I'm a 38-year-old starting to learn coding and an interest in machine learning. Your easily understandable videos are a huge help for beginners like myself.
I'm 28 and learning all this too. My degree is in physics and mathematics, but I have very little coding experience. I just got hired for a job. The hiring process and application was very strange, extremely vague. Long story short, after a month it's become apparent to me that while my job title says "physicist", the work my department is doing is much closer to data science, data analysis, and the occasional modeling and simulation. Learning all this stuff makes for a good use of the mandatory telework days, though.
As a newbie, I deeply appreciate you showing an error you got on the fly and voicing your thought process while you resolved it. That is something I don’t typically see in tutorials or pre-recorded lessons and it’s really great to have a chance to mirror someone’s thought process while still developing your own techniques. 10/10
I am a PhD student in Statistics, but currently a ML intern at Microsoft and your videos are the only thing that help me transition from the statsy R-coding world to Python. Thank you!
Nice to see these new and fresh tutorials. Biologist here, learning all the Python I can for data analysis in my spare time. Your presentation of the documentation makes the information invaluably easy to understand, really. Thank you very much
Video Timeline! Background Info 1:15 - What is NumPy 1:35 - NumPy vs Lists (speed, functionality) 9:17 - Applications of NumPy Code 11:08 - The Basics (creating arrays, shape, size, data type) 16:08 - Accessing/Changing Specific Elements, Rows, Columns, etc (slicing) 23:14 - Initializing Different Arrays (1s, 0s, full, random, etc...) 31:34 - Problem #1 (How do you initialize this array?) 33:42 - Be careful when copying variables! 35:45 - Basic Mathematics (arithmetic, trigonometry, etc.) 38:20 - Linear Algebra 42:19 - Statistics 43:57 - Reorganizing Arrays (reshape, vstack, hstack) 47:29 - Load data in from a file 50:20 - Advanced Indexing and Boolean Masking 55:59 - Problem #2 (How do you index these values?) Thank you for watching! Hope you enjoyed and let me know if you have any questions! Subscribe pls if you haven't
Keith Galli, I know this is specific to Numpy, this is a pandas specific question. If I wanted to convert a column in place of a DataFrame from byte type |S32 to string what's the best way to do this. I've been unsuccessful in changing the datatype in place using applymap from |S32 to as string. I'm using a library that create DataFrames in which strings are typecast to bytes.
Hello Keith Galli. I made notes for your Video. The video was really good. thank you. docs.google.com/document/d/1Pk13GuHdKotR0SR3PFsi0MzpMzj1m4bo9DEllm9tsrY/edit?usp=sharing
Thank you for the great course but most of all thank you for not being messy and going every where. Most tutorials dont follow any logic and jump from one thing to another without any logical line Definitely the BEST tutorial i have watched !
Thank you so much for this. I am 44 years old making the transition to Data Analysis and enjoying the lessons. I struggled with the final assignment at the end so I hope more practice will help me grasp the concepts better. Cheers.
I did NOT know you can 'put' matrix 'inside' a larger matrix like the way you demonstrated. That was real awesome. my jaw dropped and started laughing. This math world i live in makes learning and programming so much more better and insightful. 3 hours passed after I finished my data session. Thanks for the prime grade content!
Hi Keith just wanna say thank you very much for this video!! After watching your video, I went from no experience with pandas and little knowledge of python programming to now learning data science in a bootcamp! This video really changed my life!👍👍🌟🌟
This is an incredible video on Numpy, covering sooo many basic things and explaining them so freaking well. I have been on this video since 3 days, continuously learning from this video, documentation, geeksforgeeks, stackoverflow, also made 7 programs myself to try out every method myself and I have enjoyed every single moment. Thank you so much man!
This tutorial is great, thank you so much! Just an extra note about Problem #2 For the second question, we can also get the offsetted diagonal by the following neat way: np.diag(a, k=1) where k specifies the number of offset, the default will be k=0
I dont know how you you did it, but its difficult for me to sit in a code lecture for 1 hour and I understood and was lively till the end of this video. Love it man thanks. ❤️
Simply the best tutorial for a beginner!!! Taking time to introduce the numpy.reshape function( happened at 75th percentile of the video) is very great!!! Many tutorials just jump right into the reshape function. Thanks a lot Keith Galli
I've started using numpy just two days back and I find this content like God level. Extremely concise and step-by-step guide to the world of arrays and more with Numpy library. Kudos to Keith. Love from a junior Python coder.
Keith - thanks so much for spending your time to make these helpful and time-saving videos for folks such as myself who are learning new skills and technologies. The structure is straightforward and not oversimplified. Also, thanks for not inflating them with unnecessary complexity! I really appreciate the format of a background + "api walk-through" style and the inclusion of a selectable timeline for the option to pick and choose what I need for me at the moment. You rock dude!
This was extremely helpful! I appreciate the fact your video was organized into individual lessons. I'm an old guy just learning python/numpy. Thank you for your time!
I am currently going through your data Science Playlist. Thank you very much. This was even better than the Pandas Tutorial. You should include small tasks an questions in all of your tutorials.
very helpful video ,love from india,i watched pandas video yesterday ,today i searched for numpy ,i am luckiiiii that u uploaded just 1 day back.Please make some more video on Datascience
I've been teaching myself Python and ran into your videos the other day. The delivery was informative and entertaining in its simplicity. Thank you: great videos!
hi! just wanted to say that ur videos are helping a lot. i just started to learn python for fun, aaaaaaaaaaaaaand it appears that it is helping a a lot in my job. it makes things way easier. AND THANKS TO YOU, i can learn thing more faster. wish you the best mate.
quiz at 56:42, we can use the trick tough before. to30 = (np.array(range(30))+1).reshape((6,5))[0:4,1:5][(np.identity(4)==1)] print(to30) 1. generate an array. 2. transform to 6x5 martix 3. cut the 2/20 square. 4. use identity matrix+comparison to obtain the items.
Excellent tutorial! Thank you very much! I really appreciated that you provide a clear and concise overview of a broad number of topics that are critical to getting started.
14:50 You can also compare the itemsize function results as well; This can come in useful when figuring out how much relative space one data type uses over another; Along with the thing that @Keith Galli says, you can also print stuff such as print(a.itemsize < b.itemsize), or print(b.itemsize - a.itemsize).
I have to say your content is helping me tremendously. I don't understand how you do not have more subscribers but I guess since there are so many people trying to do what you do here on youtube it can be a little tough to stand out? idk man but hope youre channel grows more you deserve it compared to silly channels like techlead. Maybe get flashier with some content? lol anyways thanks again and keep up the awesome work!
Thanks for the video. It was one of the most detailed video. Just one suggestion -- please try to explain everything related to a specific topic like when you were explaining "matmul", you showed how it works but you didn't explained how you got the output like if we matmul 2*3 array with 3*2 array then how the result is 2*2, I mean how the multiplication is happening. But overall nice video man.
Dude, one of the best and simplest tutorials I have ever seen. I learnt a lot and would highly recommend it for beginners to develop concrete basics of python and most importantly thank you very much for the time & effort.
@@KeithGalli Also, congratulations for your graduation, I heard it on one of your videos. By, the way, I am a double graduate & improving my skills for GUI & meteorological data visualisation.
![Noob To Max With DRAGON REWORK In Blox Fruits [FULL MOVIE]](http://i.ytimg.com/vi/LnBMOinoOvA/mqdefault.jpg)
I created a second channel with more Python content, check it out!😊
ruclips.net/user/techtrekbykeithgalli
I'm a 38-year-old starting to learn coding and an interest in machine learning. Your easily understandable videos are a huge help for beginners like myself.
Keep going my man. Learning only stops after you die!
I just started myself ! This one was really great! I liked the problems at the end
I'm 28 and learning all this too. My degree is in physics and mathematics, but I have very little coding experience. I just got hired for a job. The hiring process and application was very strange, extremely vague. Long story short, after a month it's become apparent to me that while my job title says "physicist", the work my department is doing is much closer to data science, data analysis, and the occasional modeling and simulation. Learning all this stuff makes for a good use of the mandatory telework days, though.
Me too
Hi man, I am 35, from India, and I m in the same boat as u.
As a newbie, I deeply appreciate you showing an error you got on the fly and voicing your thought process while you resolved it. That is something I don’t typically see in tutorials or pre-recorded lessons and it’s really great to have a chance to mirror someone’s thought process while still developing your own techniques. 10/10
I've been using Numpy for 3 years and this is quite informative for me. Pay attention guys.
may I ask you what kind of job you do? I have learned python and I have a solid understanding about it, but I don't know which way to go
@@mad8ry go somewhere. Go where you can.
@@FirstnameLastname-ys1up Is this suppose to mean something?
@@mad8ry it means you are lost.
@@FirstnameLastname-ys1up BRO
I do ML videos and even this is informativer for me!!
I am a PhD student in Statistics, but currently a ML intern at Microsoft and your videos are the only thing that help me transition from the statsy R-coding world to Python. Thank you!
Very happy to hear that! :)
Nice to see these new and fresh tutorials. Biologist here, learning all the Python I can for data analysis in my spare time. Your presentation of the documentation makes the information invaluably easy to understand, really. Thank you very much
Video Timeline!
Background Info
1:15 - What is NumPy
1:35 - NumPy vs Lists (speed, functionality)
9:17 - Applications of NumPy
Code
11:08 - The Basics (creating arrays, shape, size, data type)
16:08 - Accessing/Changing Specific Elements, Rows, Columns, etc (slicing)
23:14 - Initializing Different Arrays (1s, 0s, full, random, etc...)
31:34 - Problem #1 (How do you initialize this array?)
33:42 - Be careful when copying variables!
35:45 - Basic Mathematics (arithmetic, trigonometry, etc.)
38:20 - Linear Algebra
42:19 - Statistics
43:57 - Reorganizing Arrays (reshape, vstack, hstack)
47:29 - Load data in from a file
50:20 - Advanced Indexing and Boolean Masking
55:59 - Problem #2 (How do you index these values?)
Thank you for watching! Hope you enjoyed and let me know if you have any questions!
Subscribe pls if you haven't
helpful cheers
Keith Galli, I know this is specific to Numpy, this is a pandas specific question. If I wanted to convert a column in place of a DataFrame from byte type |S32 to string what's the best way to do this. I've been unsuccessful in changing the datatype in place using applymap from |S32 to as string. I'm using a library that create DataFrames in which strings are typecast to bytes.
Hello Keith Galli. I made notes for your Video. The video was really good. thank you.
docs.google.com/document/d/1Pk13GuHdKotR0SR3PFsi0MzpMzj1m4bo9DEllm9tsrY/edit?usp=sharing
you missed the np.arange.reshape, which was part of the basic ..... but good job
Make video on pandas, matplotlib & datascience
Thank you for the great course but most of all thank you for not being messy and going every where. Most tutorials dont follow any logic and jump from one thing to another without any logical line
Definitely the BEST tutorial i have watched !
Thank you so much for this. I am 44 years old making the transition to Data Analysis and enjoying the lessons. I struggled with the final assignment at the end so I hope more practice will help me grasp the concepts better. Cheers.
I did NOT know you can 'put' matrix 'inside' a larger matrix like the way you demonstrated. That was real awesome. my jaw dropped and started laughing. This math world i live in makes learning and programming so much more better and insightful. 3 hours passed after I finished my data session. Thanks for the prime grade content!
I love the density of data given in this video and the inclusion of mistakes in the code writing, thank you greatly!
No one has ever kept it this simple and straight. You saved me a lot of time. Keep up the good work !
Amazing video! Thank you so much. I'm 37 and have started tinkering with coding and python. It's so much fun!
I am new in Data Science and i would like to thank Keith for his informative videos, he's simply the best
Same here....
Hi Keith just wanna say thank you very much for this video!! After watching your video, I went from no experience with pandas and little knowledge of python programming to now learning data science in a bootcamp!
This video really changed my life!👍👍🌟🌟
This is an incredible video on Numpy, covering sooo many basic things and explaining them so freaking well. I have been on this video since 3 days, continuously learning from this video, documentation, geeksforgeeks, stackoverflow, also made 7 programs myself to try out every method myself and I have enjoyed every single moment. Thank you so much man!
This tutorial is great, thank you so much!
Just an extra note about Problem #2
For the second question, we can also get the offsetted diagonal by the following neat way:
np.diag(a, k=1)
where k specifies the number of offset, the default will be k=0
this video is very good for beginners. its because you can know more about Numpy and know how to convert numbers in programming.
i been afraid i'm to bad with maths to look at data science - really. but now i seen your vids, thanks so much man! that's all so easy
I'm glad you included the quiz. I actually need to slice a 2D matrix diagonally, and then the solution just appeared! All around good video.
After searching a lot, finally I found a great tutorial on numpy with everything I want to learn. Thanks a lot sir.
Thanks for sharing, had saw Pandas and NumPy tutorials, useful information covered in tutorial videos 👍
*pulls plug off the back of the neck*
*NEO's voice, in my head, right now*
"I know NumPy"
Awesome video mate.
Cheers for sharing this.
lolol
I dont know how you you did it, but its difficult for me to sit in a code lecture for 1 hour and I understood and was lively till the end of this video. Love it man thanks. ❤️
Sat through this taking notes, 500 lines of notes and code. Fantastic video, thanks.
Simply the best tutorial for a beginner!!!
Taking time to introduce the numpy.reshape function( happened at 75th percentile of the video) is very great!!!
Many tutorials just jump right into the reshape function.
Thanks a lot Keith Galli
Nice job doing an intro explaining why NumPy is so powerful before heading to the how-tos
Very well explained, and though I'm not an absolute beginner, it wasn't going too slow. I'll definitely watch your Matplotlib video next
I've started using numpy just two days back and I find this content like God level. Extremely concise and step-by-step guide to the world of arrays and more with Numpy library. Kudos to Keith. Love from a junior Python coder.
one of the best video on numpy that covers all the fundamental operations by numpy...... highly recommendable for learners 😊😊
super informative. I'm currently taking a class for data science and this video REALLY helped! felt like i learned more from you than my class.
Getting me through lockdown!
Glad to hear it! Stay safe & healthy friend :)
Keith - thanks so much for spending your time to make these helpful and time-saving videos for folks such as myself who are learning new skills and technologies. The structure is straightforward and not oversimplified. Also, thanks for not inflating them with unnecessary complexity! I really appreciate the format of a background + "api walk-through" style and the inclusion of a selectable timeline for the option to pick and choose what I need for me at the moment. You rock dude!
I'm halfway through the video and so far so good. Nice tutorial !
This was extremely helpful! I appreciate the fact your video was organized into individual lessons. I'm an old guy just learning python/numpy. Thank you for your time!
I am currently going through your data Science Playlist. Thank you very much. This was even better than the Pandas Tutorial. You should include small tasks an questions in all of your tutorials.
I had to watch all ads because of ur hard work.
very helpful video ,love from india,i watched pandas video yesterday ,today i searched for numpy ,i am luckiiiii that u uploaded just 1 day back.Please make some more video on Datascience
You don't know how much you helped me with your Numpy and pamdas videos. Thanks a ton bro. God bless you. Lots of respect ❤️
Really happy to hear that man! You're very welcome :)
Short but informative, also showed how powerful NumPy is.
This was simple, short, easy to understand while covering all major topics! plus if you know pandas then it's more easy ;)
I've been teaching myself Python and ran into your videos the other day. The delivery was informative and entertaining in its simplicity. Thank you: great videos!
Veryyyyyyy helpful. Went through your Pandas tutorial as well yesterday. Got many doubts cleared! Helping me be productive in the lockdown. Thanks xD
Thank you Thank you Thank you.... I watched almost all your channel videos in this quarantine. I learned a lot. I just wanted to say thanks.
You're welcome! :)
This the very first channel that I've seen such a clear explanation
I watched linear algebra (4 min with x2) part and thank you for your great video. Great effort.
Probably the best explaining NumPy ! Thank you
Brilliant piece of content. Most valuable way to spend 55min of your time!
Just one word, Awesome. Delivered the content with so much ease. Looking forward for some more videos on Data Science :) :). Already subscribed
Aww thanks :)
hi! just wanted to say that ur videos are helping a lot. i just started to learn python for fun, aaaaaaaaaaaaaand it appears that it is helping a a lot in my job. it makes things way easier. AND THANKS TO YOU, i can learn thing more faster. wish you the best mate.
Thanks, I needed this simple and organized background. Now I can build from here. Thanks again.
Nice tutorial was waiting for this since so many days😊
Thank you so much love from India ❤️
You're welcome! :)
Great Videos, I am starting to learn so much about Python because of all of your great videos. Thanks Keith.
Thank you, Keith Galli
Keep up the good work
quiz at 56:42, we can use the trick tough before.
to30 = (np.array(range(30))+1).reshape((6,5))[0:4,1:5][(np.identity(4)==1)]
print(to30)
1. generate an array.
2. transform to 6x5 martix
3. cut the 2/20 square.
4. use identity matrix+comparison to obtain the items.
Great tutorial man, really helped me understand the library
Excellent tutorial! Thank you very much! I really appreciated that you provide a clear and concise overview of a broad number of topics that are critical to getting started.
You made it so easy! Thank you. Will definitely be using this video as a reference in the future. Keep up the great work :)
Great content. The concept is explained in a steady pace and great reasoning. A new subscriber from India.
I really like how non-condescending you are when explaining through each function!
14:50 You can also compare the itemsize function results as well; This can come in useful when figuring out how much relative space one data type uses over another; Along with the thing that @Keith Galli says, you can also print stuff such as print(a.itemsize < b.itemsize), or print(b.itemsize - a.itemsize).
Just finished it on FreeCodeCamp and came here to give it a like. Looking for more videos like this! Take love!
Awesome tutorial, simple n clear. Keep up the good work!!!
Helps me during the lockdown study hours. Thanks Keith!
I've never heard of it before. It's hard, but I'll try to copy it slowly. Thank you.
this was much better than the courses on coursera or udemy, thx for the indepth knowledge you provided.
I have to say your content is helping me tremendously. I don't understand how you do not have more subscribers but I guess since there are so many people trying to do what you do here on youtube it can be a little tough to stand out? idk man but hope youre channel grows more you deserve it compared to silly channels like techlead. Maybe get flashier with some content? lol anyways thanks again and keep up the awesome work!
THanks for the vid! Besides the clear and simple teaching, the interspersed questions were great!
Thanks for the video. It was one of the most detailed video. Just one suggestion -- please try to explain everything related to a specific topic like when you were explaining "matmul", you showed how it works but you didn't explained how you got the output like if we matmul 2*3 array with 3*2 array then how the result is 2*2, I mean how the multiplication is happening. But overall nice video man.
Definitely content worth watching and learning during this lockdown
Thank You.
awesome video keith, really helped me a lot with numpy for a beginner like me
U are doing great job
Great video! A video on curve fitting using python would be much appreciated 🙏
This was super helpful, Thanks Keith!
You are amazing. I have to coding background, yet I'm able to follow you. Thank you for the videos.
thank you so much!
i just start using python, and it really help me.
Cool. Better than many paid course in Udemy!
Very good video. Self explanatory. Thanks a lot for bringing this up.
This was indeed useful, thank you Keith for making it!
Wonderful video man! Been a while since I last used NumPy and this was a great refresher!
very nice Keith Galli, worth session to the beginners to learn Numpy library. Thanks
Dude, one of the best and simplest tutorials I have ever seen. I learnt a lot and would highly recommend it for beginners to develop concrete basics of python and most importantly thank you very much for the time & effort.
Glad you enjoyed!! :). I appreciate the kind words
@@KeithGalli Also, congratulations for your graduation, I heard it on one of your videos. By, the way, I am a double graduate & improving my skills for GUI & meteorological data visualisation.
For Prob#1, I used the following code,
a = np.zeros((5,5), dtype = 'int16')
a[(0,4),:] = 1
a[:,(0,4)] = 1
a[(2,2)] = 9
Great Job Keith!
Helped me a lot. Thanks a ton!
Tks Keith. you're a great teacher. It was enlightening. Greetings from Brazil
56:53
a = np.array([i+1 for i in range(30)])
a = a.reshape([6,5])
a[[i for i in range(4)],[i+1 for i in range(4)]]
You can also use arange instead of range:
a = np.arange(1,31,1)
Or linspace:
a = np.linspace(1,30,30, dtype=int)
There are tons of ways to do it:
np.r_[1:31].reshape(6,-1)
Awesome tutorial !!! Many thanks!! Already liked and subscribed !
Thanks Keith! i found this tutorial very helpful and easy to follow
Oh man, I started learning code watching your videos. Why don't you keep them coming?
This tutorial is a life saver. Thanks, man.
Great video just started to Learn python your videos help Lot thanks bro 2021
Very useful tutorial! Really helped me. keep on!
Congratulations... 10k subs.... keep the good work up!
That test at the very end was awesome.
Awesome tutorial Keith!!! Thanks a lot for sharing your knowledge with us! !!!!
watched to see what numpy does. I am considering taking a coursera course in python data science using numpy and panda.
i loved ur videos and content so much honestly ur courses are premium quality thanks a lot u
Really good tutorial, very good and understandable presentation, def gonna subscribe, pls keep on going
Found your NumPy tutorial very helpful...Thanks!!!
Great tutorial man! I watched it till the end, the tutorial is really well structured and also informative! Thank you very much!
thanks, it actually let me through so i could download it.