Daniel Chen: Cleaning and Tidying Data in Pandas | PyData DC 2018

[@ediray2010]

This is the single most helpful python pandas tutorial that I've ever seen. Thank you Daniel Chen. Great job!

[@mishasawangwan6652]

you could drop the word “single”

[@mohitupadhayay1439]

Time stamps : 32: 57 : Melt function for Transposing columns and Rows. 42:17 : Handling multiple variables 48 :11 : Split Function 56: 20 : Variables stored in both Rows-Columns. 1:01:10 : Pandas Pivot table 1:06:11 : Reset_index() function 1:10:23 : Drop duplicates function

[@mishasawangwan6652]

the video is timestamped

[@tabaicanking3701]

One of the best data cleanih and tidying tutorial on KZbin

[@sergioluis6977]

Simple and easy to follow. Completely new Python student and this was such a great introduction, eager to continue learning! Thanks a lot Daniel

[@Okwudo]

I'm just 10 mins in and I already love you. Thank you for this presentation

[@yassaryelurkar3631]

Great video. Underrated, isn't recommended by youtube. Btw thanks for the video.

[@kennethstephani692]

This is a terrific video, Daniel. Thank you very much!!

[@OliverShey]

This is such a wonderful tutorial.

[@matosleni]

Excellent lecture! Thanks for letting it public!

[@empr1ze]

Great video, that gets to the point and explains complex concepts so elegantly.

[@marcmarc172]

Those timestamps were well made and very helpful - thank you!

[@gagandhaliwal40]

Well explained and easy to understand . I appreciate your help so much. Many thanks

[@MouradBENKADOUR]

Excellent Tutorial about pandas and Data Cleansing and Data preparation

[@vinuvish]

Thanks for the video. I started with pandas and got confused with the brackets, it is clear now.

[@iliasdimadis2975]

Great presentation! Thank you very much for this helpfull and understandable-easy to follow Tutorial.

[@asankacool1]

Awesome explanation and super content @Daniel !

[@michaelhaag3367]

Very nice talk, I finally got it. Seems like a nice dude also

[@lucasmunkombwe4444]

Great tutorial for beginners like me. Thank you

[@ajiboroibrahim6845]

Thanks for the timestamps but @ 16:26 it's df.loc not df.iloc

[@danielpaul65]

Good skills I've learnt here

[@sustainability.s]

Excellent really learnt a lot 👍👍👍

[@HellenofTroy897]

But how do you melt all the other columns in the pew dataset? You just demonstrated the

[@oyinkanchekwas]

Hello, please how do we get the datasets you used in the video? Could you share a link we could download from? Thanks.

[@afonsoosorio2099]

@KKC, Sir, right at the beginning, 0:04:00 to 0:40:00, datasets to be found at repository github, the link is there.

[@adamrosariorodriguez]

A great tutorial. Thanks!

[@redserpent]

where is the URL to download the data? I missed it somehow. Thank you.

[@gbengakolawole403]

A bit lengthy but worth it. Lots of good stuff in it.

[@kaluemeka4194]

Very excellent and explanatory in detail

[@pritomroy2465]

Newly starting Python. I realize that Data Manipulation is Far easier in R (Use tidyvarse)!!

[@marinagrabelli8623]

So useful, thanks.

[@olabisifayose7386]

Thank you. Very Very helpful

[@luisfelipeperezherrera5887]

it was very useful, thank you so much

[@lucianlackman8103]

Amazing tutorial thanks man

[@mustajab1767]

hey guys where can I find the relevant datasets from the tutorial. somebody please let me know (TIA)

[@geneee]

so somehow, problems 1 to 3 contradicts problem 4 and 5?

[@somethingness]

This is the first time I've understood why melt is called melt. 32:36

[@emisarangae125]

looks like the url for the data has been removed. I also can't find it in github

[@omprakashreddy4230]

simply wow !!

[@13kriztian]

Excellent tutorial

[@Sagaofsr]

Great tutorial

[@sandraoriji8351]

Awesome 👍

[@krishnavhatkar4729]

from where i can get the dataset

[@sadhnarai8757]

Ver good Aman

[@DatabaseAdministration]

could someone post the link to github repo?

[@arshad1781]

Thanks you 😊

[@SabarishS-x3j]

I feel like Daniel and Leonard's characters in The Big Bang Theory have a lot of similarities. Did anyone else notice that?

[@bryancapulong147]

Thank you

[@shariqkhatri4657]

This should be called tidy data for complete beginners

[@zeadzead7237]

جميل جدا ورائع جدا ورائع جدا

[@jongcheulkim7284]

Thank you^^

[@salimkaplan464]

Hadley Wickham'ın askerleriyiz.

[@yeahgoood]

5:24 to skip all the pointless nerd BABBLING.

[@empr1ze]

That's why you're still an underling.

[@pavanpandya9080]

Excellent tutorial