Python for Data Analysis: Numpy Arrays

This video covers the basics of numpy arrays in Python.

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This is lesson 8 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Numpy Arrays

Additional linear algebra functions for numpy:

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:

Link to the Python for Data Analysis written guide index page:

[rain]

Thank you so much, almost caught up with the lessons. I've been loving these videos, keep them coming!

[kevindelosreyes]

I'm happy to have found your channel. Thank you for sharing this!

[Pythagoras]

I made the mistake of not watching the Python Basics playlist first and ended up learning the basics from a different Youtube channel. Now that I have a better understanding of object oriented programming, it is much easier to absorb all of the great information in this playlist.

[iusehumourasadefencemechan]

Very well done. Thank you so much for the video.

[hallielam]

thank you. I hope you keep making videos!

[pickavana]

in some of your output of arrays you are showing commas between numbers. I am not seeing this??

[euzlrue]

np.reshape(a = two_d_array, newshape = (6, 3))
What is the function of the "a =" in this block of code?

[dungnguyentri]

In Array Indexing and Slicing section, # Create a new 2d array
I can run it fine on kaggle but in Python it showed the TypeError: can only concatenate list (not "int") to list
So i tried to put 6 and 12 in [] then added: dtype=object
The output is
[list([1, 2, 3, 4, 5, 6]) list([1, 2, 3, 4, 5, 6, 6])
list([1, 2, 3, 4, 5, 6, 12])]

How to fix this?

[writerconnect]

i have genuinely had a difficult time following this one specific module. so i will skip this and move to the next. perhaps i need a more elementary understanding on numpy arrays