Name: Python, Matplotlib, Pandasによるデータ分析・可視化入門｜Matplotlibチュートリアル (Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial)
Uploaded: 2021-01-14T10:22:42.000Z
Duration: 22 min 1 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

法助動詞 B1

Hey, everyone, This is my introduction to that announces slash data visualization with python in this video, I'm gonna cover why you might want to use data visualization on why you might want to use Python Amapola late for it.

様態の副詞

And then we're gonna go over some simple examples of how to actually use these tools and then using these tools were gonna do sort of, ah, riel analysis with a real data set at the end.

程度の副詞

In this video, I'm on lee gonna cover line charts just to keep everything simple.

I'm also gonna put a more detailed version of this outline in the comment section below so you don't have to watch the whole thing if you don't want to.

Okay, so why should you use data visualization in the first place?

Well, individualization is actually often the first step off.

Any type of that analysis work, whether it's simple, that analysis or so disco announces or machine learning analyses.

And the reason for that is because visualising data often gives you an intuitive understanding off the data on it often helps you see patterns that are otherwise hard to see, and we're going to see an example of that later, okay?

Well, Python is not the only good choice, but I would say it's one of the best on the reason is.

First of all, it's a general purpose language that's pretty easy to use and learn on.

It also has many libraries for scientific computing on their science, including Marple, Lib.

And if you work at a company, your company might already use python floor or something else on.

If that's the case, that's really nice, because then you and your team are not gonna have to learn a totally new language to do something that announces, And why are we using up will live for this?

Live is not the only good visualization library for Python, but it's still one of the most popular choices on there are.

Actually, other libraries thought are based on a couple lib, so if you learn couple lib, it's gonna help you learn these other libraries.

For example, this one, called Seaborn later on if you want to on Mapo Live is also pretty easy to get started with.

Anyway, Let's dive into a demo for this demo.

We're gonna use something called Jupiter notebook on a few other parts on libraries and we're gonna use Anaconda to install them.

If enough on the air with Debra Notebook on Anaconda.

I have an explanation about them in my python tutorial video.

So I'm gonna leave a link to that and the description.

Just search for Anaconda Python or directly go to Anaconda dork and there find the bundle says download Anaconda and select one of our OS you're using.

I'm using Mac here and click download under Python three point something version instead of python to point something because we're gonna use python three here and select where you want to download this package.

Once it's download it, open up the package that you just download it and then just click.

Continue, Continue, continue, Continue, agree.

And so for me only or installed on the specific disk, it doesn't matter which one and continue on quick install and this process is gonna take a while.

After some waiting, you might see this prompt to install Microsoft V s code.

So let's just continue here and then close and then to launch.

You can do it through this thing called Anaconda Navigator, so just launch it like you launch any other application.

Just dismiss whatever comes up and then click launch in the Jupiter notebook section, and then you should see a browser windows show up with the Jupiter notebook interface.

Now, if you want to follow this tutorial, the first thing you should do issue should create a new folder.

Let's call this one Data Federal addition.

We're gonna put all our data and dip it in a notebook file here.

So let's first download our data to do that.

Just go to see sojo dot io slash data and download these two files Simple data CSP on countries that CSP and then put these CSB files in the folder that uses created the individualization.

After that, go back to the Jupiter notebook interface on and you can just navigate to this stop and then the folder that we just created their visual addition and to create a new jibber notebook filed here.

Just find the new button on the right on Click Python three.

Right now, this notebook file has entitled as the title.

So let's change it to data, visualization ways, Python click rename and you have a notebook called Data Visualization.

With Python, you can take it just by going to stop and then to the folder that you just created.

I used to see that there's a file called Data Visual Addition with Python Thought I Pay and Beat.

And it's really important that this notebook file is in the same folder as wth e data that you just don't let it.

Countries, that's yes, V on the other one.

And once everything is set up just right in the first cell import pond us as p D.

This means we want to import a mojo called Panoz as P.

Or we want to give it sort of a nickname.

You can run the sell by click in this one, and now Pandas is imported as PT and here we're going to use pund us for importing on using some data from our CSB files, and we need to import another motel here.

So for that just right from Muppet Lib import Piper as p a T.

Olive package import pipa module and then call it P L T.

We're gonna use pipe blocked from Apple Lib for making our charts.

So here, let's first take a look at a really simple example off how to use pipe lot.

So here I'm gonna write X equals 123 It's a list of three elements on why Eco's 14 and nine on To plug this set of data, you can just write p o Tita plots X Come away on this plots X on the X axis and why on the y axis.

And then you can show this graph by writing p lt dot show When you run this cell used to see a graph like this.

You see that the values of X R, 12 and three as expected on the bodies off.

Why are 14 and nine If you want to add up title to this graph, you can do so by writing p o.

That title tests plots right after the plot statement before the still statement, and then you can add an ex label on the white table as well.

By writing p l t dot x label that's called the X label X on p o t dot Why label Let's call the White Label.

When you run this cell, you see that there's a title called Test Plots and X Label called X on White Label called Why?

Okay, What if you wanted to plot multiple lines here?

Well, to do that, let's create another list.

This one is gonna have 10 5 and they're inside on two plots.

Ex Andy on top off X and Y you can just write P lt the plots X comma Z right after p a t the plot X comma y And then let's fix the while They were here, too.

And when you run this cell, you should see these two lines So the bull line represents X and Y on the Orange line represents X and Z.

The plot X and Z flooded X on the X axis Andi on the y axis.

But right now it's kind of hard to tell which line represents which data so we can fix it by adding a legend statement.

Let's add that after the wide level statement by writing p o t dot legend parentheses, Square brackets, double quotes This is why comma double quotes.

This is Z So not here that this legend function takes a list as an argument.

And when you run this so you should see this legend that says the blue line is this is why on the Orange line is thesis de Okay, that's the basics off bloody.

Now let's see how to load it up from a C S V file.

For that, you can just write sample on the score data Eco's PG or pandas that read C s V.

By the way, I just pressed tab here to do auto complete and then parentheses sample on the score data that CSP.

Now, before you run this cell, make sure that the notebook file that a visualization with python that type A and B is in the same folder as simple there.

That CSP when you run this So this that a simple data that C.

S V is loaded by the pond US module, which we call P d.

And then it's assigned to this fireball called simple data.

You can check what's inside this viable sample on the score data just by writing sample on the score data in this new cell.

And then when you run this cell, you should see something like this.

Three columns column A column b On column C On five rows, you see a bunch of various inside the stable if you want to check if this set of data is exactly the same, asked the original data, You can do so by opening up the original data file.

S V With Excel or any other spreadsheet application on When you open it, you should see exactly the same.

Data column a column b column C with five rows with a bunch of bodies.

Python, Matplotlib, Pandasによるデータ分析・可視化入門｜Matplotlibチュートリアル (Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial)

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Python, Matplotlib, Pandasによるデータ分析・可視化入門｜Matplotlibチュートリアル (Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial)

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