Effective PyCharm Transcripts
Chapter: Data science tools
Lecture: Introduction to the data science tools

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0:02 When you think about data science,
0:04 it might feel like Jupyter notebooks have a lock on how data scientists work with code
0:09 in python and libraries and visualizations and all that sort of stuff.
0:14 As you'll see in this chapter,
0:15 PyCharm has a lot to offer and that's not by asking you to move out
0:20 of notebooks but in fact it has a really great support for running notebooks and even
0:25 debugging and stepping through cells of a notebook.
0:28 So if you've ever wondered what is wrong with this code,
0:31 it seems right, why is it not working well if you run it in PyCharm
0:34 you can actually set a breakpoint and step through and inspect the variables and
0:38 see what's going on. You can also treat these notebooks as text files that have
0:43 the cells just separated by comments basically in your text editor without actually rewriting them.
0:50 Not saying well I'm going to convert this to a py file but I'm going to
0:52 treat it like a file in a really nice editor but it stays as a notebook
0:56 And finally you also see that PyCharm has a scientific view.
1:01 So if it notices that you're working with things like Numpy and Pandas,
1:05 it will suggest that it swaps the way the windows are looking at the various windows
1:09 that are shown. So you can see things like as you run your code,
1:13 what are the values of the variables and as you interact with the various data science
1:18 methods, what is the documentation around what you can do with that method.
1:22 So if you're into data science you're gonna have a lot of fun with this chapter