In this video, we'll see how you can use nan to represent missing or invalid values.
0:00 - intro / setup
0:23 - identifying nan in an array / np.isnan()
1:00 - nan is a floating point!
--- Code ------------------------
https://www.practiceprobs.com/problemsets/python-numpy/intermediate/#nan
--- Vids & Playlists ----------------------------------
Google Colab - https://www.youtube.com/watch?v=SUCRr56Jzkw&t
NumPy - https://www.youtube.com/playlist?list=PL9oKUrtC4VP6gDp1Vq3BzfViO0TWgR0vR
Pandas - https://www.youtube.com/playlist?list=PL9oKUrtC4VP7ry0um1QOUUfJBXKnkf-dA
Neural Networks - https://youtube.com/playlist?list=PL9oKUrtC4VP5N3VtTTjhTfiHoFXmnrgPW
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--- Support ------------------------
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