4 Pandas Functions That I Wish I Knew Earlier

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In this video, I'm going to share with you 4 Pandas functions that I wish I knew earlier. These functions will make data analysis much easier and faster for you!
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[@CodingIsFun]

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

[@smbatharutyunyan5042]

pd.cut is new for me, and is very useful. Thanks !!!

[@BlueSkyGoldSun]

Query Can we plot the conditional query ?

[@CodingIsFun]

@@BlueSkyGoldSun Can you perhaps elaborate on what it is you're actually trying to achieve? Please provide some additional info. Thanks!

[@YYZ722]

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!

[@CodingIsFun]

Happy to hear that you learned something new. Thanks for watching and leaving a comment

[@cryptomugen1315]

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

[@CodingIsFun]

Thank you! Glad you liked the video. Today I will upload a 4min tips video on how to use Python's Pathlib module (to work with file paths & directories in Python) 😃

[@whatisagoodusernamehere]

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

[@CodingIsFun]

Appreciate your comment! Glad you found the video enjoyable. 👍

[@Asparuh.Emilov]

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

[@CodingIsFun]

You are so welcome! Glad you like them. Thanks for watching & taking the time to leave a comment! ❤

[@sawyer82nd]

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().

[@CodingIsFun]

Super, das freut mich zu hören! :) Thanks for watching and taking the time to leave a comment!

[@sandrakyoutube]

Another great video. 👍 Flatten the multiindex and the cut func are new to me. Will serve me well! Thank you!

[@CodingIsFun]

Happy to hear that you enjoyed this one too! Thanks for the comments and support, as always! ✌️

[@mazkaibil9108]

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

[@CodingIsFun]

Hi Maz Kaibil, thanks so much for your kind words! I'm really glad to hear that my videos have been helpful and that you've learned some new things from them. It's always great to hear when my content has made a positive impact on someone's life. 👍

[@hnclienteshn4501]

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

[@CodingIsFun]

A pleasure! Thanks for watching the video & taking the time to leave a comment! ❤

[@torque6389]

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

[@CodingIsFun]

Thank you and happy coding! :)

[@marsshakirov4507]

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 :)

[@CodingIsFun]

Thanks for watching the video and your suggestion. I appreciate it! :)

[@MrChristian902]

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

[@CodingIsFun]

Thanks for the positive feedback! Appreciate you taking the time to leave a comment.

[@tuxib]

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

[@CodingIsFun]

An absolute pleasure, very happy to hear that you learned something new! Happy Coding! :)

[@jqts6490]

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 🤯

[@jqts6490]

Also want to say LOVE the query aspect. Now every time I use pandas I will be able to practice my SQL aspect at the same time. You rock man

[@CodingIsFun]

Glad you liked the Pandas tips! Thank you very much for watching the video & sharing your Pandas trick! 🐼 Happy Coding! 💪

[@valensrwema4251]

Amazing tips 👌 I really appreciate it.

[@CodingIsFun]

Thank you very much! Glad you liked the tips. 😃

[@KhalilYasser]

Thank you very much for your amazing tutorials.

[@CodingIsFun]

Thank you as always Yasser Khalil, your support is much appreciated! 👍

[@SantoRedentor]

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

[@CodingIsFun]

Glad I could help! Thanks for watching and taking the time to leave a comment! :)

[@l--1226]

Wonderfully done, Thanks

[@CodingIsFun]

Glad to hear you liked it! Thank you for commenting and watching.

[@upalkundu2872]

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

[@CodingIsFun]

Glad to hear & thanks for watching the video! 😃

[@vivdroid]

Your tips are awesome 👏

[@CodingIsFun]

Thank you very much! Glad it was helpful!

[@sohambhattacharjee951]

This was truly helpful.

[@CodingIsFun]

Happy to hear that it was useful; thank you for taking the time to leave a comment and for watching the video!

[@bashar9200]

Fantastic!! Thank you

[@CodingIsFun]

Glad to hear you liked it! Thank you for commenting and watching.

[@ermalgashimramori]

Great content as always.

[@CodingIsFun]

Thank you very much! ❤ I genuinely appreciate your support! 💪

[@andybecker5001]

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

[@CodingIsFun]

Glad it was helpful and that you learned something new. Thanks for watching and taking the time to leave a comment!

[@gitgosc7075]

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

[@CodingIsFun]

Thanks for watching the video & your comment! :)

[@JUN1ORLYONN]

Great video!!

[@CodingIsFun]

Glad you enjoyed it! Thanks for watching :)

[@geekyprogrammer4831]

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?

[@CodingIsFun]

Thanks! I was thinking about doing some Machine Learning tutorials, but I think there are already many excellent tutorials here on KZbin. For now, I will stick to office (Excel) automation, visualisations fun Python projects :)

[@StalkedHuman]

These are the same functions i was feeling i needed to learn to get the function i needed

[@CodingIsFun]

Glad it was helpful! Cheers, Sven ✌️

[@doubando]

Thanks this is great, i like the agg() and cut()

[@CodingIsFun]

Cool that you learned something new. Thanks for tuning in!

[@user-qp8ei8lc3d]

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.

[@CodingIsFun]

Thank you for watching the video & great suggestion! I cannot make any promises, but l will see what I can do.

[@noeldoller2958]

Very good short cut code.

[@CodingIsFun]

Thank you very much! 😃

[@chandraprakash934]

Amazing !

[@CodingIsFun]

Glad you liked it. Thanks for watching and taking the time to leave a comment!

[@akshitabatra2500]

Great Video :)

[@CodingIsFun]

Glad to hear you liked it! Thank you for commenting and watching. Cheers, Sven ✌️

[@user-jh4wo6ok4s]

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

[@CodingIsFun]

An absolute pleasure, very happy to hear that you found it useful!

[@aliarshad111]

Informative

[@CodingIsFun]

Glad you liked it. Thanks for watching. Cheers, Sven ✌️

[@marcelocampos665]

Excelente!

[@CodingIsFun]

Thanks! :)

[@pedromerl]

THANK YOU!

[@CodingIsFun]

My pleasure! Appreciate you taking the time to watch and leave a comment.

[@__________________________6910]

Thanks man

[@CodingIsFun]

My pleasure! Thanks for taking the time to leave a comment :)

[@yeahjustlikethat]

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?

[@CodingIsFun]

Thank you very much for watching the video and your comment. I receive many requests for creating individual solutions. As much as I want to help, I simply do not find the time in my daily schedule to develop & test all the different requests. I hope you can understand. Thank you

[@Wilhuf1]

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

[@CodingIsFun]

Happy to hear that it was useful; thank you for taking the time to leave a comment and for watching the video!

[@LouisDuran]

Coming from a T-SQL background a lot of these functions seem to "make sense" as in they are idiomatic to what I wish to do with datasets. But I am glad to see working examples of this.

[@CodingIsFun]

Thanks for watching! 👍Cheers, Sven ✌️

[@datamonster3212]

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

[@CodingIsFun]

Check out collapsible headings: towardsdatascience.com/10-essential-jupyter-notebook-extensions-for-data-scientists-86b68ec7a66e

[@omkarhankare3264]

Thanks 🐱

[@CodingIsFun]

A pleasure! Thank you for watching the video! :)

[@m4meeran]

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

[@CodingIsFun]

Thanks for watching the video! :)

[@m4meeran]

@@CodingIsFun looking forward for more interesting videos like this.

[@bodyart1460]

Thank you

[@CodingIsFun]

My pleasure! Appreciate you taking the time to watch and leave a comment. 👍

[@newday8074]

Wow thank you

[@CodingIsFun]

My pleasure! Appreciate you taking the time to watch and leave a comment.

[@samaritan5197]

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..

[@CodingIsFun]

This is not what pandas.query is supposed to do. Have a look at the following Stack Overflow post: stackoverflow.com/a/45866311 I hope this helps!

[@asankacool1]

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

[@CodingIsFun]

Thanks for watching. I guess, you want to first insert a new column with the reject_ratio. Example: df['reject_ratio'] = df['defects'] / df['production'] I hope this helps!

[@asankacool1]

@@CodingIsFun If a column is added, it will give daily reject ratio. But, what i want is a cumulative % (sum['rejects']/sum['production'])

[@CodingIsFun]

@@asankacool1, I do not know your data(frame), but perhaps you are looking for the cumsum function of pandas: pandas.pydata.org/docs/reference/api/pandas.DataFrame.cumsum.html

[@Apoorvpandey]

I really wish I knew these earlier

[@CodingIsFun]

Thanks for watching the video and your comment! :)

[@suryaryali4434]

Can you make while video on lambda

[@CodingIsFun]

Thanks for watching and your suggestion!

[@djangoworldwide7925]

As an R user I must say you guys miss so much of dpkyr, tidyr and other powerful tools (such as inequality joins)

[@CodingIsFun]

Never used R, so I would need to look up what those tools can do :)

[@deepakmehra8627]

Cut is new for me..

[@CodingIsFun]

Thanks for watching the video :)

[@pritomroy2465]

as.index = False for flatten the data set

[@CodingIsFun]

Thanks for watching the video and sharing your pandas tip! 👍