Mastering PyCharm Transcripts
Chapter: Data science tools
Lecture: Concepts: Exploring data in notebooks
0:01 So we've seen that we can explore data in Jupyter netbooks
0:03 right inside PyCharm as well,
0:05 so create a new Jupyter notebook and you can just start typing,
0:08 here we have just a standard Python variable being declared,
0:12 if you want you can pip install Jupyter within your environment preemptively
0:18 or as we saw, when you try to run it,
0:20 PyCharm will open up a dialogue and suggest to fix it for us.
0:23 So if we try to run it, even if Jupyter is installed it probably is not running at first,
0:29 so it'll say couldn't connect, do you want to run it,
0:32 you click yes and it will pop up that dialogue
0:34 you either just press run, or you have to say fix it by installing Jupyter
0:38 and then run, that's cool, so just click right here.
0:41 Once it is running, this url you can actually use in your web browser
0:46 to get back to the Jupyter notebook and interact with it,
0:49 but if you want to just run it within PyCharm,
0:53 you actually don't have to use the url, just minimize this window and hit run again
0:58 so the little play button right in the middle
1:02 and now it'll just discover that that server is running inside itself
1:05 and connect back to it, and that's pretty cool.
1:07 So here we have a message, some imports
1:10 basically the same code that we wrote,
1:12 this is just a Jupyter notebook right here.
1:14 And right now we have the plot showing over on the right
1:17 and if you want to have it inline, remember use that percent in Matplotlib inline.
1:24 So we write our code, and then the stuff shows over in the data science view as well.
1:29 We can even do other things, if you're familiar with Jupyter notebooks you know this
1:33 we can do for example markdown right here,
1:36 why, because you can, right,
1:39 Jupyter notebooks are not just code,
1:42 but they are sort of reports around exploring code and data
1:45 which is why people love them.