NumPy for Beginners in 15 minutes | Python Crash Course
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- Опубликовано: 1 сен 2020
- Kick start your journey to data science with NumPy!
In just 15 minutes you'll learn everything your need to know to get up and running with NumPy. This video goes through everything from installing all the way through to advanced manipulation of your data.
You'll learn how to:
1. Install NumPy
2. Create and transform NumPy arrays
3. Apply mathematical functions and export data
Github Repo for the Project: github.com/nicknochnack/Numpy...
Want to learn more about it all:
Numpy Documentation: numpy.org/
Oh, and don't forget to connect with me!
LinkedIn: / nicholasrenotte
Facebook: / nickrenotte
GitHub: github.com/nicknochnack
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
Music by Lakey Inspired
Chill Day - • LAKEY INSPIRED - Chill... 
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This was great. I'm a large part of the way through the DataCamp Data Analyst in Python certification track and this video introduced me to new functions and really helped solidify my grasp on lists vs arrays.
This is so clear and concise. I started a asynchronous numpy class nd had no idea where to start. This helped a LOT.Thank you so much!
Fast & clear explanation Ta, நன்றி ...
I totally understand your lessons . Thank you!!
Short, specific and to the point. Great, keep going 👏👍
Thanks so much @Muhmmed!
appreciate this very much mate
Thank you so much sir for making this video. 🙏🏻🙏🏻🙏🏻
this is amazing thanks!
Well Explained, Very Insightful
Thank you Nichola :'')
Thank you Nick
Great video, Thanks.
Amazing video ❤
Very good tutorial!
Thanks.. Was having a hard time with a course.
Downloaded ❤️
😅😎I am from somaila in somaliland thank you for creating this amazing tutorial congratulation nicholas renotte
I find it very helpful
Great quality sir, thank you
😁 thanks @Nabil Mercheri! Any other vids you’d like to see?!
@@NicholasRenotte A little late to the party but a real world problem video and steps on how to solve it could be interesting! I'm just learning Python and I'm having a hard time finding projects to do to practice my skills.
Awesome stuff @@guillaumelaprotte5487, I've got some beginner friendly AI/ML tutorials. Check these two out: ruclips.net/video/8k8S5ruFAUs/видео.html and ruclips.net/video/T9KfYaS9hwQ/видео.html
great video! thanks!
Thanks so much @FrankWhite1996!
Thank you.
awesome
🙏🙏 Thanks so much @Isfhan
Sir could you once confirm @15:51 about the axis in this video ? (I think axis=0 for row and axis=1 for column) just a small confusion.
Everything else helped me to revise quick. Thanks :)
Yep, you're correct 0 is for row 1 is for column
I also googled this. Is there a name yet for asking chatgpt something? bc I guess I don't google anymore so much as I consult chatgpt...
Hello Sir. why after saving the array as new array which was after deleting, when you loaded it, it was again like before deleting . shouldnt it be with the changes you made which is deleting the first column? thank you so much
When he deleted part of the array, he never saved that new edited array to the original array variable, which means the original array stays the same after the deletion method.
@@haze13_ thank you for your kind reply
It would have been helpful to first go into what the dimensions of the array represent before jumping into reading arrays? Also, on another channel today I learned lists are not the same as arrays.
This video really helped me get a grasp on the difference between lists and arrays - not entirely because it was explained but because I had to pause and do research. I understood that lists could store data of different types and arrays stored data of exactly one type, what I didn't know was that the size of a list can be changed dynamically and the size of an array is fixed. This is why when we perform manipulations to an array like np.delete() we set the function equal to the name of the array. We're not modifying the array but creating a new one and assigning it to the same variable.
Why we need to use Numpy? To solve what issue?
Because its much better for calculating metrics / statistics / better way to organize your list and it is faster as it is underpinned by c itself
@@ihsanpro9406 Also I would like to add that Pandas on the lower level is built on top of numpy itself, same goes for sklearn, etc... So we can say that numpy is the building block on data science ecosystem in python... and in a single word - numpy is used for linear algebra. [most imp math concept for statistics and data science]
@@mahanirvaantantra thanks for the insight. I’m fairly new to coding but that’s what I understood.