

 So yeah, basically what I want to know, are your first thoughts on the  interactive analysis that you did with the first notebook.

M7  1:00:33  
It makes every task pretty much easy. Like changing the type of changing the category type, or adding new categories or removing animals from beta, like, they were very much easier in it and coding. And for coding tasks. Basically, pandas have a lot of functions. And it's, it's not like you're not going to use a particular function for a particular task. Yeah. So for for, like, basically, what everyone does is, they just look up in the internet, and then use the function and then they forget about it. So it takes a lot of time, unless you are doing using Pandas for regular use, right. So for people who are beginner, it can save a lot of time. And for people who are using it, like for everyday, it will save time for them as well. And like what I said they just shrink the time taken for tedious tasks. Yep.

Kiran Gadhave  1:01:58  
So, so you never use the persister station before, right? So just before you read the notebook, I gave you a short tutorial. And based on that tutorial, you did the task. So how was the learning curve? Yeah, the

M7  1:02:12  
learning curve was very, very steep, like, we, like I got the basic features with so short time. So if I use it for a week, then maybe I will be more efficient with my tasks.

Kiran Gadhave  1:02:34  
Okay, so now, you know, like, we'll go through some higher level tasks that we did and then what are your thoughts on you know, for that particular task between like pandas or between the interactive analysis, where did you feel which did you feel was more intuitive for that task or more, you know, make made more sense. So, for example, renaming the columns. So between like writing code and using the interactions, where which made more sense to you, which where you feel more efficient.

M7  1:03:05  
Obviously, the interactive part was more efficient, efficient, because we are renaming like one or two columns, then then it is more efficient, but as well. But if you want to automate something, then you cannot obviously use that.

Kiran Gadhave  1:03:27  
So can you talk about what you mean automate? Like,

M7  1:03:31  
for every column, we want to add a suffix or prefix right then obviously,

Kiran Gadhave  1:03:40  
then what about the tasks where you had to basically find and edit particular values? So for the one data set, you had to find the sort of like suffix p and for other users find the suffix with quotation marks? Yeah.

M7  1:03:57  
For for small number of changes. I think you can search and manually type characters, but for large datasets, where you will frequently find those minimalists then you have to obviously use the code.

Kiran Gadhave  1:04:16  
And so what about for the task where you had to basically you know, look for outliers or click some anomalous data, select it and then remove it

M7  1:04:31  
for those tasks it felt to me that if there are the anomalies are easily distinguishable by code like you like uh, you write a single line to filter out some of the data then then these are pretty much close.

Kiran Gadhave  1:04:57  
Right and on you know, like in Observe your experience. So basically, how hard or easy was it for you to write that one line of code versus do it with interaction

M7  1:05:09  
interaction doesn't require any prior knowledge or anything like that. But I, I used to that function a couple of weeks back, so I had a kit on the back of my head. So I did easily, but if someone was to try with it, the filtering process hurt and he he has to spend some time Googling,

Kiran Gadhave  1:05:36  
and whatever. So usually the way you do our classes, you have a data set. And then so how do you know that outcasts in this particular place are so you what do you do first, you usually plot it first to see,

M7  1:05:53  
obviously, the visual effects, like using the visuals, where I can easily find out where the outlines are written, I can estimate the threshold where beyond which we will count them as outliers. So this is where the visual visual effect, or visual aid helps a lot.

Kiran Gadhave  1:06:15  
Right? And so what do you think about the interactive approach where you know, since you're going to create the visualization and events, you just interact with the visualization to make first set the threshold and then remove everything of that threshold?

M7  1:06:31  
Yes, for for the, these tasks, the these also helped. And, and for these kinds of tasks, I don't think scalability is an issue. Like however large the data set is, you can always use a visual, like the brush to filter out the data, it doesn't have any issue with scalability. Unlike that, one that you have to change to get the

Kiran Gadhave  1:07:00  
word you mentioned, where you have to append like suffix or prefix. So yeah, that makes sense. So and then, like, the last task, which we did was, you know, selecting parts of the data and assigning them a category or like, you know, classifying them into two or three parts, such as we did with like, short, average and long games, or like, start middle and end of season. So what do you think about paths laid out?

M7  1:07:28  
I could not finish the task when I was reading the code, because I, I didn't remember, I don't remember using this. These methods, correct recently? Yep. So if I would have some practice in the in the in this type of tasks, then maybe I would have done it quicker, but of course, it is no match for the interactive, right?

Kiran Gadhave  1:08:03  
And so So, you know, even if you let's say, assuming, you know, like, how to write the code, how would you go about classifying it yourself? So I just give you a data frame, and tell you, I want you to classify this, you know, like, look at the x and y axis of some egg, like, look at this column A and column B and classify it, how would you start classifying? without, like, you know, without me telling you the ranges and stuff, so how do you determine the range on which to classify?

M7  1:08:38  
Well, yes, I also need to use the graph for this test. So

Kiran Gadhave  1:08:45  
you usually start with plot according to the graph, and the thresholds again, yeah, great. Okay. Yeah. So, yeah, so, you know, so my next question is the so now you're done with like the task, so, kind of coming back to just the interaction part. So, well, so, we saw that like, you know, the with persist, you start with a data frame, and then you make like an interactive output, and you interact with it and result of that is also another data frame, which, you know, you can potentially again, make interactive, so you kind of have this workflow, where you go from data frame to interaction to data frame to traction back again. And in the middle, since you have a data frame, you can write any Python code to do something advanced which is not possible interactively. So what do you think about the workflow like this, where you get to you know, for the parser interaction is easy use versus the part where coding is easy, or system allows you to generate a variable which you can write code on.

M7  1:09:50  
Well, if I, if I use it in code, then I would have to, like rebuild

Kiran Gadhave  1:09:59  
that Part of the code to add. So for example, let's say you interactive like, so we load a data frame. And you know, you load it with an interactive table, and you see a few column names have, like, you know, some mistake, you edit and fix them. But then now you decide, you want to add, like, underscore a to all the columns, that that purchase, lets you you know, create a new create a data frame out of it. So your new data frame will have all the fixes you did to the column names. And now you can write the code to add as a suffix. So you basically did, editing a few columns was easier in interaction. So you did that. And then you went to code because you think adding suffixes easier in code. So because you have to do it everywhere. So what about a workflow where you keep switching? So what do you think about that?

M7  1:10:57  
Yeah, in Jupiter, we can always do that switch between because persist is on top of working on top of the world, Jupiter Jupiter, like, so you always have the option to do the code, right? Yes, it allows more flexibility to switch when when whenever you want to do a task that is suitable for code and whenever you have a task that is manually so do for manual editing.

Kiran Gadhave  1:11:35  
And so the next question is, would you use something like purchased in your own work, you know, or like parts of your work when you're doing data analysis?

M7  1:11:49  
I am. If I'm doing a data analysis, I would obviously use it because it saves so much time. Like with with with complicated, detailed stuff, like remove the categories and categories, then it would like, speed up my work. Okay, obviously. And

Kiran Gadhave  1:12:12  
then, you know, so we have just like two overseers. Okay. So Well, the next question is, did you so based on like your previous workflow, did you find the interactive charts and the interactivity was valuable. Just being able to interact with the charts and stuff, you know, just plotting static charts and looking at them? Yes.

M7  1:12:37  
Because, for, for, if I use regular Jupiter, I would not be allowed to select some part of the data and only focus on that, I have to write code to get rid of the rest of the data. And here, it allows me to do the task very quickly. And of course, I have the option to go back. If I if I do something wrong, then I have to, I have the option to go back with one click. Yep. But if I do it in pandas, then I have to, like uncomment some lines, and maybe I have to go back using the previous commit. Right? If I use your if you prefer something, yeah, like large chunk of changes, then I have to use the commit back.

Kiran Gadhave  1:13:34  
And do so when working with Jupiter, do you often use, like, commits to kind of keep track of big changes? Or do you usually just commit at like, end of the day or something?

M7  1:13:48  
If I if I when I try out new idea. And I think the idea is, is a huge change from what I'm doing right now then I do a commit.

Kiran Gadhave  1:14:00  
So since you know you already use commits, or do you think about the part of persist, you know where going back in tree? Sure, like that's one part. But when you go back and do something, it creates a branch so it allows you to switch to what you're trying earlier or create an entirely new branch. So what do you think about the six branches it creates? So if you want you can go back to the notebook and you just go back and did you go to tutorial? The first tab no in the first tab in Jupiter so here yeah, like so the way so when you go back at some point and do a new interaction it you know, instead of removing the few like interactions you did earlier, it adds a new brand. Yeah. So since this is like you know, similar to like you commit that you can create a new brand new privacy. Yeah, but do you think about basically having your feet directions are having your analysis showed like this with multiple approaches? Well, when

M7  1:15:04  
you use Git in command line, then you have to rely on commit messages to figure out why, what, where you branched out and where you, like, commit numbers and commit IDs and commit messages to figure out what when did you branch out? And what changes are followed through that branch, but here you have the visual aid to know also you have the summary. And so you can easily find where in the history you want to go back. Right? And you can, with one click, you can go back to that. Okay. Yeah.

Kiran Gadhave  1:15:47  
Yeah. So, last question is basically more open ended. So is there anything else you want to comment on? All the feedback for us?

M7  1:16:00  
If you are asking me to, like, for more features, so it is relatively short time to go.

Kiran Gadhave  1:16:11  
And I you know, it doesn't have to be features, it was more in terms of, you know, like, what, like, so when you first looked at this and saw it, like, was there any expectation, which, you know, expectation you had with this interactive analysis, which, you know, did meet or which are something that you definitely liked and would like to keep using. So it just doesn't have to be like new features, it can just be the final comments as well.

M7  1:16:44  
I find this feature very useful, where you can just like, like, use the table interactively, and change the data, as well as the type. Because when I did try to do it in code, then I found a lot of difficulties, like having control of which cells I'm right now. Yeah, because pandas have so many notations and the tutorials they use, like, the complex lambdas. So if I can accomplish the task using, like, simple for loops, but but I think pandas is not designed to do that efficiently. Yes, it

Kiran Gadhave  1:17:29  
is not. It expects you to write lambda lambdas. Right. Yeah.

M7  1:17:32  
So, and the code looks ugly as well.

Kiran Gadhave  1:17:36  
Yeah. I mean, yeah, that's, you know, like it data analysis. It's usually ugly code is fine, as long as it gets colder. But yeah.

M7  1:17:45  
So for that part, I obviously give it a 10.

Kiran Gadhave  1:17:54  
Yeah, I think this is pretty much what I had in terms of interview. So again, I think I'll stop recording. So first, like, high level question on what are your first thoughts on, like, the interactive data analysis that you do? I
