This video is really emotional for me. I started my master's degree ~2 years ago, and I learned a lot from you Kimberly. Now I'm finishing my master's, and watching one of your videos again brings me back to the same feeling when I started my master's. We love you, Kim. Thank you so much! Cheers from Mexico :)
@@KimberlyFessel Thank you Kim! In fact your name is in my thesis, in the "Acknowledgements" section together with other awesome people like Josh Starmer (from StatQuest) or Chanin (from Data Professor)
Hi Kimberly, thanks for the video! As as social scientist, I would love to see a video on the categorical data type in Pandas: order, unorder, reorder values, transform to numerical data type and stuff like that.
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! 👍
This video is really emotional for me. I started my master's degree ~2 years ago, and I learned a lot from you Kimberly.
Now I'm finishing my master's, and watching one of your videos again brings me back to the same feeling when I started my master's.
We love you, Kim. Thank you so much! Cheers from Mexico :)
Aww - congratulations on your Master's!! So happy to be a part of your journey. Best wishes! ☺
@@KimberlyFessel Thank you Kim! In fact your name is in my thesis, in the "Acknowledgements" section together with other awesome people like Josh Starmer (from StatQuest) or Chanin (from Data Professor)
We missed you - welcome back!!
Thank you so much - it's good to be back!
Been a while. Looking forward for this series!
Yes, it has! So glad to hear that. 😊
hello Kim
I'm so glad you're back
Thank you for the great videos you make
Please continue this way
Thanks for the encouragement! 😁
very glad to have found this channel!
Happy to have you here - welcome! 😀
welcome back Kimberly, it's a long time
Sooooo long! Thanks for the welcome 😄
New subscriber here. Excellent exposition. I am excited to go through your old material and see your new stuff.
Awesome, welcome aboard! 👍
Very nice!! Thanks. Looking forward to seeing more videos.
Thank you! More to come for sure 🙌
Please make more videos Kimberly I love your teaching style 😭
Hopefully new videos on Polars coming soon! 😏
Glad to be back Kimberly! I'm curious to see how you used ChatGPT for file creation and really excited for the new series!😄
Thank you! Yes, I’d love to make a video about ChatGPT for the dataset as well. 😁
@@KimberlyFessel Hi Kimberly!!! Have you made this video yet? I am keen on it, please.. Thank you
@@mfoncharles1401 Not yet - but thanks for the reminder!! 👍
@@KimberlyFessel Alright, then. I'll be waiting... Thank you in advance.
Hi Kimberly, thanks for the video! As as social scientist, I would love to see a video on the categorical data type in Pandas: order, unorder, reorder values, transform to numerical data type and stuff like that.
Great suggestions! Thanks for those 😄
Welcome back
Thank you
Finally , Where where you Kimberly?)
Right!? Lots of professional and personal milestones over the last two years, but very happy to be back here on YT now! 😄
@@KimberlyFessel We missed you and your videos :)
'!ls' is not recognized as an internal or external command,
operable program or batch file.
!ls for Mac and Linux, !dir for Windows 👍
Can I have an email from you to get details about online course registration?
I have great ideas for online courses
thank you
You can reach out to me via my company email address: hello at drkimdata dot com 👍
Chunksize!
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! 👍