Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping)

This video overviews the NumPy library. 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. A full video timeline can be found in the comments.

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Videos of mine that use NumPy

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Links with more information!

NumPy vs Lists:

Indexing:

Array Creation Routines:

Math Routines Docs:

Linear Algebra Docs:

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Video Timeline!
0:00 - Introduction
1:15 - What is NumPy
1:35 - NumPy vs Lists (speed, functionality)
9:17 - 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?)

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[KeithGalli]

I created a second channel with more Python content, check it out!😊

[Noartisthere]

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.

[leaddiet]

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

[maganaluis]

I've been using Numpy for 3 years and this is quite informative for me. Pay attention guys.

[KeithGalli]

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

[adt]

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!

[smolboyi]

I love the density of data given in this video and the inclusion of mistakes in the code writing, thank you greatly!

[GreyBandanna]

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

[HazemAzim]

Probably the best explaining NumPy ! Thank you

[zanad]

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.

[Boringpenguin]

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

[willingwinning]

Amazing video! Thank you so much. I'm 37 and have started tinkering with coding and python. It's so much fun!

[millionwolves]

No one has ever kept it this simple and straight. You saved me a lot of time. Keep up the good work !

[suryamgupta]

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!

[Arkssa]

Nice job doing an intro explaining why NumPy is so powerful before heading to the how-tos

[cyberloh]

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

[SimonYells]

I'm halfway through the video and so far so good. Nice tutorial !

[hellothere]

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 !

[xyzzyx]

After searching a lot, finally I found a great tutorial on numpy with everything I want to learn. Thanks a lot sir.

[martintoilet]

Short but informative, also showed how powerful NumPy is.