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License: GNU General Public License v3.0
Code and more for the QUB Development Weeks event 'High Performance Python'
License: GNU General Public License v3.0
Minimise custom ReactJS component use - but provide links for where to find out more
I was thinking we could all do parts for this, it's probably where we all have the most experience
Numpy data structures
Numpy mathsy operations
Numpy mathsy operations on numpy arrays
Numpy Random submodule (?)
Scipy stats sub-module (?)
Numpy&Scipy linalg sub-modules (?)
Split into several notebooks, 1 per demo / topic
add in custom C/C++ files and show how to use from python
Should touch on basic Flask, and refer to Plotly section
Just examples for lists, dicts, generators, nested ones
download this repo
install any libraries not already on QUB computers
start up Jupyter in the right directory
work for any operating system
work for any Python version
work on non-QUB computers
show joins / merges
show df.min, .max, .std, .describe, etc. (metadata info functions for columns)
[maybe] full worked example of data input, cleaning, preprocessing, and investigation.
Only looping over a list is shown currently, show loop over range() object, list, dict, list with key,val
make a website for setup & questions for the talk
4th example is inheriting from the wrong class and the function that is defined is not overwriting any function from the original first class that is currently inherited
Have them recreate the download -> install dependencies -> start Jupyter program
Weather Forecaster CLI (using darksky.net API)
File organiser/modifier CLI
Reddit/Twitter bot
Recap of PythonRefresher.ipynb one plus extras.
Silas, you've probably done more stuff with graphs than I have, could you take a look over the draft so far and annotate it to explain parts? Also feel free to add in / remove anything if you think it would be better.
Conor, would you be interested in doing this one? Just as you've some experience from the week in Allstate, we'll be keeping it pretty short - just a basic intro.
creating a dataframe, adding columns
operations on columns
df.head(), df.tail()
some basic .groupby() / .apply() stuff
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