Drawing from data
@DataDrawing
Data exploration and visualization with Python, pandas, seaborn, numpy and matplotlib
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Here's an idea that I bet many people have had given the recent meme stocks excitement - building a trading strategy by analysing social media posts: buff.ly/3fdMB5B
Another tool that aims to make it easier to scrape data from well defined APIs buff.ly/3qW8ERa Maybe a good way to get hold of example data for practising!
I have a soft spot for tools that allow one to stay on the command line as much as possible. In that spirit, colorpedia buff.ly/39vZOU9 allows you to look up colours and palettes - the palette output I think is particularly cool.
A nice collection of pandas challenges, with answers, on a real-life parking dataset buff.ly/391W2RM
datareader makes it straightforward to get financial data into pandas dataframes for analysis. Several great clear examples here buff.ly/3tjkfe7
A good summary buff.ly/2Nz6qss here of the different kinds of memory problems in Python. See chapter 16 and video 2 in the Drawing from Data book buff.ly/2OBa2Lf for details of how to avoid them!
A great example of why Python is so useful for automating stuff - using existing libraries for interacting with a spreadsheet program (Excel) and an email client (Outlook) to send customized reports buff.ly/3iKAywE
One of the most important abilities of experienced programmers is a willingness to reshape the data to suit the question at hand. Here's an article I just wrote with a specific pandas example buff.ly/3rMtrXz
A round up of a few GUIs that work with pandas buff.ly/3oO6GBz. I'm undecided on the usefulness of these; seems like a lot of control to give up in exchange for writing slightly less code, assuming that we're inevitably going to bring in a charting library eventually.
Two new versions of Very Important Packages that are probably worth taking a quick look at: JuptyerLab 3.0 buff.ly/3rXu5CC and scipy 1.6 buff.ly/3rhiuxc
I bet one could write an article like this (switching from Excel to Python buff.ly/3hu3kAX) as applied to many different fields.
Not sure whether I cautiously like or vehemently dislike this idea: a package that draws charts from dataframes by guessing what you're looking for buff.ly/2YvRrle
This is not data science related, but kind of a fun write up: building a minimal Python command line text from scratch buff.ly/3ns1y4A
New article up: tracking down a massive increase in memory usage when concatenating dataframes buff.ly/3amx9Qz
Cerberus is a library for doing data validation buff.ly/2JUmMKE Another nice example of not-reinventing-the-wheel, and an illustration of how declarative programming can make things easier.
There's something appealingly meta about using the tools of data science to investigate.... the tools of data science. Like this analysis of ~10M Jupyter notebooks buff.ly/3munTOD
This is pretty cool - a Python package for building interaction question/answer based command line interfaces buff.ly/34NCHSk I could see this being useful to deploy e.g. datavis tools to non-programmers.
Though Python is slower than some other languages, in many scenarios that doesn't matter because either (a) it's fast enough or (b) some other component in the workflow (disk IO, database, API) is even slower. buff.ly/34Fs3gj (use private window if you get a sign in wall)
Recursion and trees are intimately linked in programming; if you find yourself using recursion in data science then it's probably for dealing with treelike data. So it makes perfect sense to draw a tree to visualize it buff.ly/3h75Qgf
Very cool lightning demo: tabular data exploration in the terminal with visidata buff.ly/39e4HQi
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