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Here are three things that I see my non programming counterparts doing every single day. These really sum up so much of what folks do within an office. So many of us dabble in or become power users of spreadsheets without knowing there is an alternative out there that can save us time, automate boring things, and allow us to open up our minds for the part that we add value, Thinking about the data. Focus on Value Add Operations # Lets face it, stitching together spreadsheets is zero value add by itself, but if you can see something in the data and take action on it, this can be huge value add to your company. Learning just a bit of python will help focus more of your attention on “value add operations” and leave the mundane stuff to your computer. Merge a directory full of spreadsheets into one # I see this one all the time. One team gets a spreadsheet from another team once per month and they need to stich all the pieces together. Excel really opens the door for some nasty hidden bugs…

miniconda is a python distribution from continuum. It’s a slimmed-down version of their very popular anaconda distribution. It comes with its own environment manager and has eased the install process for many that do not have a way to compile c-extensions. It made it much easier to install the data science stack on windows a few years ago. These days windows are much better than it was back then at compiling c-extensions. I still like its environment manager, which installs to a global directory rather than a local directory for your project. Installing miniconda on Linux # Installing miniconda on Linux can be a bit tricky the first time you do it completely from the terminal. The following snippet will create a directory to install miniconda into, download the latest python 3 based install script for Linux 64 bit, run the install script, delete the install script, then add a conda initialize to your bash or zsh shell. After doing this you can restart your shell and conda will be ready…

This post was inspired by a comment I left on @dsteenman’s post. {% post dsteenman/how-long-should-a-blogpost-be-2k6n %} Most of the time I prefer short as I am more likely to read the whole thing. If its setup as a series I am more likely to work my way through the whole series in a matter of a few sessions. Just my preference I will say though there are certain articles that fit well to the long format. They are articles that folks tend to come back to often as a reference again and again. Sections # layout is key Break it up Article types superpost single post series discussion Post what you want to read layout is key # Either way, you go layout is key. You are not Steven King, no matter how great of a writer you are, you are unlikely to hold attention like he can. Most folks reading blogs scan articles first. I often scan, then read. If the article is really good or pertains well to me I will read everything, otherwise, I go back and read only the sections of interest. If there are…

I was particularly inspired by @chantastic episode 103 of the react podcast with @ParissAthena. They spoke about the black tech pipeline as well as Diversity, Equity, and Inclusion. Pariss is quite an inspiration. She has done so much work to create a better place for POC in tech. I like that not only is she helping them get jobs but acting as a mentor for their first few months on the job to make sure that they are able to find their place and fit in. Based on an episode of react podcast. 🎙 Listen to the full episode. So Inspirational # I was particularly inspired by @chantastic episode 103 of the react podcast with @ParissAthena. They spoke about the black tech pipeline as well as Diversity, Equity, and Inclusion. Pariss is quite an inspiration. She has done so much work to create a better place for POC in tech. I like that not only is she helping them get jobs but acting as a mentor for their first few months on the job to make sure that they are able to find their place and fit in.…

It’s a really cool GitHub action that will automatically commit files changed during the action. I was using this to render a new readme based on a template. Check out the repo for git-auto-commit-action. It’s a really cool GitHub action that will automatically commit files changed during the action. I was using this to render a new readme based on a template. This has been by far the easiest way to commit back to a repo that I have seen. Other patterns often require fully setting up the git user and everything. While it’s not all that hard, this action already has all of that covered. You must give it a commit message and thats it. Optionally you can configure a number of things. Its possible to configure the,, and. I often also scope the to a certain subset of files. Getting Started with GitHub Actions Github actions are written in configuration files using the YAML syntax. YAML is a superset of JSON. Most YAML can be expressed inline with JSON syntax.... Mar 16, 2020 If you’re new t…

If we take a look at the release notes I see one major feature improvement on the list, auto-discovery of hooks. This one comes a bit surprising as it was just casually mentioned in #435 Think pytest # As mentioned in #435 this is the model that pytest uses. Not all plugins automatically start doing things right out of the box but require a CLI argument. simplicity # It feels a bit crazy that simply installing a package will change the way that your pipeline gets executed. I do like that it requires just a bit less reaching into the framework stuff for the average user. Most folks will be able to write in the catalog and nodes without much change to the rest of the project. Implementation # Reading through the docs, they show us that we can make our hooks automatically register by adding a endpoint that points to a singleton instance of our hook. from the docs Careful with the singletons # hook authors beware I will be a bit cautious before installing a plugin that is automatically reg…

Caution None of the testproject.io urls resolve anymore in JAN 2025, I removed all of the broken links. As I continue to build out waylonwalker.com I sometimes run into some errors that are not caught because I do not have good testing implemented. I want to explore some integration testing options using GitHub’s actions. Running integration tests will not prevent bugs from happening completely, but it will allow me to quickly spot them and rollback. 🤔 What to test first? # The very first thing that comes to my mind is anything that is loaded or ran client-side. Two things quickly came to mind here. I run gatsby so most of my content is statically rendered, and it yells at me if something isn’t as expected. For performance reasons I lazy load cards on my blogroll, loading all of the header images gets heavy and kills lighthouse (if anyone actually cares). I am also loading some information from the top open-source libraries that I have created. To prevent the need to rebuild the whole…

Today I setup a new machine on Digital Ocean to use with TestProject.io, Here are my installation notes. envsubst <.github/ci/docker-compose.yml > docker-compose.yml

When learning a new skill it’s important to practice along the way. In order for me to show up to practice I need to make it easy to show up. An easy way to show up to practice with python is to use an online repl. With these you can try out something quick. Sometimes I see snippets from blogs or tweets and I need to try the out for myself to really understand. When learning a new skill it’s important to practice along the way. In order for me to show up to practice I need to make it easy to show up. An easy way to show up to practice with python is to use an online repl. With these, you can try out something quick. Sometimes I see snippets from blogs or tweets and I need to try them out for myself to really understand. Three online REPLS # Here are three different options that I have used in the past to try out something at some various levels. I am sure there are plenty more, but these are three that I have tried. I am not covering all of them, because It’s been a while since I have…

I am exploring a kedro catalog meta data hook, these are some notes about what I am thinking. Process # metadata will be attached to the dataset object under a attribute metadata will be updated metadata will be empty until a pipeline is ran with the hook on optionally a function to add metadata will be added metadata will be stored in a file next to the meta Problems This Hook Should solve # what datasets have a columns with in the name what datasets were updated after last tuesday which pipeline node created this dataset how many rows are in this dataset (without reloading all datasets) implementation details # metadata will be attached to each dataset as a dictionary list/dict comprehensions can be used to make queries Metadata to Capture # try pandas method -> try spark -> try dict/list -> none column names length Null count created_by node name Database? # Is there an easy way to create a nosql database in memory from a a list of dictionaries? list-dict-DB dataset TinyDB