4 Pandas Functions That I Wish I Knew Earlier
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- เผยแพร่เมื่อ 22 ก.ค. 2024
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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!
Download the Jupyter Notebook here:
► github.com/Sven-Bo/four-panda...
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*Let me know which function was new for you, or even better, share your favourite pandas trick in the comments.*
pd.cut is new for me, and is very useful. Thanks !!!
Query
Can we plot the conditional query ?
@@BlueSkyGoldSun Can you perhaps elaborate on what it is you're actually trying to achieve? Please provide some additional info. Thanks!
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!
Happy to hear that you learned something new. Thanks for watching and leaving a comment
short and straight to the point. need more of these 4 min tips! thank you
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) 😃
Wow! What a revelation! Great video! I think this format deserves a whole playlist!
Appreciate your comment! Glad you found the video enjoyable. 👍
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().
Super, das freut mich zu hören! :)
Thanks for watching and taking the time to leave a comment!
That was amazing!!!!! Thank you so much! Your videos are truly meaningful! ❤️❤️❤️
You are so welcome! Glad you like them. Thanks for watching & taking the time to leave a comment! ❤
These are the same functions i was feeling i needed to learn to get the function i needed
Glad it was helpful! Cheers, Sven ✌️
Another great video. 👍 Flatten the multiindex and the cut func are new to me. Will serve me well! Thank you!
Happy to hear that you enjoyed this one too! Thanks for the comments and support, as always! ✌️
Never used cut before. Definitely a time saver if you need sub categories
Glad it was helpful and that you learned something new. Thanks for watching and taking the time to leave a comment!
Great tips, thanks! I was making many of these things the "Hard way "
Thanks for the positive feedback! Appreciate you taking the time to leave a comment.
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 :)
Thanks for watching the video and your suggestion. I appreciate it! :)
Great video. Query was new to me and I’ll definitely put it to work.
Thank you and happy coding! :)
Amazing work! Thank you! I love your videos! Your videos have made my life easier. Most functions were new to me.
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. 👍
Thank you for some very useful tips. I didn't know of the nlargest & nsmallest functions so thanks for sharing those
An absolute pleasure, very happy to hear that you learned something new! Happy Coding! :)
Thank you very much for your tips, they are really very useful, excellent for continuing to share !!
A pleasure! Thanks for watching the video & taking the time to leave a comment! ❤
Thank you so much!
Gotta go use the nlargest right now! It solves a problem that I have at the moment.
Glad I could help! Thanks for watching and taking the time to leave a comment! :)
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 🤯
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
Glad you liked the Pandas tips! Thank you very much for watching the video & sharing your Pandas trick! 🐼 Happy Coding! 💪
wow, query() is completly new for me, awesome, thanks
Thanks for watching the video & your comment! :)
Really helpful. Gonna save my hours of hard work :")
Glad to hear & thanks for watching the video! 😃
Thank you very much for your amazing tutorials.
Thank you as always Yasser Khalil, your support is much appreciated! 👍
Wonderfully done, Thanks
Glad to hear you liked it! Thank you for commenting and watching.
This was truly helpful.
Happy to hear that it was useful; thank you for taking the time to leave a comment and for watching the video!
thanks a lot. very useful. You also showed the old way.
An absolute pleasure, very happy to hear that you found it useful!
Thanks this is great, i like the agg() and cut()
Cool that you learned something new. Thanks for tuning in!
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.
Thank you for watching the video & great suggestion! I cannot make any promises, but l will see what I can do.
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?
Thanks! I was thinking about doing some Machine Learning tutorials, but I think there are already many excellent tutorials here on TH-cam. For now, I will stick to office (Excel) automation, visualisations fun Python projects :)
Amazing tips 👌 I really appreciate it.
Thank you very much! Glad you liked the tips. 😃
Didn’t know nsmallest and nlargest, along with cut. Great vid, thanks!
Happy to hear that it was useful; thank you for taking the time to leave a comment and for watching the video!
Very good short cut code.
Thank you very much! 😃
Cut and float('inf') was new for me
Thanks for watching the video! :)
@@CodingIsFun looking forward for more interesting videos like this.
Fantastic!! Thank you
Glad to hear you liked it! Thank you for commenting and watching.
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.
Thanks for watching! 👍Cheers, Sven ✌️
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?
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
Your tips are awesome 👏
Thank you very much! Glad it was helpful!
How did you make the jupyter sections collapsible? Looks neat!
Check out collapsible headings: towardsdatascience.com/10-essential-jupyter-notebook-extensions-for-data-scientists-86b68ec7a66e
Great content as always.
Thank you very much! ❤ I genuinely appreciate your support! 💪
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..
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!
Great video!!
Glad you enjoyed it! Thanks for watching :)
THANK YOU!
My pleasure! Appreciate you taking the time to watch and leave a comment.
Great Video :)
Glad to hear you liked it! Thank you for commenting and watching. Cheers, Sven ✌️
Amazing !
Glad you liked it. Thanks for watching and taking the time to leave a comment!
Informative
Glad you liked it. Thanks for watching. Cheers, Sven ✌️
Thanks man
My pleasure! Thanks for taking the time to leave a comment :)
Thank you
My pleasure! Appreciate you taking the time to watch and leave a comment. 👍
Excelente!
Thanks! :)
Thanks 🐱
A pleasure! Thank you for watching the video! :)
@CodingIsFun Using aggregate function, how to get an aggregate reject% (defects/production)?
df columns are |date | production| defects|
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!
@@CodingIsFun If a column is added, it will give daily reject ratio. But, what i want is a cumulative % (sum['rejects']/sum['production'])
@@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
Wow thank you
My pleasure! Appreciate you taking the time to watch and leave a comment.
Can you make while video on lambda
Thanks for watching and your suggestion!
I really wish I knew these earlier
Thanks for watching the video and your comment! :)
Cut is new for me..
Thanks for watching the video :)
As an R user I must say you guys miss so much of dpkyr, tidyr and other powerful tools (such as inequality joins)
Never used R, so I would need to look up what those tools can do :)
as.index = False for flatten the data set
Thanks for watching the video and sharing your pandas tip! 👍