which were recorded subsequent questions.

Kiran Gadhave  35:23  
Persist gives you the datatype. So if you hover on the column names, okay. I mean, that's yeah, it'll change the category. That's how you check what type it is currently.

M5  35:36  
Right. Okay. Yeah, if it gives, like, because I didn't cover on every call out. Okay. So if it gives, then it's like, a very good, okay. And then I will use like, persist, and no other objects in the persist.

Kiran Gadhave  35:48  
Yeah. And so how about when you had to edit individual values?

M5  35:52  
So when you admitted dissents? Individually, so for example, the first task it was removing the courts. The second one, it was removing the letter P. Yeah. So again, like the purchase, like made it feel like a simple action, okay, which is like, the user interface was, like, descriptive enough, okay, to search and like replace something. Okay. Yeah. And it's compared to partners I need to like again search for the code. So and also like, I need to print to like a check visible and my code is visible or not, but in persistent the changes from layer three routing, so it was

Kiran Gadhave  36:34  
okay. And then how was your experience and your tasks where you had to remove parts of the data, for example, remove outliers or remove certain dates.

M5  36:52  
Okay. So, like, again, it was, like, efficient and easy on the persist, because like I could able to see like the outliers, okay? Because in the merge because you can't see in normal pandas, you only like, play with numbers and you can't you do not have the whole picture with the person you had the whole picture or like, plot, okay, so which made it easy to like, see the outlier? Okay. And since like the changes fertility, so yeah, it was efficient but in case of pandas I need to like I will not like need to see which one did I have removed and need to increase the plot to see like, did I remember correctly or not?

Kiran Gadhave  37:31  
And so so one of this was removing the outlier so for avalanche when it was removing like, elevation feet, which were upside Ranger futile for the video game status, it was removing negative values. So it seems you struggled with that part, right. Just skip the to a question to a right or Yes, yes. Yeah, I suppose they're just removing the negative values. So some, you know, like you just briefly talk about so why did you skip that question? Where were you stuck what was it called? Yeah.

M5  38:06  
So in ponders like, I was stuck at question number two way of removing the negative as us so the problem I was facing I was trying to use data from the drop okay. But it was not working as a like as it was intended to or like not able to use it properly. I tried multiple things, but yeah, it was not working. Okay.

Kiran Gadhave  38:30  
And then the final task tweeted was adding new columns and assigning categories for them. So yeah, your thoughts on

M5  38:40  
again like this This task was again like Basie on the procession okay because in the other thing like in the Panthers I need to create one like temporary function or like util function, okay and then apply the data data data from the top line okay, which again requires the use like knowledge of code, okay. But in case of persist if you don't know it, you could just like play with it and you will be able to change the categories and or like add to categories or new thing immediately like with a few thoughts, okay.

Kiran Gadhave  39:19  
So, so, one thing that person has allowed us to do is you know, interact with something and then switch and like, you know, like create a data frame from it and you know, data frame is a Python variable, so, you can start working with Python. So, what are your thoughts on like a workflow like this where you switch between interactions and if something is not possible with interaction, you go to code and then you come back to interaction again, for tasks which are easy with interactions?

M5  39:49  
Yeah, so few things. Is that like, a file? Okay. We are, respectively like only talking about pandas, which expect

Kiran Gadhave  40:00  
to point us, right? Yeah. So basically now and like now, this is not a comparison, I'm mostly asking you. So what percentage so so the way percentage work is you take a data frame, and you create a chart or a table out of it, then you interact with it. And in the output, you get the new data frame, right? So you can have a workflow where you go from data frame to like an interactive table or interactive chart, then you get a data frame, you do something with text, a Python or pandas with it. And then you again, create another interactive chart. So you have this workflow where you go back and forth. Okay, so what are your thoughts about our product is, yeah,

M14  40:38  
so my

M5  40:39  
initial thought on this question is like, I would go to partners, okay, because, like, once I've made the changes, okay, then it remains like, I just need to execute if sell, but like, I know, like, the persist also does the same thing. Once you make that the user interact, like using the user interface, the changes remain there. Okay. Which is why like, I feel like, since purchases new, I may be hesitant, but yeah, I will, like, dry the process to where I need to change the columns. Okay, where am I like changing the columns or like, need to analyze the condoms. Okay, or need to remove certain data. Okay. Yeah, again, on the point of certain data points, I would like us proceed, but I will, like be more confident with the actual code, because in the process, we were seeing that some of the points were like, not captured by the scatterplot. Right. Okay. So which basically better like if we use like a pandas. Okay, switch should capture the views?

Kiran Gadhave  41:41  
Well, that kind of, like, brings us to next question, I think you answered some part of it was, will you actually use purses? too? Few. Want to do some data versus has like, at least a starting point?

5  41:53  
Yeah. I would like to use purchase, like for making inferences. Okay, on the data. Okay. And like, updating the data is like, for the trivial task, like such as updating columns or changing the name fields and something? Yeah. Good.

Kiran Gadhave  42:11  
So let's see. So did you find the interactive table or interactive charts? Valuable? Why? Yeah. I mean, like, if yes, why and if not, why?

5  42:26  
Okay. Yeah, the persist, like, interactivity was in charge for very, very, like, a thing valuable, okay, are very valuable. Like, first of all, like it made very easy, okay. I didn't like think twice, like what I'm doing, like, I just follow the symbol. Okay, since they were descripted, I just do it immediately. Anyways, the badass like, it required a knowledge of code. And also you need to like, again, verify what you did, in case of person like they were real time. So yeah, it made like, the changes very seamless, effective, and like the changes were also visible. Interesting. So yeah, yeah, it's definitely valuable. Okay,

Kiran Gadhave  43:03  
great. So like, just the last question I have is anything else you'd like to comment on? Or like any feedback?

M5  43:15  
Yeah, I think. Like, the persist is very good. What I was thinking, like, since purchased, is like, good for seeing the data. Like, could it also be, like different views? Also, because I would like to change data, like some of the data, I would like to like, if I'm creating some data, okay. Doing some value is good. But also, if it provides me a chance to update the data from the chart, in terms like if I increase the like, I'm how I updating the data, like if I move certain points. Yeah. Yeah, it's like this will give more interactivity to the

Kiran Gadhave  43:56  
pretty tight security. Interesting. It could be really cool to have something like that. Yeah, so I think this is all I have for study and just stopped recording
