Effective PyCharm Transcripts
Chapter: Data science tools
Lecture: Concepts: Exploring data in notebooks
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0:01
So we've seen that we can explore data in Jupyter netbooks right inside PyCharm as well,
0:06
so create a new Jupyter notebook and you can just start typing, here we have just a standard Python variable being declared,
0:13
if you want you can pip install Jupyter within your environment preemptively or as we saw, when you try to run it,
0:21
PyCharm will open up a dialogue and suggest to fix it for us. So if we try to run it, even if Jupyter is installed it probably is not running at first,
0:30
so it'll say couldn't connect, do you want to run it, you click yes and it will pop up that dialogue
0:35
you either just press run, or you have to say fix it by installing Jupyter and then run, that's cool, so just click right here.
0:42
Once it is running, this url you can actually use in your web browser to get back to the Jupyter notebook and interact with it,
0:50
but if you want to just run it within PyCharm, you actually don't have to use the url, just minimize this window and hit run again
0:59
so the little play button right in the middle and now it'll just discover that that server is running inside itself
1:06
and connect back to it, and that's pretty cool. So here we have a message, some imports basically the same code that we wrote,
1:13
this is just a Jupyter notebook right here. And right now we have the plot showing over on the right
1:18
and if you want to have it inline, remember use that percent in Matplotlib inline.
1:25
So we write our code, and then the stuff shows over in the data science view as well.
1:30
We can even do other things, if you're familiar with Jupyter notebooks you know this we can do for example markdown right here,
1:37
why, because you can, right, Jupyter notebooks are not just code, but they are sort of reports around exploring code and data
1:46
which is why people love them.