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So the next tool that I'm going to show to you is called patron charm.

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So here in our Anaconda Navigator, we have one panel called Pitch ARM Community, which is the free

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version, which is totally sufficient and I recommend using this one.

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Or you could also use the pouch.

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I'm professional, but I think you have to pay something for it.

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So in the current installation of Anaconda Petroleum is already installed, so you can just click launch.

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If this is not true for you, then maybe you just have to click here, install, or if the panel is

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not there at all, then you go to your browser and you Google Pay charm and the correct website to download

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it is jet brains dot com.

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So let's click on it.

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And the website is a bit slow at the moment, wondering why, but here we are.

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This pie charm?

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Maybe let me change to English so that you can see what's written here.

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Yeah, this is the python ideal for professional developers.

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Sounds really fancy, and I also think that is really a nice tool to use.

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So what you can do then, is click on download.

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And as I said, the community edition is totally fine.

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All right.

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But I think for your case, this should already be installed.

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So you just click on launch and you don't have to install anything.

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So let's launch by Charm Community.

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So here we are.

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We are in sign of pie chart, and you see we have lots of things that we can change and it looks a bit

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more difficult compared to Jupiter notebook.

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And you see here at the bottom right.

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It's doing some things.

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This is because I opened this for the first time.

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And you also see that it's currently using Python 3.9.

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And what you could do then, is you could also add another interpreter and install some other version

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of Python.

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But that's all not really necessary for our cases.

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So what we could do now is we could open a file or we could even just drag and drop a file that we want

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to run.

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So let me do this.

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So I just drag and drop the our first notebook file inside of here, and it opens the file.

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And you realize that this looks very confusing and doesn't really make sense at all.

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And the reason is that a picture can only work with native Python files.

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The dots p white files and it cannot use Jupyter notebook files.

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So we cannot open up our our first notebook file here.

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Or we would have to do that is create a new file.

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And this whole Pichon program is really very useful for writing a very complicated, the very large

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project.

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So it works in terms of projects.

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So what do you have to do is you have to click file and then new project.

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And now this looks all very difficult and I'm just going to use that is not that difficult at all.

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So for example, you write here, you specify the location, I just leave it as it is and right here,

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test projects and then the environments and all of this you can leave as it is.

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What's just important is that you have the Python three point nine here and here, and you don't just

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click Create.

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So I would say I click on New Window.

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This is because from my old installation, I'm already inside of another project, so I just click New

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Window.

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And now it runs the program to create a new environment.

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Takes a few seconds.

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Even has to download something, huh?

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I didn't know that.

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I think this is because I had already on my PC, an old version of Python, and now there is a new one

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here.

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All right.

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So it comes with a sample Python scripts where you can see it's really a bit more difficult and a bit

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more advanced compared to the Jupyter notebook thing.

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But I would say, let's delete all of this, and let's just write here once again, some very easy equation

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five plus three.

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And what I do now is I run this.

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And it worked.

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And this is what happens in the console.

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And it just gives me.

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Process finished with exit code zero, which means no error.

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Everything worked.

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So where's our answer?

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Where's our eight?

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Well, now the situation is different compared to the Python or the Jupyter notebook file and the Jupiter

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notebook file and also in the console.

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When we would write something like this, it was it would always directly give us the results.

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However, not in a native Python file, because this calculation is done only internally and it does

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not give us an output automatically.

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We have to specify that it should give us an output.

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So, for example, we have to write, print and then in brackets five plus three and then rerun it.

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And this time the output is shown here.

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For example, we could also define something like a equal to one and then two times a plus one, which

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is five.

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And then we write print

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five times a plus seven.

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So maybe let me delete this one.

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And now let's see what happens when I run this.

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Just gives us as an output.

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So let's see what happened here.

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We have a is equal to one.

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We have two times eight plus one is five.

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And then we have five times a, which is five plus seven is 12.

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And here we have written down print.

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So it only gives us this result and not this one.

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So this is really the difference between a Python file and a Jupyter notebook.

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File and pie chart is really useful if you have some very complicated project, which even contains

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many files that you load.

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For example, here you see all the files that are relevant.

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But for simple projects and short codes, the Jupyter notebook is, of course, much better.