# Lecture 3 - Numerical Computing with Python & Numpy

Lecture 3: Numerical Computing with Numpy

Topics covered:

• Going from Python lists to Numpy arrays
• Working with multi-dimensional arrays
• Array operations, slicing and broadcasting
• Working with CSV data files

Resources

Please use this thread for asking questions. Upvote questions by pressing the “Like” (heart) button. We will answer 5-10 highest upvoted questions and the rest will be answered by the community and course team. If you see a question you know the answer to, please add an answer to help others.

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Who’s here, give me a like!

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can we get some practice exercise on lecture 2

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Can you provide a ppt or pdf after every lecture, video pura dekhne me dikkat aata hain

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Jupyter Notebooks are already being shared with us, you know!

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not possible to highlight important point, my system takes lots of time to open the notebook and run

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The video is being recorded. You can watch it anytime. Wait for the lecture to complete. Observe the details he is teaching.
It may be useful to understand the concept first then hands-on-practice.

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Okay, for that you can simply convert the notebooks to .pdf format.
And open .pdf files in acrobat and highlight it in it

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what is difference between

result += (…)
and
result= result+(…)

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why it is 64? maximum 64 bits acceptable ?
~ regarding int(64) and float (64)

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Both of the two statements are same.

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What is the difference between dot product and matmul() function…?

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previously mentioned .apend not valid for tuple , then np.concatenate overrid the problem ?

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Can we get some other txt file for practice

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You can search over the internet. There are plenty of such files available for free.
Or, you can create one on your own. That will be fun!

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Can you show some matrix operations ? like eigen value,
cannonical representations,
‘trace of matrix’;
‘element manipulation’
‘matrix diagonalization’ etc. ?

once I use them at MATLAB editor. Also give some examples of ploting of graphs with different legends and "colour"