Name: Full Dash App - Data Visualization GUIs with Dash and Python p.12
Uploaded: 2020-03-31T18:18:15.000Z
Duration: 15 min 37 s
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And welcome to another data visualization with python and dash tutorial.

Future continuous

In this tutorial, What we're gonna be doing is kind of bring everything up to at least the point where I'm at right now with the APP.

There's a lot of little things that have been thrown in here and changed and added, and all that.

Ah, and I just kind of go over them pretty quickly.

There's really no super new concepts here, except for maybe the way, though, that we're doing the database, which I may get into the But that's a totally separate tutorial.

It's It's mostly just for text search, specifically with sq light on getting that to be very fast.

Um, so I probably won't do that necessarily for this one, But at least tell you what we've done.

So, anyways, this is the APP live Twitter sentiment.

You type in the search, and let's say you wanted to search for car.

There's car now, part of the reason why we wait a while.

But if I'd highlight Corrine, just delete car really quickly.

And the problem with with the search as it is right now is if we type car, each letter gets searched in the database.

So if I typed it slower, you would see Oh, it searches for C and then see a and then our It's just if I type it quickly enough, it's not going to you're just not going to see it all update.

But anyways, so that's one thing I'd like to improve otherwise.

Ah, what we have is just live updated sentiment over here.

Just basically, it's every second I think.

Ah, and then over here, this is just longer term sentiment.

So as they come in, it gets updated here.

Ah, and then also, I show basically the date to the tweet and then the sentiment.

I also color code the sentiment with a slight kind of background, um tint either greener red.

Then we also attract positive and negative sentiment.

And then I realized you can click on these and make it appear disappear.

It's kind of annoying, though, because each time the graph load that it goes back to wherever the original state was, and I felt like this was a little more useful than also showing the neutral.

But all the code is open sourced, which I have.

The report right here is your Centex slash social sentiment.

So get up dot com slash and slash social sentiment.

I will put a link in the description, though, so if you need it, uh, you can head there rather than typing innings.

Everything is pretty much explained here.

So I guess I don't really think there's anything else I really want to go over.

Oh, well, I guess we have, like, related terms and stuff like that.

But that's also so like depending on what you searching to hear.

Like research car, we can see the related terms to car, and that's just any.

Basically, it's any now NHS that are contained in tweets with car, and then we count, and then the top ones get shown.

So for especially for a lot of things like, for example, with car career gets chosen.

Well, car is a it's a sub word inside of career.

Whereas if we do maybe nothing, we get maybe a little more useful information.

And then if we were to search, maybe maybe Trump.

Hopefully we'd get some more related terms to Trump than just the fact that because Trump's probably not a sub word to so many things of sessions, obviously, Donald Trump President um, I'm not sure what Connor would be in reference to Rex Tillerson just stepped down Russia because, of course, the GOP, um, not sure about new and wood, and I'm not sure cut cool Okelo.

Uh, anyway and then, obviously, just recent trending, um, messy.

Um, Stephen Hawking unfortunately passed today, So that's trending, um, and yet another thing.

So let's run through the code at this point.

So what we have here is the contents in here.

Basically, the main application is dash mess.

If you wanted to run it locally, you can use the Deb server.

Um, just so you could run it on, you know, on local host port 8 80 50 Ah, the Twitter stream.

This is what you're going to use to actually stream tweets from Twitter.

You've got the fig, which right now is just stop words.

So it has to do with these, uh, these related terms, for the most part.

So there's a lot of words that we, you know, they're just totally used useless.

So we actually want to just ignore them if they did happen to come up.

But we'll look at it in a moment in yet cash this just for cashing, especially and on the web app itself just to hopefully get things to run quicker.

If so, we're not just performing the same searches and stuff.

Just truncated, infinitely growing database.

So, um, I just cleared it actually made a mistake when I was building the truncate script, and I was either gonna have to leave probably 10 million records that I'd never be able to delete or just start over fresh.

I chose to start over fresh, but hopefully now the truncate script works s So anyway, we lost.

I think we had, like, 35 million records or something, and I just I didn't want to leave a bunch of records that could never get rid of, so I just started over, But it should be about 3.5 million records a day.

If you're faster processing, you should be able to get more than four.

Ah, and if you're slower at processing, you might get less.

Every day in that database grows very quick.

T s with the sq light, which is great for text search, but more on that in a moment.

So I think probably first thing will open up here is just dash messed up I to kind of run through the code here.

Um, I'm trying to think if there's anything super major, so a lot of stuff here, it's just really pretty, pretty badly organized.

So you, if anybody wants to contribute, that's probably one really easy way to do.

But as you can see, we've got ah, you know, basically the layout of the application.

I'm using the row road dibs, basically from material I C s s.

So that's kind of how we're maintaining the, um, the interact ability, I guess of the of the applications.

Like, for example, if I squished this up, everything's kind of interactive, and eventually it'll pop down, and then we just have one graft and one thing per and the tables actually interactive as well.

Um, so using materialize CSS were able t you actually have that interactive interact ability, I guess.

Um and then I'm also importing Google Analytics.

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Full Dash App - Data Visualization GUIs with Dash and Python p.12

stuff

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material

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