Python NumPy Tutorial for Beginners

Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more.
💻 Code: github.com/KeithGalli/NumPy
🎥 Tutorial from Keith Galli. Check out his KZbin channel: / @keithgalli
⭐️ Course Contents ⭐️
⌨️ (01:15) What is NumPy
⌨️ (01:35) NumPy vs Lists (speed, functionality)
⌨️ (09:17) Applications of NumPy
⌨️ (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?)
⭐️ Links with more info ⭐️
🔗 NumPy vs Lists: / channel
🔗 Indexing: docs.scipy.org/doc/numpy-1.13...
🔗 Array Creation Routines: docs.scipy.org/doc/numpy/refe...
🔗 Math Routines Docs: docs.scipy.org/doc/numpy/refe...
🔗 Linear Algebra Docs: docs.scipy.org/doc/numpy/refe...
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[@cornelius600]

1.25 speed is perfect, thanks for the video

[@cybermanithan7514]

thanks for tips

[@marcustulliuscicero9512]

I'm on 2.5

[@ugos_bizarre_adventures_6866]

Thx bro

[@JaFupy]

Yup

[@toke7342]

2x speed is better. Saves alot of time.

[@cram2208]

Seriously, side-by-side comparisons are the BEST !! As visual as it can get ! 🙏

[@gokul8747]

⭐️ Course Contents ⭐️ ⌨️ (01:15) What is NumPy ⌨️ (01:35) NumPy vs Lists (speed, functionality) ⌨️ (09:17) Applications of NumPy ⌨️ (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?)

[@yahyafati]

Why?

[@baylee1791]

thanks bhai

[@za012345678998765432]

+

[@sanketemala1118]

@@yahyafati u were dumb or something'

[@alexroode2659]

@gokul8747 is the hero of this comment section

[@jozbornn]

This was a phenomenal overview of numpy. I feel confident that I can tackle more advanced topics now!

[@TheNotoriousFonzy]

Keith, I've taken a heavy interest in data science lately and your courses absolutely rock !!! Many thanks to you for teaching me these fundamentals in such an informative, easy-to-understand manner.

[@George-te4ms]

how is the progress?

[@bluegtturbo]

You Sir are an amazing teacher!! There are many software gurus in the world, but sadly few who can impart their knowledge as you do...

[@shritishaw7510]

finally, done with the entire video, tbh, it took me 6 hours to get myself acquainted with the working of the NumPy library and the Jupyter notebook. Thank you for this awesome tutorial

[@rajdeepchakraborty7961]

One of the finest Numpy tutorials. Keep up the great work guys!

[@mrak8948]

imp points: 5:38 contiguous memory 8:28 how are lists diff than Numpy 9:42 applications of numpy 26:17 full and full like

[@smiley-wu1kn]

This is the first tutorial that I actually finished. Thank you, Keith!

[@arthurgomberg164]

Well done. Quick ,short & straight to the point!

[@stoyangeorgiev77]

for the part at 31:50 a = np.zeros((5,5), dtype='int8') a[:,0:5:4], a[0:5:4,:], a[2,2] = 1, 1, 9

[@SK-zl3qg]

Absolute clarity and upto speed. Very comprehensive coverage.

[@63khushalsolanki9]

Thats the most english I have heard all day

[@shdnas6695]

@@63khushalsolanki9 lol

[@dohkang3725]

Thank you for great video, Keith Galli. I had some problem of understanding Numpy before. Thanks to your help, I have strong basic knowledge of Numpy :)

[@tonyhathuc]

This is absolutely great content! Thank you so much for doing this!

[@CzechPatriot1918]

Watching this at 2x speed so I can learn Numpy in 29 minutes instead of 58 minutes.

[@krrishkataria560]

i have installed video controller extension, i am watching at 2.5x

[@biological-machine]

@@krrishkataria560Just don't watch the video and read the specific documentation. It will be even faster if you have skill.

[@flow2917]

great vid, thanks for leaving the little mistakes in there, helps me remember that I dont have to be perfect at this and remember every little thing

[@cangulmez9248]

This video improved my numpy information. So thanks everybody who contributed.

[@Luxcium]

This guy is smart and he makes this stuff really interesting !!! I like it !!!

[@user-yt7vl3uh7g]

love the content ! i have just started to learn numpy for my course and this certainly helped !! cheers , would be looking forward to your content!

[@laiqianji7078]

Nice mate! What a wonderful review from all the possible uses of Numpy. Thanks a lot!

[@cameronp3157]

Amazing! Thank you for the explanation dude. It is really helping me with a certification course that I’m taking now

[@rogerknight8092]

Much better than courses that I've paid good money for - Top Man Galli

[@SMFahim-vo5zn]

Just finished it. It was really awesome! I like how you would look at your notes, so that we don't see you 😂. Thanks a lot for this tutorial Keith Galli. Not following any other tutorial on Numpy. Take love!

[@steevenkenny9791]

Thank you very much sir... the course is crystal clear... thank you

[@judeleon8485]

Thanks so much Keith, for the very educating tutorial. Quite explanatory

[@rajanalexander4949]

Excellent pace and explanations -- thank you!

[@avivran1198]

Thanks for your effort and the good stuff. Effective introductory! Thanks

[@rafaelgpq]

Awesome Tutorial. Thank you very much, Keith !

[@Tradesbycami]

Thanks for the free class! I'm just learning programming :) I felt very motivated after I could make the array on Problem #1

[@tonyohore288]

learning as well, would u like a study budy?

[@fabrizio.anichini98]

Thanks you Keith , great video (also subscribed to your channel). Also thanks to FCC , love you for your service!

[@san.s.shriyan]

Even OpenCV a top choice among computer vision professionals uses numpy array to store the image data.... Basically if you know how to manipulate numpy array you can do fine / pixel level operations... really appreciate your video.

[@nicholasziglio]

Really well put together, thanks! :)

[@rickpala_]

excellent tutorial. feeling comfortable with numpy now thanks to you :)

[@user-cy3je1xd1c]

Thank you! The only thing was a little bit complicated to me is working with axis. None the less, great tutorial!

[@user-tf1bs6yy3b]

رحؤنشضهكبءخؤذمء ء يددحمس

[@shainamehta408]

Thank You for clearing my concepts on NumPy library.

[@hasandaaboul5322]

Really amazing introduction to numpy, it helps a lot Thank you man!

[@nutellabrownbelt9023]

Super helpful tutorial. When you went back and used -1 indexes instead of exclusive 4's at 33:36 my world stopped imploding. Thank you.

[@mechtorious]

Why tho?

[@mamadouaw3129]

Best crash course on Numpy ! Thank you for your interesting videos

[@redviper20]

Here's how you watch these videos: Hover over your right arrow key and hit it when he's initializing or doing some boring stuff, and when something interesting happens, something you might wanna know, you stop, pay attention, maybe type something similar in your own jupyter notebook; continue. Don't watch it at 2x speed. It doesn't work... Reading docs is hard! So this video is really cool.

[@mohamedgaal5340]

Thank you Keith for this awesome tutorial!

[@robsonsilvadasilva]

The second exercise from last part we can do this as well: a[range(0,4),range(1,5)]

[@bhavpreetsingh1842]

shouldn't the two range functions be in square brackets so as to make them a list

[@robsonsilvadasilva]

@@bhavpreetsingh1842 Hello Bhavpreet. I think that is a good practice to use square brackets to read the function, but it`s not necessary. You can test and see that works :)

[@akshat2778]

Even i did the same way ✌️🤟

[@lbars]

Mine: np.hstack(a[0:4, 1:5])[0:19:5]

[@brettnelson7518]

a = Np.arrane([0, 4] [1,5]) is more efficient

[@justforwork5343]

ur tutorial IS AWESOME, plz do more man i also watched ur pandas too and it was as expectedly AWESOME tnx for the help man i appreciate it

[@PrakashRaj-md4wo]

56:00 b=[ ] for i in range(1,31): b.append(i) c=np.array(b) c=c.reshape(6,5) print(c)

[@PBJYM]

Thank you bro! This was an amazing tutorial!

[@mahbleh404]

one of the best numpy tutorial ever

[@frankservant5754]

Thanks bro you I have learnt a TON of stuff from your tutorials

[@anujdubey7324]

Just completed this tutorial. Thanks a lot for the content. Peace Out!!

[@maryanivanov961]

Thanks for this amazing course!!

[@nakjoonim]

Thank you so much for this amazing video!

[@RaynerGS]

Good job, way to go. Salute from Brazil.

[@minhduc8a21]

Thank you for the useful content. The very quick start with numpy.

[@arnavchauhan9637]

amazing lecture by amazing teacher⭐⭐⭐⭐⭐

[@artistpw]

Love. this. Truly great content and it was even nice to see the little faux pas because everyone has those!

[@marco.nascimento]

This is a great tutorial, thanks!!

[@Mushsayer]

Thank you very much for sharing the video. It was very helpful.

[@tempor8336]

Thank you dude ! That was great !

[@DJ-ct6so]

Excellent sir, very well explained !! Many thanks for uploading. 5 stars. ⭐⭐⭐⭐⭐

[@sankarmunirathinam115]

Awesome Keith, thank you for this great video

[@misketbey]

Very good job, it was very helpful to me, thank you!

[@seekthetruth383]

At 31:50 - more compact form: output = np.ones((5, 5), dtype='int8') output[1:4:1, 1:4:1], output[2, 2] = 0, 9 print(output)

[@PawanKumar-tu6ti]

Thanks a lot for this video!! much appreciated really !

[@prazzaldebnath5930]

Great tutorial completed full. Love from heart

[@Yo5463]

At the end, I indexed [2, 8, 14, 20] as np.delete(a[a%6 == 2], -1) to make use of the cool stair pattern

[@diananggreini5089]

Thank you for the video, its help me a lot to understand the concept and the function

[@easydatascience2508]

welcome to check my playlists also. I made most of the videos for Python and R. easy to follow.

[@erenjohn12345]

Thank you so much for this video. It helped a lot.

[@FacuBradaschia]

Excellent video. Thank you so much.

[@saifurrahman3961]

Thank You Very Much for teaching us this nicely

[@udyan_upal]

completed. thanks man! u r amazing

[@sahilkhandelwal8534]

Great video . God bless you and you keep making such great videos

[@thinhtruong6583]

thanks for making this video ! It's helpful !

[@huehuehue13]

Great video. LOVED IT!

[@aneeshkhandelwal3807]

Fantastic Tutorial !!!! Loved It !!!

[@epsilonator]

I did the matrix exercise a bit differently: arr = np.ones((5, 5)) arr[1:-1, 1:-1] = np.zeros((3, 3)) arr[2, 2] = 9

[@NinjaTxGaming]

Nice. I noticed, you can also just use 0 instead of np.zeros((3, 3))

[@andygordon6880]

Fantastic tutorial, thank you

[@nasser_omar]

Thanks a lot, man. You are amazing.

[@bhosalepranil16]

very good video for learning numpy every topic is covered very well.....

[@trisolation]

Great video. Thanks!

[@muhammadmuzammil2140]

Great video and awesome examples

[@arupabarua1721]

what a great tutorial!

[@mipmap256]

I'm not an expert, but I know numpy is fast is because of good cbinding. what running behind is c code. not because fix type. save memory

[@reluminopraha5948]

Even that c code could never be so fast should it handle complex data structure within each cell. Ie. both is critical.

[@spinipsFI]

Thanks for the tutorial! 👍

[@aliceinbath2850]

Thanks for this video!!

[@spider279]

Thanks you for this amazing video , great explaination

[@sanjanakatala4311]

thank you so much. It was very useful

[@rohankandra1928]

We can also solve the exercise at 33' using output = np.ones((5,5)) print(output) output[1:4,1:4]=0 print(output) output[2,2]=9 print(output)

[@foofoo17]

I solved it in the same way as you :)

[@negusuworku2375]

Thank you. Very helpful.

[@Minisynapse]

34:23 _"And then print out b_ _And as you can see_ _It's still one-two-three"_ Beautiful!

[@shrikantrane9601]

Great Tutorial .. can u upload the pandas, scikit learn also.. So we will get the complete basic ml package

[@rajdeepchakraborty7961]

Also matplotlib

[@cellphoneacademy5454]

Thanks for the awesome video!

[@enahpincer6233]

Thank you so much for this video :) :)

[@shamsmehdi3725]

very very helpful. thank you!

[@dev.regotube]

56:02 how to make the array: p1 = np.zeros((6,5), dtype='int8') x = 0 for i in range(6): for j in range(5): x+=1 p1[i,j] = x

[@lifeofsanjai]

50:00 test = np.genfromtxt('sample.txt', delimiter=',', dtype = 'int32') This too works

[@sanjr3079]

Thnx for these great lessons .😇

[@luism5822]

The first 11 minutes of the video have really useful info

[@bernardopalmer8846]

Great video! Just got confused on min 43:55, output 143 should be a sum, but rather we got an array.

[@gerdar]

thank you for this helpful tutorial!

[@ayushgupta1881]

Thanks a lot for this amazing video

[@TheGreatAnnouncer]

Thanks for everything big man