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Amazing explanation!

You are amazing!!

2024 - now all dictionaries are ordered in Python by default .

Thanks a lot sir . Helped me a lot

You are doing a great job. My concept of KDE is much more clear.

Thank you for this video. It helped.

Thank you so much for your easy-to-understand explanation! I want to make sure one thing, if I have 5 variables (5 columns of data), does that mean I plot my data in 5 axes? So when I compute MD, the vector (x and m) will contain 5 dimensions? Thank you in advance!

What a perfect indisn accent :)

Hii sir, this pandas series so good and clear... just to clarify the challenge section in this video at last shouldt we put axis='rows' for apply since check the qcut for each column?

I have watched about 15 beginner videos on Cython and yours is the best so far. It starts by showing the 2x improvements with no code change. This was excellent. No other video event mentioned this. Then the step by step instructions to add cdef and cpdef was a great explanation for a beginner. And the table to show the list of C data types is very useful. This is also the firstt video that shows how to update setup.py with multiple pyx file. All other videos only have 1 pyx file. Teaching is really an art. Thank you!

Very well done. Intuitive explanation that can arm practitioners with enough knowledge to apply this in real life.

please remove the thumbnail feature at the end of your videos. what's the use of it if last 20 seconds content if we are not able to see it.

please remove the thumbnail feature at the end of your videos. what's the use of it if last 20 seconds content are not able to seen.

jesus christ bless you

jesus christ bless you

Excellent and clear cut explanation...

Great explanation!

Yes, cython will let you interact with code libraries written in C. If you do 99% of your work in those C libraries, not in Python; you will get a good increase in speed. Of course you may need a lot of Python code to make sure your data is in a format compatible with those C libraries, and then more code to convert the C results back into Python format ... Why not simply program in C ? you can easily create dictionaries and sets -- these are just hash tables you can easily create tuples, linked lists or trees -- these are just just a "struct" maybe you feel that the python for i in range(n): ... is SO much cleaner than the C for (i=0; i<n; i++) {... maybe you can't endure life without this python loop: for fruit in fruits : ... maybe you simply can't comprehend this C code: startFruitLoop(); while (fruit = nextFruit()) { ... or maybe this C code is just morally offensive for (count=0; count < numFruits; count++) { fruit = fruits[i]; ... maybe you just H A T E all those braces "{}" and semicolons ";" Learn C ... for no other reason than: it looks good on your resume. But you may be surprised. C is a good programming language. If you ever need something to execute QUICKLY; C is a great choice.

useful tutorial. Thanks )

Could I get the DataSQL.csv file that you runned earlier in your class.?

❤ 😊

One of the best explanations out here! Thankyou

Wow best tuto ever, would it work and actually provide improvement on front end part like tkinter?

Very useful and clear explanations on these concepts, thank you

Gemini 1.5 Pro: This video is about how to convert Python code to Cython and achieve significant speed improvements. The video starts by explaining what Cython is and how it works. Cython is a language that allows you to write Python code with C-like syntax. This means that you can take advantage of the speed of C while still being able to write code in a more readable Python style. The video then goes through a step-by-step process of how to convert a Python function to Cython. The process involves creating a new file with a .pyx extension and pasting the Python code into it. Then, you need to use the `scython` library to compile the .pyx file into a C extension module. Once the C extension module is created, you can import it into your Python code and use the Cython function just like any other Python function. The video also shows how to improve the performance of your Cython code by using decorators. Decorators are a special type of function that can be used to modify the behavior of other functions. In Cython, there are a number of decorators that can be used to optimize code for speed. For example, the `@cython.nogil` decorator can be used to tell Cython that the function does not need to acquire the Python Global Interpreter Lock (GIL). This can improve the performance of the function by allowing it to run concurrently with other Python threads. Overall, this video is a great resource for anyone who wants to learn how to speed up their Python code using Cython. By following the steps outlined in the video, you can achieve significant performance improvements without having to rewrite your code in C. Here are the key points covered in the video: * Cython is a language that allows you to write Python code with C-like syntax. * Cython code can be significantly faster than pure Python code. * The process of converting Python code to Cython involves creating a .pyx file and compiling it into a C extension module. * Cython decorators can be used to further improve the performance of Cython code.

Please can you share the solution for challenge in comment section.

helped me a lot, keep up he good work!

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Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course

Course materials Download: github.com/machinelearningplus/pandas_course