Kimberly, these are really great videos. At higher age, I am hitting University again for DH and python, pandas and co are giving me a hard time. You make it all look so easy, and it functions with me. Thank you so much. God bless you and have a good start into 2023.
On TH-cam, in a class that I am taking online, the written documentation, everything associated with these packages are a steaming pile of 💩. Your videos and your way of presenting this material is the best thing that I have seen anywhere. There really is no comparison. You have an amazing gift in teaching this material. If you did a subscription website about data science and all these packages, I would subscribe gladly. I think a lot of people would. Excellent work. Some of the best datasci or even comsci teaching videos that I have ever seen.
Thank you for the videos! Is it possible to make a 3D plot of the bivariate data, using a histplot or KDE plot, so for example you would see "mountain" peaks for high frequencies, and "lowlands" for low frequencies?
Thank you I think that the series would be much better if it was distinguished when and why to use these different graphs and what do they mean statistically
Thank you for an interesting and well put tutorial on how to use Seaborn! I have one question regarding the Hue. I get this error when calling for a column name that has 8 different words in it.ValueError: The following variable cannot be assigned with wide-form data: `hue`. Any idea why this is happening? When I'm plotting a sns.relplot with the same data it works fine
You're welcome! The histogram is probably the most well known distribution plot, but there are a few others. The KDE plot is a way to see a smoothed-out, continuous approximation of your data's distribution function. (You can check out my KDE plot video if you want to learn more: th-cam.com/video/DCgPRaIDYXA/w-d-xo.html) Seaborn also has a rug plot, which is basically a histogram with a zero-width bar, and an ecdf plot that looks more at the cumulative distribution. I'm planning to also make a video about the Seaborn displot which combines all of these!
hey! how can I create a histogram with the horizontal axis as the data and the vertical axis as the absolut frecuency?? If anyone can help me I will be very grateful, thanks in advance!
Hi there - the seaborn histplot has an argument called "stat" that may be helpful. The default is stat='count', which just counts up the number of observations in each bin. But you also have the options of 'frequency', 'percent', 'probability', and 'density'. See the seaborn docs (seaborn.pydata.org/generated/seaborn.histplot.html) for more information. 😄
Hi -- do you use pip? If so you can run "pip install seaborn -U" to upgrade to the latest version in your Terminal or Shell. Or you can run "!pip install seaborn -U" from within a Jupyter Notebook.
Kimberly, these are really great videos. At higher age, I am hitting University again for DH and python, pandas and co are giving me a hard time. You make it all look so easy, and it functions with me. Thank you so much. God bless you and have a good start into 2023.
Thanks Kimberly, for making such a detailed video on each plots. Loved the whole playlist :)
Thanks so much for the wonderful video tutorial. You have what it takes to teach and speak clearly whatever the subject is. Awesome!
playing with keywords is always a great deal, beautiful explanation of all useful keywords.....
Glad you enjoyed it!
On TH-cam, in a class that I am taking online, the written documentation, everything associated with these packages are a steaming pile of 💩. Your videos and your way of presenting this material is the best thing that I have seen anywhere. There really is no comparison. You have an amazing gift in teaching this material. If you did a subscription website about data science and all these packages, I would subscribe gladly. I think a lot of people would. Excellent work. Some of the best datasci or even comsci teaching videos that I have ever seen.
Thank you so much for your hard work put into this video! It helped me a lot :)
Oh wonderful! Very glad to hear my video was helpful! 😄
So cool and easy to learn...Nice Explanation ..please do more .Thank you..!!
Quick & to the point! Sweet!! TQVM.
Thanks! Glad you enjoyed this video! 😄
Ur lectures are awesome....😍 Keep going🔥
Thank you! And will do 🔥😎
Thank you for the videos! Is it possible to make a 3D plot of the bivariate data, using a histplot or KDE plot, so for example you would see "mountain" peaks for high frequencies, and "lowlands" for low frequencies?
You kept it so simple . Nice Explanation :):)
Thank you - I do my best to break it down and keep it simple! 😄
Thank you very much Kimberly :)
You're very welcome! 😄
your videos are amazing!!! thank you
So glad you like them - cheers!
Thank you
I think that the series would be much better if it was distinguished when and why to use these different graphs and what do they mean statistically
Thanks for watching and thanks for the tip! I've definitely considered doing more videos about selecting an appropriate visualization technique. 👍
Thank you for an interesting and well put tutorial on how to use Seaborn! I have one question regarding the Hue. I get this error when calling for a column name that has 8 different words in it.ValueError: The following variable cannot be assigned with wide-form data: `hue`. Any idea why this is happening? When I'm plotting a sns.relplot with the same data it works fine
Thanks for this video. You're amazing. It helps a lot.
So glad you enjoyed the video and that it helped!
very useful video
Thank you for the amazing explanation, I have a qs: what plot should i use to view distribution? Only histogram?
You're welcome! The histogram is probably the most well known distribution plot, but there are a few others. The KDE plot is a way to see a smoothed-out, continuous approximation of your data's distribution function. (You can check out my KDE plot video if you want to learn more: th-cam.com/video/DCgPRaIDYXA/w-d-xo.html) Seaborn also has a rug plot, which is basically a histogram with a zero-width bar, and an ecdf plot that looks more at the cumulative distribution. I'm planning to also make a video about the Seaborn displot which combines all of these!
thanks a lot
Great!
Thanks!
You’re very welcome! Thanks for the support - cheers! 😀
hey! how can I create a histogram with the horizontal axis as the data and the vertical axis as the absolut frecuency?? If anyone can help me I will be very grateful, thanks in advance!
Hi there - the seaborn histplot has an argument called "stat" that may be helpful. The default is stat='count', which just counts up the number of observations in each bin. But you also have the options of 'frequency', 'percent', 'probability', and 'density'. See the seaborn docs (seaborn.pydata.org/generated/seaborn.histplot.html) for more information. 😄
hello madam, i have version 10, how can upgrade to 11.can you please tell me.
Hi -- do you use pip? If so you can run "pip install seaborn -U" to upgrade to the latest version in your Terminal or Shell. Or you can run "!pip install seaborn -U" from within a Jupyter Notebook.
Thank you :D
Most welcome! 😄
Thanks alot mam
You're very welcome!