How To Fix Incomplete Data - Pandas For Machine Learning 12

[@Codemycom]

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[@vladyslav-py-js-cs]

Oh man you know that you're the best at this stuff!😁

[@Codemycom]

Ha thanks

[@collinslife7021]

Your super helpful with your videos, have learned quite a bit

[@Codemycom]

Happy to hear it!

[@FritsvanDoorn]

Hi, interesting. I noticed that the column wit the NaN had numbers with .0 while the other number not. What is the reason for this? Where does it come from? Thank you for explaining.

[@Codemycom]

It just defaults to a float. You can change it by slapping a .astype(int) to the end...like: my_df.fillna(value=99).astype(int)

[@LOSFOUFOUP]

Thresh is not very well explained here, but in case someone casually pass through here and is wondering what the heck is up with that: 'thresh=N' means that at least N non-NaNs values have to be there to survive. SO 'thresh = N' ALONE (or with axis = 0 after) checks the ROWS: every row with at least N non-NaNs survive, others disappear. HOWEVER 'thresh = N, axis = 1' checks the COLUMNS: every column with at least N non-NaNs survive, the others disappear.

[@charles_phruno]

Is this playlist enough to start with pandas in ML

[@Codemycom]

We seem to be starting with Pandas in the videos...so... yeah

[@hakimdz3358]

how python cx_oracle dump database to file??? plz

[@Codemycom]

I don't have videos on anything like that.