Effective PyCharm (2021 edition) Transcripts
Chapter: Data science tools
Lecture: Introduction to the data science tools
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When you think about data science,
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it might feel like Jupyter notebooks have a lock on how data scientists work with code
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in python and libraries and visualizations and all that sort of stuff.
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As you'll see in this chapter,
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PyCharm has a lot to offer and that's not by asking you to move out
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of notebooks but in fact it has a really great support for running notebooks and even
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debugging and stepping through cells of a notebook.
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So if you've ever wondered what is wrong with this code,
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it seems right, why is it not working well if you run it in PyCharm
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you can actually set a breakpoint and step through and inspect the variables and
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see what's going on. You can also treat these notebooks as text files that have
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the cells just separated by comments basically in your text editor without actually rewriting them.
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Not saying well I'm going to convert this to a py file but I'm going to
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treat it like a file in a really nice editor but it stays as a notebook
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And finally you also see that PyCharm has a scientific view.
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So if it notices that you're working with things like Numpy and Pandas,
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it will suggest that it swaps the way the windows are looking at the various windows
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that are shown. So you can see things like as you run your code,
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what are the values of the variables and as you interact with the various data science
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methods, what is the documentation around what you can do with that method.
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So if you're into data science you're gonna have a lot of fun with this chapter