4 Pandas Functions That I Wish I Knew Earlier

Let me know which function was new for you, or even better, share your favourite pandas trick in the comments.

Great tips, thanks! I was making many of these things the "Hard way "

Can you make while video on lambda

Wow! What a revelation! Great video! I think this format deserves a whole playlist!

Thank you for some very useful tips. I didn't know of the nlargest & nsmallest functions so thanks for sharing those

The query function is new to me. It is similar to applying filters on the database, but definitely faster for generating results. Thank you for sharing!

Didn’t know nsmallest and nlargest, along with cut. Great vid, thanks!

thanks a lot. very useful. You also showed the old way.

Wow thank you

This was truly helpful.

Amazing !

Wonderfully done, Thanks

Thank you

Fantastic!! Thank you

Never used cut before. Definitely a time saver if you need sub categories

Amazing work! Thank you! I love your videos! Your videos have made my life easier. Most functions were new to me.

as.index = False for flatten the data set

How did you make the jupyter sections collapsible? Looks neat!

I really wish I knew these earlier

Thank you so much!

Gotta go use the nlargest right now! It solves a problem that I have at the moment.

I learned query thru your (awesome) streamlit tutorials. Didn't know about cut, super useful. Do you know how to cut in multiple dimensions? Say in this case, gender and tip? To produce an occurrence chart?

wow, query() is completly new for me, awesome, thanks

Your videos are on the next level buddy! Keep it up. But, can you start with Machine Learning and Deep Learning course only the coding part that can be understood by everyone?

Fantastisch! Kurz und sehr informativ!

I've been using Pandas for a few months now and everything in this except groupby() was new to me. I can't believe I've watched two Pandas tutorials and this is the first time I've learned about query().

Thank you very much! If you are looking for ideas, please do video about advanced combinations of groupby function and other methods.
Anyway, thank you for short description in this video too :)

Hi Sven, once again saw ur informative video. How to write SQL query displaying strings (select * from friend LIKE %string %) using pandas. I tried with str.contains but literally failed..

Great video. Query was new to me and I’ll definitely put it to work.

short and straight to the point. need more of these 4 min tips! thank you

Cut and float('inf') was new for me

Great content as always.

Thank you very much for your tips, they are really very useful, excellent for continuing to share !!

Your tips are awesome 👏

Amazing tips 👌 I really appreciate it.

Very good short cut code.

Cut is new for me..

Really helpful. Gonna save my hours of hard work :")

Great video!!

Marvelous video
Many thanks 🦾🦾

This is awesome! Saved and liked this video. I am actually working on groupby now to better master it for visuals. Not the best at setting up filters(using number or most of the time counting strings and numbers) and then using it in my groupbys to graph them.

That said here is something really cool I found out.
Making a new column filter and inserting it in the position I want for better comparing

df.insert(1, “new column’s name”, df[“column1”] / df[“column2”])

What the above does is inserts at index 1 a new column named whatever, and based on a condition(in this case dividing) so simple but 🤯

That was amazing!!!!! Thank you so much! Your videos are truly meaningful! ❤️❤️❤️

@CodingIsFun Using aggregate function, how to get an aggregate reject% (defects/production)?
df columns are |date | production| defects|

Thanks man

Very good! Could you make a tutorial on data handling inside def, for loop functions? I wanted to know the importance of putting lines of code inside def functions for optimization.

Thanks 🐱

Thank you very much for your amazing tutorials.