Python NumPy Tutorial for Beginners
Vložit
- čas přidán 6. 08. 2019
- 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 CZcams 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...
--
Learn to code for free and get a developer job: www.freecodecamp.org
Read hundreds of articles on programming: www.freecodecamp.org/news
⭐️ 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?)
Why?
thanks bhai
+
@@yahyafati u were dumb or something'
@gokul8747 is the hero of this comment section
1.25 speed is perfect, thanks for the video
thanks for tips
I'm on 2.5
Thx bro
Yup
2x speed is better. Saves alot of time.
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.
how is the progress?
Seriously, side-by-side comparisons are the BEST !! As visual as it can get ! 🙏
This was a phenomenal overview of numpy. I feel confident that I can tackle more advanced topics now!
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...
Well done. Quick ,short & straight to the point!
This is the first tutorial that I actually finished. Thank you, Keith!
One of the finest Numpy tutorials. Keep up the great work guys!
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
This is absolutely great content! Thank you so much for doing this!
Absolute clarity and upto speed. Very comprehensive coverage.
Thats the most english I have heard all day
@@63khushalsolanki9 lol
@@63khushalsolanki9 real
Watching this at 2x speed so I can learn Numpy in 29 minutes instead of 58 minutes.
i have installed video controller extension, i am watching at 2.5x
@@krrishkataria560Just don't watch the video and read the specific documentation. It will be even faster if you have skill.
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 :)
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
This guy is smart and he makes this stuff really interesting !!! I like it !!!
Much better than courses that I've paid good money for - Top Man Galli
Amazing! Thank you for the explanation dude. It is really helping me with a certification course that I’m taking now
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
Really well put together, thanks! :)
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
Thanks so much Keith, for the very educating tutorial. Quite explanatory
This video improved my numpy information. So thanks everybody who contributed.
Thank you very much sir... the course is crystal clear... thank you
Thanks for your effort and the good stuff. Effective introductory! Thanks
Excellent pace and explanations -- thank you!
excellent tutorial. feeling comfortable with numpy now thanks to you :)
Nice mate! What a wonderful review from all the possible uses of Numpy. Thanks a lot!
Thanks for the free class! I'm just learning programming :) I felt very motivated after I could make the array on Problem #1
learning as well, would u like a study budy?
Thank you! The only thing was a little bit complicated to me is working with axis. None the less, great tutorial!
رحؤنشضهكبءخؤذمء ء يددحمس
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!
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!
Thanks you Keith , great video (also subscribed to your channel). Also thanks to FCC , love you for your service!
Awesome Tutorial. Thank you very much, Keith !
Thank you bro! This was an amazing tutorial!
Thank You for clearing my concepts on NumPy library.
Really amazing introduction to numpy, it helps a lot
Thank you man!
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.
Why tho?
This is a great tutorial, thanks!!
Thank you so much for this amazing video!
Thank you Keith for this awesome tutorial!
one of the best numpy tutorial ever
Best crash course on Numpy ! Thank you for your interesting videos
Thank you dude ! That was great !
The second exercise from last part we can do this as well: a[range(0,4),range(1,5)]
shouldn't the two range functions be in square brackets so as to make them a list
@@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 :)
Even i did the same way ✌️🤟
Mine: np.hstack(a[0:4, 1:5])[0:19:5]
a = Np.arrane([0, 4] [1,5]) is more efficient
Thanks bro you I have learnt a TON of stuff from your tutorials
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.
Good job, way to go. Salute from Brazil.
Thanks a lot for this video!! much appreciated really !
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
Thank you very much for sharing the video. It was very helpful.
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.
Really useful video! Been using Pandas for a couple years but learning Numpy is showing me why Pandas does the things it does.
Very good job, it was very helpful to me, thank you!
what a descriptive video on numpy 👍👍👍
Just completed this tutorial. Thanks a lot for the content. Peace Out!!
Great tutorial completed full. Love from heart
Thank you for uploading this.
thanks for making this video ! It's helpful !
Love. this. Truly great content and it was even nice to see the little faux pas because everyone has those!
Thank You Very Much for teaching us this nicely
Thanks a lot, man. You are amazing.
Thank you for the useful content. The very quick start with numpy.
Thanks for this amazing course!!
Thank you so much for this video. It helped a lot.
Great video . God bless you and you keep making such great videos
Excellent video. Thank you so much.
Awesome Keith, thank you for this great video
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
Nice. I noticed, you can also just use 0 instead of np.zeros((3, 3))
Great video. LOVED IT!
Great Tutorial .. can u upload the pandas, scikit learn also.. So we will get the complete basic ml package
Also matplotlib
Thank you for the video, its help me a lot to understand the concept and the function
welcome to check my playlists also. I made most of the videos for Python and R. easy to follow.
Excellent sir, very well explained !! Many thanks for uploading. 5 stars. ⭐⭐⭐⭐⭐
Great video and awesome examples
completed. thanks man! u r amazing
Thank you so much for this video :) :)
Great video.
Thanks!
The first 11 minutes of the video have really useful info
Fantastic Tutorial !!!!
Loved It !!!
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
Thank You! 😊
very good video for learning numpy every topic is covered very well.....
Thnx for these great lessons
.😇
Thanks you for this amazing video , great explaination
very very helpful. thank you!
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
Even that c code could never be so fast should it handle complex data structure within each cell. Ie. both is critical.
Thank you. Very helpful.
Fantastic tutorial, thank you
Thanks for the awesome video!
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)
I solved it in the same way as you :)
Thanks for the tutorial! 👍
What happened to the live stream, I can't focus anymore!!!
thank you so much. It was very useful
thank you for this helpful tutorial!
Very good! Thanks a lot :)
Thanks for everything big man
Thank you so much for your time
That was PRETTY amazing. Thankyou. But the most interesting thing was that I was the exact 3k'th like because I liked and unliked the video several times to confirm it.
56:00
b=[ ]
for i in range(1,31):
b.append(i)
c=np.array(b)
c=c.reshape(6,5)
print(c)
Awesome work dude.
love from India