Want to follow along with the same dataset and python environment? Big thanks to someone who made a kaggle notebook with this entire tutorial: www.kaggle.com/code/lizhecheng/pandas-2-0-1-tutorial/notebook Just fork the notebook and explore the data with pandas!
This is a masterpiece Rob. A condensed pandas course. Wow. Even regular Data Scientist can refresh their mind or discover tips and tricks they are not used to use such as the query methods. And what I like the most, it all fits within 23 minutes. I would love to have such videos for some of the other commons libs.
Thank You so much for putting this together Rob, you make it look so easy and it's well explained and very clear. I really appreciate you for sharing this with everyone !
This video came many times in my home page recommendation but i was ignoring cause it's just a 22 min video.....but boom 🤯 ! Omg not even a 2 hours video of others cover all these.❤ thanks
Thank you so much ❣️ I have watched your previous pandas video, but this had everything ❤ it was awesome ❤ I understood everything except for to write csv, Thank you so much for this amazing video ❤
This is the best video on pandas I’ve seen so far (and I’ve seen dozens). Thank you so much for keeping your explanations short and up to the point!!! Gonna use the video as my top 1 reference resource when I feel stuck!
Hello Rob great video! I have a question, how do you enable the description of the methods that you use. They are showing on the right when you type in the ‘dot’.
This video is fantastic. informative, concise and a strong foundation for pandas. Most importantly, it is easy to understand and follow along. Thanks for the video, I'm subscribing!
@@robmulla I typically take notes when watching videos like this so I am accustomed to pausing. In my opinion it's better when there isn't much filler in between so that it's easy to get to the next point or move back to where you want.
Thanks for the great Video! How did you manipulate that folder with bunch of.csv files to put fit all together in the df? And how to deal with irregular datas in a typical case like this? Have you already done some tutorial explaining and detailing these kind of tasks?
I’m sorry I know this will sound dumb to you guys but how is it listing all option after writing a part of if. Like read_ ( then a whole bunch of different commands like read_csv and so on)? I’m using jupyter lab everyday and haven’t seen that ! Cool
Hi, i have one silly question. How do you get intellisense i.e. functions menu for each object and for each function, the whole list of available parameters. Which IDE you are using ? It really helps to focus on use case rather than mugging up the function names and their syntax.
nice, if would be useful if you could put a link for downloading your dataset so we could play around with your data while you explain, it would be appreciated, for example I would need to see by myself what the difference reindexing does when combining datasets, it is not immediately obvious to me and would require some test and comparisons
The datset is on kaggle. Check out this notebook where someone linked the dataset and included the tutorial code: www.kaggle.com/code/lizhecheng/pandas-2-0-1-tutorial/notebook
It was really helpful, but I think you missed a section for converting data types in dataframes, specially for date types. thank you very much for this summary.
@robmulla do you know a website or where I can find data cleaning exercises or challenges? I want to practice cleaning different kinds of data, any suggestions will be helpful
Great video Rob, I would love to see you explaining Machine Learning and Deep Learning models, from theory to practice using scikit-learn, Keras or Pytorch. You really made things look easy. Can't wait to see another of your awesome videos.
I'm new to Data Science. Type every information on my jupyter lab. And im getting error and not dine. I don't understand this, smh what I'm im doing wrong
I've been learning Pandas for a couple of years on and off now, and have even used it a little at work, and yet there were still a few things in here I didn't know about. The rolling method in particular is a game changer, I've been manually creating functions to do that and now I can just do it in one line of code (and likely faster than my hacked together functions).
Thanks for this. Straight to the point. Great! Do you think Polars is going to be especially disruptive? I’ve been using it a bit and I can’t believe how much faster it is at a lot of things. But pandas is very entrenched (and probably has slightly more friendly syntax).
Hi @robmulla In Handling Missing Data chapter, would be nice, if you could provide your insight as the best approach and what is normally recommended to do, if it is fillna or dropna, I know that it could be subjective to the task at hand, but having insight as expert would be nice.
I'm wanting to ask a bit more of a meta question. How much time do you spend outside of work on your skills? How much passion or drive do you have and what are your routines? I work in medical ML and came across your EDA video and wanted to get a successful person's view on how to improve and grow.
Hi, does anybody know a website or where I can find data cleaning exercises or challenges? I want to practice cleaning different kinds of data, any suggestions will be helpful
Want to follow along with the same dataset and python environment? Big thanks to someone who made a kaggle notebook with this entire tutorial: www.kaggle.com/code/lizhecheng/pandas-2-0-1-tutorial/notebook
Just fork the notebook and explore the data with pandas!
This is a masterpiece Rob. A condensed pandas course. Wow. Even regular Data Scientist can refresh their mind or discover tips and tricks they are not used to use such as the query methods. And what I like the most, it all fits within 23 minutes. I would love to have such videos for some of the other commons libs.
+1
+1
+1
+1
Thank You so much for putting this together Rob, you make it look so easy and it's well explained and very clear. I really appreciate you for sharing this with everyone !
Glad it helped you!
This video came many times in my home page recommendation but i was ignoring cause it's just a 22 min video.....but boom 🤯 ! Omg not even a 2 hours video of others cover all these.❤ thanks
Love that! Share it with a friend or two.
@@robmulla ok
check ur bed tommorow
jk
Wonderful channel for beginner data analysts & learned a lot of concepts from you…. Great work man
This is truly incredible! It's the finest pandas tutorial available on the internet, offering a remarkable balance of breadth and depth.
Thanks Rob for sharing the knowledge and experience to data community 😊
🙌
Great work….thank you for posting the kaggle file!
Brilliant tutorial. Absolutely the best. Thank you, thank you,🏆🏆🙏🙏
Just brilliant! Thank you so much!
You're very welcome!
Thank you so much ❣️ I have watched your previous pandas video, but this had everything ❤ it was awesome ❤
I understood everything except for to write csv,
Thank you so much for this amazing video ❤
This 20 min video is equivalent to 2hrs of other youtube videos...masterpiece
Thanks! Tell your friends.
It is solid tutorial for Data Geeks. Thank you)
This is the best video on pandas I’ve seen so far (and I’ve seen dozens). Thank you so much for keeping your explanations short and up to the point!!! Gonna use the video as my top 1 reference resource when I feel stuck!
Very cool ninja panda style!!! So useful and like a real pro awesome!!!
Hi. I wish I watched this before my last project. Hope you will do an advanced series.
Nice Video Rob. This helped me a lot :)
only jesus can save me
Thanks Rob!
Are you streaming this evening?
Thanks for the content, Rob! it's really excellent! Can you do another video like this but with numpy?
Great lesson
Glad you liked it!
Hello Rob great video! I have a question, how do you enable the description of the methods that you use. They are showing on the right when you type in the ‘dot’.
Thanks. With Jupyter you just do shift-tab
Thank you Rob 😊
This video is fantastic. informative, concise and a strong foundation for pandas. Most importantly, it is easy to understand and follow along. Thanks for the video, I'm subscribing!
Really appreciate the feedback. Glad you found it easy to follow. I was a little worried it might be too fast.
@@robmulla I typically take notes when watching videos like this so I am accustomed to pausing. In my opinion it's better when there isn't much filler in between so that it's easy to get to the next point or move back to where you want.
Thanks for sharing your knowledge
Thank you Rob!!!
Absolutely a masterpiece of a tutorial this one is! Hope you do more of this kind! Thank you for helping me to get kick started with Pandas!
Thanks Rob 15 min done still 7 to go.
Nice! 🙌
Doesn’t appear as tho you really used the power of pandas 2.0 with the backend pyarrow default param and checking for nulls/data types :-(
Thanks for the great Video!
How did you manipulate that folder with bunch of.csv files to put fit all together in the df? And how to deal with irregular datas in a typical case like this?
Have you already done some tutorial explaining and detailing these kind of tasks?
Great video as always ! Would be Nice to have the same one with polars
we are waiting for the next part! I personally wanna see sth on visualization!
Thanks for the feedback. I’ll keep that in mind for the next video.
Helpful overview. Good content. But way too fast. Not everyone has an IQ of 150, Mr Mulla. Slow down..
Do you have a panda functions cheat sheet (df functions) available? Thanks. Follower 👍
I’m sorry I know this will sound dumb to you guys but how is it listing all option after writing a part of if. Like read_ ( then a whole bunch of different commands like read_csv and so on)? I’m using jupyter lab everyday and haven’t seen that ! Cool
Hi, i have one silly question. How do you get intellisense i.e. functions menu for each object and for each function, the whole list of available parameters. Which IDE you are using ?
It really helps to focus on use case rather than mugging up the function names and their syntax.
nice, if would be useful if you could put a link for downloading your dataset so we could play around with your data while you explain, it would be appreciated, for example I would need to see by myself what the difference reindexing does when combining datasets, it is not immediately obvious to me and would require some test and comparisons
The datset is on kaggle. Check out this notebook where someone linked the dataset and included the tutorial code: www.kaggle.com/code/lizhecheng/pandas-2-0-1-tutorial/notebook
It was really helpful, but I think you missed a section for converting data types in dataframes, specially for date types. thank you very much for this summary.
It took me 2 hours and 30 minutes to revise pandas, but it's worth it
cover EDA for time series data
@robmulla do you know a website or where I can find data cleaning exercises or challenges? I want to practice cleaning different kinds of data, any suggestions will be helpful
Thanks bro
amazing!!
thanks for the video, one request though, can we have the same dataset so we can follow along.
Great video Rob, I would love to see you explaining Machine Learning and Deep Learning models, from theory to practice using scikit-learn, Keras or Pytorch. You really made things look easy. Can't wait to see another of your awesome videos.
Very very helpful! thank you so much for this upload
Hey Rob! Any resouce to download and handson with parquest file
I'm new to Data Science. Type every information on my jupyter lab. And im getting error and not dine. I don't understand this, smh what I'm im doing wrong
I've been learning Pandas for a couple of years on and off now, and have even used it a little at work, and yet there were still a few things in here I didn't know about. The rolling method in particular is a game changer, I've been manually creating functions to do that and now I can just do it in one line of code (and likely faster than my hacked together functions).
Can you give an example of a rolling method application? I'm curious
@@mark-dy9zomoving averages
Hi Rob,how to read the details of function in jupyter lab just like 2:22
kagle desnt seem to have any datasets in parquet format, moreover it cant seem to preview those files
How to handle JSONArray in dataset?
Please add speech to audio method
Not enough half way through and I can tell this video is gold.
hi! What plugin do you use to see the details of each function?
Great question! Shift-tab in jupyterlab.
How did you make your Jupiter look like that
Thank you for the videos Rob, your hard work is highly appreciated.
Can u tell me where u execute ur code/ How do I get to the same terminal
Thanks, Rob. That's a great summary of the features. Really useful!
Thanks for this. Straight to the point. Great!
Do you think Polars is going to be especially disruptive? I’ve been using it a bit and I can’t believe how much faster it is at a lot of things. But pandas is very entrenched (and probably has slightly more friendly syntax).
Hi @robmulla
In Handling Missing Data chapter, would be nice, if you could provide your insight as the best approach and what is normally recommended to do, if it is fillna or dropna, I know that it could be subjective to the task at hand, but having insight as expert would be nice.
Its time for you to show us hiw to build a dashboard
I'm wanting to ask a bit more of a meta question. How much time do you spend outside of work on your skills? How much passion or drive do you have and what are your routines? I work in medical ML and came across your EDA video and wanted to get a successful person's view on how to improve and grow.
Masterpiece thanks thief!
Thank you for this lesson and all your work. As always, I learn so much from you! Any chance you'd do a video lesson on data cleaning? 🙏
Audio writing methods
Hello Rob.
1:52 min. how to get that dropdown option
Awesome ❤
Awesome! Thanks!
Thanks for this video. Packed with info, but still easy to follow, no small talk… Really appreciate your effort!
Great as always! Now get to work and make tutorials for seaborn and matplotlib :)
Nice dictionary.
Fair introduction on pandas library
Thank you for the info
Great tip on renaming the multi index columns!!
Glad it was helpful!
Thank u very much.
I can now officially announce and recommend this video to my friends as one stop pandas tutorial and solution.
Thanks Rob
Hi Rob, Please start some series on Tableau. Regards.
I can tell even before watching this video that's its great!!! You're such a great tutor.
Great refresher, but too fast for tutorial. I suggest breaking it in chuncks.
Magic Rob! hopefully be like you one day
This is great work!! Thank you very much for putting it out here!!
🤗
WERY NİCE .. THANKS FOR YOUR EFFORTSS :))
Hi, does anybody know a website or where I can find data cleaning exercises or challenges? I want to practice cleaning different kinds of data, any suggestions will be helpful
this vid is a gem
Thanks! Glad you liked it.
how to get the data of this video
One of my favorite teachers
Good Intro! Thanks!
Very easy to follow along, thank you!
Perfect!
Thanks!
Great stuff!
Thanks Rob 😁.
is this guy AI generated? His jawline is too perfect.
No AI. I’m a real person.
jesus christ bless you
This is great