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Thanks! Glad you liked it! 😁

You are most welcome - glad it helped!

So glad to hear you found my videos valuable! Wishing you all the best on your wind data project 😀

Haha - glad you liked the video. The table is supposed to be “distressed.” But it looked even worse after my duct tape modeling 😆

You’re very welcome! Thanks for the support - cheers! 😀

Most welcome - cheers! 😀

I do love a good talking head segment! 😆

Of course! Glad you liked it 😁

Most welcome! So happy you’re enjoying my content 😀

Excellent! Glad you found it useful 😀

Really appreciate the support - thanks so much! 😀

Awesome! Welcome aboard! 👍

Thanks so much for supporting my channel! Wishing you all the best 😁

Oh yeah! 👍 This is honestly my go to but if it doesn’t work, then I switch my approach.

Thanks so much for your purchase, and I am beyond thrilled to hear my channel has helped with your data visualization skills. Wishing you all the best! 👍

Thanks! I'm not sure exactly what you're seeing. I don't remember having problems with this in the past. So my only guess that I might try is saving as a SVG file instead of a PDF and converting it later.

Thanks so much! 😊

Wow - it sure is. I attempted to update it, but that doesn't appear to be working. Hopefully will get fixed soon!

Thanks for the feedback! I’ll keep that one in mind for future videos 👍

Awww - thanks so much! So glad to hear you find my content helpful 😀

Right on! Happy you liked it!

Great question - the output looks very similar but the input can be quite different. For the histplot, you'll have a column of numbers. Those can be integers or floating point numbers with a decimal. Seaborn will automatically make buckets for those numbers and count up how many numbers it sees within the range of each bucket (e.g. how many numbers fall somewhere within 3 to 5?) With the countplot, however, you'll often have a column full of categorical items, such as a column full of text values like dog, cat, and bird. Seaborn will tally up how many of each item it sees in that column (e.g. how many times does the word "dog" appear in the "pets" column?). Things can get fancier from there, but that's the rough gist. 👍

@@KimberlyFessel Thanks a Lot😊

The move_legend function may be useful for this: seaborn.pydata.org/generated/seaborn.move_legend.html

Thanks! Glad you enjoyed it 😃

Thanks for the update. Yes, the distplot has been replaced by the displot. You may find that one more useful. Cheers!

Yes, I believe you can still make a bivariatr KDEplot but you need to use new syntax. Try sns.kdeplot(data=df, x=‘col1’, y=‘col2’) where df is your pandas dataframe and col1 and col2 are names of your dataframe columns. 👍

So it goes for videos about coding libraries 😔- concepts should still be similar but the syntax definitely updates from time to time. Maybe I’ll make a new one… 🤔

Excellent - glad to hear it was helpful!

Thanks! Glad you liked it 😁

Most welcome! Hope it helped 😁

Hello 👋🏼 and thanks for stopping by. So glad to hear you enjoyed the video!

Great suggestion! Perhaps I can make some videos about how to diagnose the information seen in the plots 🤔

Okay! 👍

Hope the video helped you then! They are definitely tough to remember 🤔

So happy to hear that! Any suggestions for other video topics?

Most welcome! 😃

Hi, this course features all asynchronous materials so you can access them any time you’d like - no time zone issues! Yes, the videos are pre-recorded, self-paced materials.

So glad that helped you - cheers! 😀

Yes - that's correct. The total number of apples we have (ON THE APPLE SIDE = 3). I've totally forgotten about the orange-side of the model myself! 😆

Hi there - my PhD is in Applied Mathematics. I created models for the biomechanics of the inner ear. 😀 You can find me on LinkedIn here: www.linkedin.com/in/kimberlyfessel/ 👋

@@KimberlyFessel woah.. I just completed my masters in applied mathematics. Nice to see someone in my branch too. Well I am thinking to pursue PhD in bio informatics or bio-intermediary disciplines.

Awesome - my postdoc was in math bio, too. Very interesting stuff! Congrats on the Master's 👍

@@KimberlyFessel ty mam.

Will be joining for PhD in weather forecasting.😊

Most welcome - glad you enjoyed it! ☺

So glad that helped you! I love a good mnemonic. 👍

Yes! Chunksize was so close to making the cut of my favorite arguments to put into this video. (It's even written on the outline I made for the video but then crossed out 😆) I went with nrows instead to limit down the number of rows, but chunksize is great for having little pieces of your dataframe to work with and then throw away before they clog up your memory. This shows a little demo (pandas.pydata.org/pandas-docs/stable/user_guide/io.html#iterating-through-files-chunk-by-chunk), but of course, you would want to perform some actual calculations as opposed to just printing the chunks! 👍

Sure, I don't see why not. You would just want to supply an equation for the pyramid shape instead of the cubic shape I have here in the definition of function_z()... may need a piecewise function to make the edges nice and crisp.

Awesome! 👍

Oh so glad it helped you - cheers!

Thank you! You are most welcome and thanks for stopping by ☺