ChatGPT Code Interpreter vs Tableau | ChatGPT, Tableau GPT & LLMs

0:00Hey, it's Tim here. Over the weekend, the

0:02chat GPT open AI, the company that runs

0:04chat GPT added

0:06the code interpreter for chat GPT. This is

0:09a separate model from GPT for that we've

0:12been using

0:13or GPT 3.5. If you're using the free

0:15version, it's a separate model that is able

0:17to do mathematical

0:19data analysis because it's using Python

0:21behind the scenes. In this video, I put it

0:24head to head

0:25with Tableau. As ever, let's get stuck in.

0:27Okay, so here we are, we're in GPT four.

0:30And if you're

0:31wondering how to enable this, you have to

0:33go into your settings. And there's a little

0:35beta section

0:35there. When you click on that, you're able

0:37to click on the toggle to enable it. And

0:39then it

0:39appears up here on the top right hand side,

0:41I've actually been using plugins already,

0:43the plugins

0:44have been around for a while. But code

0:46interpreter is a new feature. And that's

0:47available here. And

0:48you have to choose one of the models. If

0:50you use plugins, for example, you're not

0:52able to use the

0:53code interpreter. So these are the plugins

0:55that I've been playing around with, I found

0:56them

0:57extremely useful for various things that I

0:59do. But I can't use those plugins. If I'm

1:01trying to use code

1:02interpreter, at some point, I'm hoping chat

1:04GPT enables the capability to use plugins,

1:07and the

1:07code interpreter, because then you could do

1:09some really crazy things. Okay, so I'm

1:11going to put my

1:12face up here. Now what I want to do is see

1:15if I can do the basic analysis on this

1:17dashboard, this

1:19is the default dashboard that ships with

1:21tablet. And so what I want to do is try and

1:24see how close

1:25can we get to some of these numbers? Okay.

1:28And is the analysis that it's going to do

1:30actually up to

1:31snuff? Is it going to work? Is it reliable?

1:34There's already a couple of glaring issues

1:36with this whole

1:37process. For example, the only way that I'm

1:39able to going to be able to do this is to

1:41upload my

1:42data to open AI. And for most people, that

1:44's already a big no no, you shouldn't be

1:46doing this

1:47work with work data with any data that you

1:49don't trust. I'm doing it with superstore

1:51because it's

1:51sample data. This is the demo data that

1:53comes with a tablet. So we're happy to use

1:56that. So

1:56what I always like to do is to discover the

1:58features that chat GPT uses by simply

2:01asking,

2:01Hey, can I do this thing? So hi, can you

2:04analyze the data for me? Okay. And this is

2:07actually one

2:08of the big things that's been in tweet

2:10threads. And I'm always like, Listen, this

2:12tool is not

2:13going to take over your job, right? Like,

2:16these tools are driven by intent, and they

2:19don't have

2:20the autonomy to come up with that intent

2:22without being given some sort of

2:23instruction. So as long

2:25as these schools don't sort of just go off

2:26and decide to do things, which by the way,

2:28if they did,

2:29the world would be panicking, because then

2:31they could go off and decide to take over

2:33things that

2:34people people, people, people are, you know

2:36, concerned about, but nonetheless, here we

2:38are.

2:38So this is what it can do. What is the

2:39nature of your data? What are the key

2:41variables? Are there

2:42any specific questions you're trying to

2:44answer? Also, if you have the data ready,

2:46can you upload

2:46it here, and we can get started. So I

2:48absolutely do have the data set, I actually

2:50have it on my

2:51desktop. So we'll go ahead, we'll bring it

2:52in, you'll see me drag it in here, and I'll

2:54just put

2:55it inside of the text box. And I'll say,

2:58Okay, here is the data. Okay, let's just

3:01have that

3:02conversation. And you'll see that it's

3:04working. This is the crazy thing. When it

3:07says it's

3:08working, it just says, hey, look, finish

3:10working. And if you actually expand this

3:12box, it's going

3:12to type out a bunch of things, it's going

3:14to tell me that it understands what's in

3:16the data.

3:16And this is already pretty impressive. I

3:18mean, I saw this, and I was like, wow, this

3:20is, this is

3:21pretty interesting. And so what we can do

3:23is we can actually open this up. And you

3:25can see what

3:26it does, it wrote the Python for this. So

3:29import pandas as PD. So basically gives it

3:32an alias,

3:32and then it loads the data using that alias

3:35, so PD read the Excel, this is the file.

3:38And it's

3:38essentially mounting that file that we've

3:41uploaded. And it's able to then, you know,

3:43load up the

3:44headers. And that's what it's basically

3:47using. And then it's this, this is what's

3:49crazy, it's actually

3:50done some initial analysis, it's actually

3:52gone to see like, okay, what can we get

3:55here, here's the

3:56customer information. And so it's gone, got

3:58customer ID, customer name, customer

4:00segment,

4:01it's gone and got the order information.

4:03And it's got that kind of nicely grouped up

4:05. It's got some

4:06country region specific information sub

4:09category. But ultimately, all it's doing is

4:11just listing the

4:12fields. There's nothing sort of, you know,

4:14crazy about this is just I think this is

4:15just the order

4:16the data comes in. So, so far, so good. We

4:19know it has the data. Now we can start to

4:21ask it the

4:21basic questions. So the basic question I'm

4:23going to ask, and I've checked that this is

4:25the right

4:25version of Tableau, the right file, because

4:28each version of Tableau comes with its own

4:30version of

4:30Superstore, I've checked it, so we should

4:32be getting exact numbers. So I'm just gonna

4:34ask it,

4:35what was the total sales in this entire

4:36data set. So let's go ahead and do that.

4:38We'll look past the first steps. What was

4:42the total sales in this entire data set. So

4:46I'm giving it

4:47context. And I'm giving it sort of a

4:49purpose. So let's go ahead, we've given it

4:51intent.

4:52Let's see what it comes up with. There we

4:54go. Now. It's done it in euros. I don't

4:59know if that's what

5:00we wanted. I had it in pounds. And there

5:02was nothing in the file, suggesting that it

5:04was euros,

5:05but 2,938,889. 2,938,889.06. Oh, God, let's

5:14, is it correct to be doing that? I mean, I

5:17'm going to

5:18assume that it's correct. If I just go into

5:20the sheet, you probably want to know, how

5:22do I know

5:22it's correct? Let's go into the sheet. And

5:25let's just right click format. We'll just

5:28go ahead and

5:29say measure values, we'll just choose a

5:32number, number custom will add two decimal

5:35places. Okay,

5:36actually, that's not going to work because

5:37it's the sales value, sorry that I need to

5:39actually

5:40adjust here. So we'll go currency custom

5:42two decimal places. And it's a little bit

5:45small.

5:45So we need to make it larger point zero six

5:47team. So it was it was actually correct

5:50point zero six.

5:51That's what it's got there. Two decimal

5:52places. Okay, we trust it. It works. It's

5:55done the total.

5:56Pretty interesting. Okay. That's pretty

5:58basic. I'm going to assume it can do the

6:00other things. Let's

6:01go ahead and ask it the profit ratio. Now

6:03the profit ratio is more complex, because

6:05what it

6:06has to do is it has to go and do the

6:07calculation for profit, it's only got the

6:09base data, the the

6:11profit ratio is not in this data. So has to

6:13go and figure out the margin, then it has

6:15to aggregate

6:16everything and come to this percentage. So

6:18let's go ahead and just check those fields.

6:20So you can

6:20see here there is no profit, there's a

6:22profit value, but there's no profit ratio.

6:24So let's go

6:25ahead and say ask it, what was the profit

6:30ratio across the entire data set as a

6:35percentage,

6:37I'm being specific about percentage because

6:39I think it does help it sort of get things

6:42better.

6:43And actually, that's how you'd so that's

6:45how the numbers have actually shown. So let

6:46's go ahead

6:47and see how long this takes. And we'll look

6:50at the working as well.

6:52A 12.69 absolutely nailed it. So let's talk

6:56about 69. Let's let's just go back into

6:59this,

6:59I want to see if this is being rounded up.

7:01And I actually don't know about it. So let

7:03's go ahead.

7:04Let's go into the profit ratio. This is a

7:07percentage 12.69.

7:09That is so good. I don't know why that

7:12makes me happy. But this is crazy, because

7:15previously,

7:16this was not the case. Okay. So this means

7:18that for every one euro, it still thinks it

7:20's euros

7:21in sales approximately this amount, this

7:23amount of profit, which is which is perfect

7:26. So okay,

7:27it's done the basic maths. This discount,

7:30this discount is the average discount

7:33across the data

7:34set. So let's go ahead and ask it that.

7:37What was the average discount across the

7:43entire data set?

7:47So this is a difficult one, because the

7:49context isn't always clear. When you do

7:51averages,

7:52you have to like assume something. And in

7:54this case, I think it's done the right

7:55thing.

7:56It's gone across the entire data. And I

7:58think it's just aggregated it all down as

8:01tableau would have

8:01done 10.31. Again, I think this is going to

8:05be correct. I think we can confidently say

8:08this is

8:08pretty easy for this thing to do. And if we

8:11say this is a percentage 10.31 is the exact

8:14answer

8:14the tableau has. So at least that face

8:17value this top level here, easy stuff for

8:20chat, GPT,

8:22I'm not even going to bother with the

8:23others, because those are just sums, it's

8:25going to get

8:26it right. Okay, now we get a little bit

8:28harder. Okay. Now, I want to try and see if

8:32I can get some

8:32of the information that I'm getting in

8:34these charts, because these are sort of

8:36more complex

8:37questions. Okay. And I don't want to just

8:39do that, I want to go in here and pick a

8:40filter. So we'll

8:41go ahead, select central. So we'll narrow

8:44this down to central. And we'll give it a

8:47time range.

8:48Let's go ahead and say that we want to go

8:50from the first of this day. And what I'll

8:53do is I'll

8:53copy these dates into chat GPT when I do

8:56the query. And on top of that, I want to

8:59know the value

9:01for Germany. Okay, so Germany, Bavaria here

9:06. And I think it's, well, this is

9:09interesting. So this

9:10is this the whole no, these this is a

9:12region. So this is a, I need to go in and

9:15be specific about

9:16what exactly this is. This is a region. Yes

9:20. So Germany, province Bavaria is a province

9:24in this

9:24particular concept. So state slash province

9:27. So what I'm going to need to ask chat GPT,

9:30and I need

9:30to kind of be specific here, because I

9:32think it really does want you to be this

9:34specific is I need

9:35to ask it in Bavaria for the central for

9:38the specific date and time. What was the

9:42total sales?

9:42Okay, so this is going to be a little bit

9:44hard. But let's, let's, let's do one thing,

9:47actually,

9:47let's go in here. And if I, if I just

9:51duplicate this as a cross tab, it will give

9:56me this field,

9:58and I can just go out here. And I can, can

10:00I copy this, I can edit the alias, I cannot

10:03spell. So I

10:03want to grab this and just say, Okay, cool.

10:06So go back to the view. And then I say,

10:08Okay, what

10:10was the total sales in central region?

10:16Paste that Bavaria in Germany, between the

10:23dates. Let's go ahead. This is a little bit

10:27of a tricky

10:28one. So I hope I get this right between the

10:32dates, this one. And let's get this out.

10:38This is boring

10:39to watch, I'm sure. But hopefully, you're

10:42still with me here. And that now I'm

10:45missing anything

10:46total sales for german, Bavaria, Germany, I

10:48've got no other open, they don't want to

10:50click my face.

10:51I don't want anything else. Everything is

10:54good. Okay, we can just go ahead here and

10:57say, Okay,

10:57what are the total sales in central region

10:59Bavaria in Germany between these two dates?

11:01Okay. Here we

11:03go. moment of truth. What's it gonna do?

11:07Given it two dates have given it all the

11:10other contexts.

11:11That is like an interaction. That's why I

11:13think this is a more complex question.

11:15There's a lot

11:16to get right in there. And here we go. Now

11:19I see here, this is interesting total sales

11:23in central

11:24region specifically in Bavaria, Germany,

11:27between those two dates was 24,000 euros.

11:30Now, that

11:31doesn't look right. Because did I did I

11:34give it the right data? Oh, no, it's it's

11:37got it right.

11:3824,651. Geez, this. Sorry, if anyone's

11:43religious, I apologize. I'll try and keep

11:46it clean here. That

11:47is that is nuts. Wow. Okay, that is that

11:52just blew my mind away. I cannot believe it

11:56got that right.

11:58Let's look at the working. I mean, I don't

11:59know Python. So I don't know what I'm going

12:01to be able

12:02to do here. But this is absolutely nuts. So

12:04it's gone and got the data. And it's

12:06written what looks

12:08like fairly simple Python here, it's gone.

12:10And basically, it's like it's written SQL

12:12saying,

12:13hey, this is the information, go get this

12:15and figure it out. And by the way, it's

12:17been doing

12:18this all along. So if you're not familiar

12:20with Python, it's basically been sort of

12:23building this

12:23up. So you could also use this as a really

12:25nice way to sort of go backwards and figure

12:27things out.

12:27What is alarming is that, and I think this

12:31is why companies like Tableau are really,

12:34you know,

12:34really worried about this technology is

12:37that the threat isn't this this, the threat

12:40isn't that it

12:40can do this, this is not the threat. The

12:43threat is that the interface for how we do

12:45data analysis

12:46might be changing right in front of our

12:49eyes. Everything I've done has not been

12:51drag and drop

12:52here in Tableau. Just imagine just to build

12:54out that chart to build everything here

12:57would have

12:57taken out more time than typing the

12:59questions. And I'm pretty good at Tableau,

13:01I can confidently say

13:02that it would take me less time to type it

13:04than it would to build what's in front of

13:06me, even if I was

13:08racing and trying to do everything really

13:10as fast as I can. The other thing is that

13:14this little box

13:15here, anyone can use anyone can type into

13:18that it doesn't just take me a data analyst

13:20, it takes

13:21anyone who has a basic skill set of asking

13:23good questions that is an important skill

13:25can use this search box. So in my mind, the

13:28innovation behind open AI, all these AI

13:30models

13:32is not the models, it's actually the

13:34interface that allows anyone to use them

13:37well, that is actually

13:38really hard thing to do in open AI, I've

13:40done a lot of work to train these models.

13:42So this to me

13:43is the innovation Tableau, everyone, Power

13:45BI, all these products, they're racing

13:47towards AI,

13:48you might think they're racing there

13:49because you know, this is the next big

13:50thing. No, they're

13:51racing there because I think they all see

13:54this little box as the biggest threat to

13:56their product.

13:57If they can't get their product into this

13:59box in the next five years, they might as

14:02well give up

14:03right now because someone out there is

14:05doing exactly that. And it's actually alarm

14:07ingly good

14:08at being specific about certain things. Now

14:11, this is where tools like our data kind of

14:14went down,

14:14but they were doing something which wasn't

14:16as good as some of these models. And the

14:17key thing

14:18to remember here is that open AI have had a

14:20massive investment in technology to get to

14:23the

14:23point where they could build the model that

14:25is what we're using here. They've had lots

14:27of money

14:27pumped into them by Microsoft and lots of

14:29other investors to get to where they are

14:31today, to be

14:32able to then ship this model out to

14:34companies, the actual model, the final

14:36model they build

14:37is very easy and portable for them to share

14:39with companies and deploy. The hard thing

14:41is training

14:42it and that takes time, people resources,

14:44and a lot of hardware, but pretty much only

14:46Nvidia makes.

14:47So this is pretty crazy. I just cannot

14:50believe this. So we've seen it answer

14:52questions. Let's see

14:55what it can do with charts. Let's go ahead.

14:57I want to go to a different dashboard just

14:59because I'm

15:00interested in this. Okay, so let's ask it

15:05for a sales by product category, broken

15:09down by year

15:10and month. Okay, so let's do that. Let's go

15:14back to this and say, hey, we don't like

15:17tables,

15:17but we'll ask for one. Hey, can you build

15:23me a table showing sales by product

15:27category?

15:31Category with years as a subheading and

15:40months as columns along the top. So I've

15:46put some

15:47information here about how I want to see

15:49the data and let's go ahead and see what it

15:52does. So it's

15:53going to have to do the processing first,

15:55but then it's going to have to visualize it

15:56. So because I'm

15:57asking for a table, it could print the

15:59table to be fair as text. It maybe doesn't

16:01have to,

16:02but here we go. Let's have a look category

16:04year. Oh, this is interesting. Okay.

16:08Oh, my word. Oh God, it's doing it.

16:14So then the next question is, is this

16:19number correct? 6751 for furniture 2020 Jan

16:25. Let's go

16:26ahead 6751 6752. Oh, I'll give it that's

16:32just a roundup. That is that is remarkable.

16:37That is,

16:37I can't believe it got that right. I know

16:40it sounds so simple. You're probably

16:43thinking,

16:44Tim, this isn't that sensational, like, no,

16:46but it is because all I did was type it in,

16:51and it nailed it, it nailed it. And I'm

16:53comparing against a finished solution. I'm

16:56literally drawing

16:57the gap between you don't miss yourself and

17:00typing the question that is that is

17:02ultimately what is

17:03groundbreaking here. And so far, it's got

17:05everything right. There's not been a single

17:08mistake in this entire pipeline. This is

17:11pretty crazy. Now, it did what I thought it

17:13would do,

17:14which is it didn't actually do any sort of

17:16visualization. You can see here, it's just

17:18literally just done a pivot table. And it's

17:20it's actually printed the result as I

17:21thought it would

17:22here. And then it's just basically gone and

17:25styled it in HTML, which is even nicer.

17:27Okay. And it says

17:28the values in the euros, we know that's

17:30wrong. I could have corrected that earlier,

17:31but we didn't

17:32bother. Okay, it's passed that test. Nice.

17:35Okay. We'll ask for this sales and profit

17:38by product

17:38names. Okay, this is a this is a this is a

17:40difficult one. So let's just grab this. I

17:43'll try

17:44and give it less information this time. So

17:47can it show us sales and profit by product

17:50names?

17:50Oh, this is a this is a really, this is

17:52really difficult. This is all about the art

17:55of typing

17:56the good prompt. So wow, what I'm what I'm

17:59going to do this, what I'm going to do is I

18:02'm going to

18:02put tabular on another screen. And I'm

18:05going to put chat GPT here. And what we're

18:07going to do is

18:08I'm going to type the prompt in front of

18:10you. So you can see what I'm typing. So I

18:12'll say, can you

18:14build a scatterplot? I want it to show

18:19sales and profit by product names with

18:27category

18:29and subcategory breakdowns in rows. Nested.

18:39And then what is on the top? What is that?

18:45What is

18:46that official terminology? Let's just go. I

18:48think this is customer segment. Yes,

18:51correct. So we want

18:52segment and then segment has column

18:59sections. Okay. So this is a slightly more

19:05ambiguous thing,

19:07because I've asked you for a scatterplot,

19:09but I'm talking about rows and things being

19:12nested,

19:12and then the segment is column sections.

19:15Let's go ahead and hit enter. Let's see.

19:17This is this

19:18is tough because my prompt could have been

19:20bad. I don't think it's that bad. Probably

19:23has typos,

19:24but let's let's wait and see. I'm not going

19:25to do the work. I'm not going to show the

19:27working. I

19:27always look like to look at the working

19:30afterwards. Oh, it stopped. So what is

19:33going on here? I think

19:34what it's doing is it's potentially firing

19:36up another thing. Oh, wow. Okay. Yeah. It

19:40got busy.

19:40Okay, so here we go. So what did it do? It

19:45imported matplotlib by pyplot. So it's

19:48imported

19:49a library and it's imported seaborne. And

19:52then it's commented it out. So it has map

19:55scatterplot,

19:56create facets. Seaborne seems to be able to

20:00do some of this segmentation. So hold on.

20:05Okay. And it's created it and then it's

20:08basically kind of pulled the charts and

20:11that's what we've

20:12got below here. But did it do it correctly?

20:16So we've got corporate consumer home office

20:20.

20:20Yeah. Okay. Now, what it didn't do nicely

20:27is this this thing here. So what it did is

20:31it kind of said

20:32each scatterplot is going to be a subset of

20:35the total. So it didn't put it didn't put

20:38them along

20:38the left. That is something that kind of is

20:40confusing. And I thought this would be

20:42confusing,

20:43but it's not done a bad job of giving me

20:45the alternative. And I can still see the

20:47outliers.

20:47So I can see the outliers clearly here in

20:50many ways, it's maybe made it easier for me

20:52to do the

20:53analysis because in Tableau, these would be

20:55in one line. And just to show you what I'm

20:57comparing

20:58against, I'm comparing it against the

21:00bottom bottom chart here. And that is a

21:02pretty interesting

21:03sort of take on that isn't exactly what I

21:05wanted, because I was starting with

21:08something that was

21:09already, you know, articulated. The trick

21:11here is going to be prompt. And I think if

21:14I did a better

21:14prompt, it maybe would get closer to the

21:16answer. Also, I'm not sure if the thing I'm

21:19asking for

21:20specifically possible, you know, having a

21:22table like structure on one column, on one

21:24sort of axis

21:25and then like a section set up on the other

21:27and it got some of that, but it's not

21:29perfect.

21:30And it goes and explains what it's done.

21:32And again, you've always got the ability to

21:35show

21:35the working. So that that is that is truly

21:37remarkable. I don't know, I don't know what

21:40you're using this for. If you're using this

21:41, let me know what you think. There is a

21:45couple

21:45of issues with it, it still can't do all

21:48the all the things you would expect. So if

21:51I go ahead and

21:51ask it, hey, can you plot a map with each

21:58data point sized by cells at the level of

22:04cities in the

22:06data set. So I've sort of asked it in a

22:08really bad way, there's even a typo in

22:10there. Sorry for the

22:12inconvenience, but currently in this

22:14environment, I'm unable to access internet

22:16services, which

22:16includes mapping services such as Google

22:18Maps or any other geolocation service, it

22:21could be

22:21used therefore I'm unable to create a map

22:23plot for your data. Fine. However, I can

22:25guide you to this

22:26using Python libraries, such as Geopandas

22:28and folium. So it's basically saying, hey,

22:31you can

22:31do this in your own environment. And by the

22:33way, this is the code to go and get a

22:35little bit of

22:36geocoding that it does, you see it takes

22:38the city column, and it uses geolocate

22:40using the latitude

22:41and longitude to basically get the exact

22:43location. And then it's able to then import

22:46folium, which

22:46takes that information. And for each row in

22:49the data set, is actually able to pass that

22:51and then

22:52create the map. So this telling you how it

22:54would work, if it had access to that. But

22:56the other

22:56crazy thing you've got to remember is that

22:59chat GPT is firewalled from the internet,

23:01everything

23:01you get back, it's not able to search the

23:04internet, it can only use its large

23:06language model or model

23:08that's built into it for everything that it

23:10does. And in more recent features, they've

23:13had this

23:13ability to browse the internet with Bing,

23:15but they actually have to disable that

23:16because it was doing

23:17things they didn't like. But in Bing, you

23:19can actually get it to go ahead and still

23:21do that. So

23:22in here in chat GPT, it's completely sort

23:24of isolated still. And that's how it's

23:26working.

23:27Anyway, what do you think? I think the

23:29biggest threat to pretty much everything is

23:32this search

23:33box, this search box, what is incredible is

23:36this tool, if I didn't expose the working

23:39every single

23:39time, what I'd be seeing is just answers.

23:42Okay. And you might be wondering, well,

23:45what is the what

23:46is the confidence level of some of this

23:47work? Well, I walked away with pretty high

23:49confidence,

23:50the things that can do it told me and

23:52things that didn't quite get right were

23:54based on the

23:55prompts. And that's another sort of

23:58interesting angle. But yeah, and if I'm

24:00Tableau, I can

24:01absolutely understand why they're racing to

24:03build something like Tableau GPT. Because

24:05again,

24:05if in five years your tab your product

24:08doesn't fit in this text box. I'm certain

24:12something will

24:12come in at your lunch. Anyway, thanks for

24:15watching. I'll see you in the next one.

24:17Transcribed by https://otter.ai

24:26[ Silence ]