
My first question is pretty high level, though. What are your thoughts on? Like, the interactive analysis? You did? does it make sense?

M6  44:32  
Yeah, most of the tasks, I mean, most of the discussions we had, I already did try to think aloud during the like, the study session. But yeah, I actually really find it really helpful and I'm actually planning to use it on my own research. Okay. So yeah, it is very helpful, but to do most of the basic tasks, right, so I should say so yeah, if you want like doing a print unary pre processing like renaming columns, seeing outliers, having selection and adding categories. Those are super easy and very helpful. And yeah, these, these are mostly the first steps for any kind of data processing. So yeah, it makes sense to use process for all of those tests, because it's super easy.

Kiran Gadhave  45:21  
 Okay, yeah. And so yeah, like you didn't you haven't used purses before. Right. So you did the notebook after like a short tutorial session. So how was the learning curve? I6 mean, it was still like, very short. For the purposes of your task was a learning curve. 

M6  45:40  
I mean, to be honest, working with the like, already generated visualizations were super easy. I know what each of the buttons, does. They have meaningful glyphs in them. And yeah, I could literally tell that Okay. Before even you started to tell about it, I know that that name will be it will be exporting the variable name right from the table. So all of those were like super meaningful in a visual way. Yeah. But about like, creating those charts I wish I didn't do is that like, I don't know how to write this code yet. So the the caveat to that is, you know, there is no persist code, you have to basically write the altar code and procedures. So yeah, if I, yeah, I'm not familiar with bigger tire. But yeah, if I probably, if I knew we countered, then. Yeah, it's just like calling a function wrapped function on the chart of a culture. Yeah. So yeah, that shouldn't be that difficult. 

Kiran Gadhave  46:46  
So yeah, I think it's just like so one thing that we tested was basically you can go to the documentation page, and they have like, an Examples page. And you just have, like, all the other visualizations, and you can copy any of the interactive ones and just give it to persist, and it should show it. That's super cool. So yeah, you know, like vega altair and it's always good to learn vega altair, because it's like the only instructor you use. Okay, so the interface itself, was there anything that was confusing? Or you know, really hard to wrap your head around? 

M6  47:21  
No. Okay. straight answer is no. Because, yeah, I found the most of the after the very short tutorial, I found most of the tasks very easy and straightforward to gather on persistent.

Kiran Gadhave  47:33  
 Okay. So now we'll go through some of the high level tasks. Basically, I want your thoughts on, you know, that it will feel like more, you know, more efficient, or what was more intuitive versus store pandas

M6  47:47  
Definitely for pandas, I, I looked grinded through all the documentation directly, like a lot of Google searches. For it is surprising. 

Kiran Gadhave  47:57  
Like you actually, pretty much knew most of the functions. You just tried to look up syntax. So 

M6  48:02  
yeah, I mean, I worked with pandas before, as I said, but still, I had to look for like, you know, that this one I ended up with, I was reading, and I knew that I sometimes miss with the parentheses because it has like, yeah, presidents issue. But later, I found out after searching that, it is just a percent, not the and. So yeah, these kinds of issues always hits. I know what I want to do, but I still getting stuck, because of only the documentation. I mean, does the tax. But with persist, I don't have to write anything. If I have the visual. I mean, I don't have to do any kind of processing on pandas. So most of the time, it's just one button, which is the visualization. If I could do it, do the scatterplot. I'm sure that I can filter all the data. I don't have to do these kinds of things. Yeah. So yeah, that's really good. Plus, okay. 

Kiran Gadhave  48:59  
Yeah. And you know, like just going on the highlighter. So what about like the Tasker with the the column manipulation operations? You know, like renaming and yeah. 

M6  49:12  
So yeah, these were pre very straightforward. And I'm very happy with all of the column manipulation tasks, like, yeah, like, as you say, removing changing column names, editing row fields, like searching for like one p2p And then going to the row and directly changing it. I do need I'm pretty sure that I need to do like at least 10 Google searches before doing that too, in pandas code. So that's a really good plus. The only one thing as I already said is that it's probably a problem with all the visualizations including our tire is that I'm just just not comfortable with visual selections. Because, as you know, there are edge cases with human errors in the right so me.

Kiran Gadhave  49:59  
Basically if you have a better way to cleanly select data without coding. No. I mean, you know, something like a range selection. So something like range filter or slider or something.

M6  50:12  
yes, I would really love that. Like, in this search, maybe there's a toggle for like query search and normal global search origin search. So if I toggle on the query, then it will look very similar to like searches like this as a, like a type query, query type of search, and then I would be sure that okay, it is selecting between these ranges, that will be really helpful. Also update visual selection. 

Kiran Gadhave  50:43  
So the so as to be clear, your major gripe was the visual selections are not accurate. Yes. But it's also annoying to actually filter out more. And I would say, yeah, so you know, like a better way to, you know, specify exact filters would like address both?

M6  51:04  
 Yes, I just thought it would be the sweet point. And yeah, for the other selections, like the one of removing outliers wasn't an issue, because they are outlier, they're too far. And you can easily select that and remove that. I can do it even with the scatter plots, or even on that direct table here. 

Kiran Gadhave  51:25  
So what about So research? Talking about this one point? So would you say you prefer, like for something that's obvious, like outliers? Just how would you do that without? without persist? So how would you do? Would you usually just plot and then try to see which range to remove?

M6  51:46  
 Oh definitely? So most of I mean, for these? Yeah, also, for these data? I don't know what the data has. So I don't know the data is the first time I'm looking at it. So for obviously, I can see all the data in the table. Yeah, I can see at most, like, 10 100 100 rows. 

Kiran Gadhave  52:03  
So yeah, like you have to scan 12 pages. Yeah, yeah. 

M6  52:06  
So and obviously, I can't do that. It's infeasible. So what I would definitely do is do the scatterplot. Even with pandas without persist, right. And when I then I would mentally select okay, for without persist, I would maybe, maybe make up a range where the outlier stands, and then go again, to write the code to just to do the filtering and then remove it. But with persist, I don't have to do the second part. Right, because I have the scatterplot. And inside the scatterplot, I can just directly do the outlier filtering. Yes. Really? Good. Yeah. 

Kiran Gadhave  52:50  
And so what about for the tasks where he had to do assignments? 

M6  52:58  
Yes. Um, so yeah, there's a I should put a note here is that for basic task assignments, like this one, where I have the I can select and then assign tasks. I, there are some times while my own my own research, there's something like, I had to do some kind of like, what do I say? Like, a little more extensive filtering, or as extensive assignment, for example, be six engines, let's say for this.

Let's say I want four cylinders, and eight cylinders, but not any other cylinders to be VCs. It's, uh, it's not like, doesn't make sense. But I, I want to do that you can.

Kiran Gadhave  53:46  
 So why would you not be able to do that? Versus because you can select individual bars if you switch to like a point selection?

M6  53:53  
 Yeah, I think that's a bad example that I did. So I can definitely do these. And so basically, you're saying you might have conditions which are, you know, not based on like, one particular value, but something like not based on a visual selection? Yeah. Right. Something or domain specific? Yes. For example, I might want to like, yeah, maybe hopefully, this will be a good example. But still the tutorial notebook that probably has it. Okay. The first one? Yeah. So let's say I have I have some data points that has no assignments. So I don't know if there is any way on persist to do that. But maybe I want to assign some category to the no assignments. Yes. So I think so. 

Kiran Gadhave  54:37  
Yeah. The one way to do it is you know, like, so this one. So you can basically have more vega altair code. So it lets you do selection based on legend. Okay, at that selector, but you can click on the legend and it will select anything that matches your selection. So I don't have that code in here. Yeah, so yeah. essentially like, you know, but you would prefer something like that. 

M6  55:05  
Yeah, I mean, something like, again, it goes to the my demand on doing a a query selection, right. But yeah, let's say I want to transform them and then do the filter. Right? Yeah, well, let's say, I want to transform them from normalize them from zero to one, and then see, okay, point A to about point it will be a category. So that I can do visually in the past historic altar, right. But that was the adding the pre selection would solve that as well. Because I can do the query, select, it will select that, and then assign category. Yeah. 

Kiran Gadhave  55:40  
So that actually will fit into the next part. So you mentioned something like, you know, you would want to do data transforms first. So what part of the percentage workflow is, you know, you start with a data frame, you will give it to persist either for a chart or for a table, and you'll get an data frame out again. So you still get to do anything you want to data frame, and then again, go back to an interactive mode. So what are your thoughts on a workflow where you switch between this so for the tasks that are good with interaction, you do demonstrate interaction when you switch to Python for something that's, you know, to ? Or you want accuracy? So you switch to Python be accurate, and then come back? 

M6  56:24  
Yeah, one thing is that, so if I just change, let's say, in the code here, let's say you change that will persist DF six, it will not directly update figure, right? I need again, 

Kiran Gadhave  56:39  
so you can, so you don't directly have to figure but you can do like PR dot persist chart, and then instead of data equals DF, you can do data equals DF, DF Oracle table, you know, you can just create a new table out of it.

M6  56:53  
 Yeah, yeah. Yeah. That has, actually I think, yeah, overall, some of the, there are some of the, like, requests or requests that cannot be served. That I think that I wish I had like that. But it's not like just feasible to do in visual, in per se, or overly visualization. But yeah, overall, I think it's a great alternative to writing raw Python code. Okay. 

Kiran Gadhave  57:27  
So you actually mentioned this already. So you would like to try persist in your world? Exactly.

M6  57:34  
 Probably like today or tomorrow? Yeah. I mean, okay. 

Kiran Gadhave  57:38  
So, and yeah, so the next question would be true. Did you find the so in terms of like, you know, for kids, given like a certain task, do you find the interactive tables and interactive charts more valuable than the usual, like static charts? Or just looking through the data frame? 

M6  58:01  
Yes. So I mean, what I would do if I get a custom to persist, is that instead of just putting printing TF, most of the time, I would just maybe create a macro in Python, I mean, create a lambda in Python is globally on top, and use like vs chart, per CTF, or PDF. PDF data, it was TF. Yeah, I think so. Like we are working. So the plan is you can have like, Jupiter and magic command. So if you have the percent of the top, like, if the output or the return value of the sale is a data frame, it just makes it a percentage table directly. Yes, that will be really good feature. Is it already here? Or? It is but like, I still have to test it. But like the magic is there, but we still need to work on? Yeah, yeah, that will be good, because then I can totally get rid of an interactive pandas table and just work on it. And quickly, whenever I see it's kind of like a mental What do I say? Persistence between mental and physical work I'm doing so if I see directly that okay, I need to remove this. This happens a lot to me that is that. So whenever I see a column name, okay, this is there is something wrong with the partner, I need to fix it. I go to the code, I look to the documentation, and then I forgot which club name I need to rename. And then I again had to look. So the context switches. Yes, every time that yeah, the context, which actually sometimes takes some time for me. Yeah. But in interactive DS, I can. Yeah. Okay. So yeah, final question. So anything else you would like to comment on? Most of the things I already mentioned, so I don't have any more comments on it. Yeah, I just don't have it. Okay, so yeah, great. 

Kiran Gadhave  1:00:03  
So I think you're done, I will stop the recordings quickly.done this.
