Pairplot – Seaborn




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We continue to build on our knowledge and look at the pairplot. I talk about how and when to use this plot, show regression functionality and talk about further expansions to this design.

Associated Github Link:
https://github.com/knathanieltucker/seaborn-weird-parts/commit/ba0bc43b4e6302a7ae0570ceeb0ea2ed0c4fd556

Associated Seaborn Documentation:
http://seaborn.pydata.org/generated/seaborn.pairplot.html#seaborn.pairplot
http://seaborn.pydata.org/tutorial/distributions.html

Source


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Comment List

  • Data Talks
    November 13, 2020

    Things I learend from this lecture:
    Pairplot
    *When : For looking at correlations across multiple dimensions
    *Categorize it by color : (hue=" ")
    *(kind="reg") : regression

  • Data Talks
    November 13, 2020

    Thanks for all this videos, but I think that the weird parts is about the interpretation of the plots !!

  • Data Talks
    November 13, 2020

    very helpful

  • Data Talks
    November 13, 2020

    Hey Nataniel your videos were very helpful to me. I like that you go straight to the point. I was able to create all the plots I needed. Thanks for sharing your knowledge.

  • Data Talks
    November 13, 2020

    Hello
    NICE video I just wanted to know when you are plotting “kind = reg” we also get confident intervals – but clearly there is heteroskedasticity present so OLS method is no longer BLUE (best linear unbiased estimator) so can we use confident intervals – are they valid?
    Thank you

  • Data Talks
    November 13, 2020

    how do seaborn pairplot know not to take species into plotting.?

  • Data Talks
    November 13, 2020

    yeah got it, you fucking have no idea what it is )

  • Data Talks
    November 13, 2020

    Thanks for the tutorial. I was wondering if pairplots can be combined with Plotly to create interactive plots. The 'hue' feature in pairplot is great, and it would be awesome if we could just click on the legend to make the data active and inactive with Plotly.

  • Data Talks
    November 13, 2020

    Thank you for your tutorial. Seaborn has been very helpful.

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