Archive

It’s been a long time coming. We use some very harsh language within tech so much sometimes that we become numb to it. It’s time to do my very small part in this movement and purge this language from my active repos starting with this blog right here.

Large Refactor At The Command Line

this post follows my method of refactoring code bases from the command line, read more about that in this article.

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I just did a quick refactoring of my JAMStack blog urls. Some didn’t fit with my style, some had _ that I wanted to switch to -, and others were ridiculously long. I’ve been using forestry as my CMS, I write many of my posts there, and sometimes it picks some crazy file names (based on my titles). It was time to refactor.

Large Refactor At The Command Line

When refactorings similar to this get really big I often need to do some project wide find an replace, I usually do this right from the command line.

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Python *args and **kwargs are super useful tools, that when used properly can make you code much simpler and easier to maintain. Large manual conversions from a dataset to function arguments can be packed and unpacked into lists or dictionaries. Beware though, this power can lead to some really unreadable/unusable code if done wrong.

Python *args and **kwargs are super useful tools, that when used properly can make you code much simpler and easier to maintain. Large manual conversions from a dataset to function arguments can be packed and unpacked into lists or dictionaries. Beware though, this power can lead to some really unreadable/unusable code if done wrong.

*args are some magical syntax that will collect function arguments into a list, or unpack a list into individual arguments.

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I recently discovered the ✨ awesomeness that is pre-commit. I steered away from it for so long because it seemed like a big daunting thing to set up, but really it’s easy. It will automatically run checks for you. In some cases, it will even automatically fix them for you. Out of the box, it will do things like automatically trim extra whitespace, fix file endings, and ensure file sizes are not too large for git.

I recently discovered the ✨ awesomeness that is pre-commit. I steered away from it for so long because it seemed like a big daunting thing to set up, but really it’s easy. It will automatically run checks for you. In some cases, it will even automatically fix them for you. Out of the box, it will do things like automatically trim extra whitespace, fix file endings, and ensure file sizes are not too large for git.

It comes with a sample-config that is pretty general purpose and use for just about any project in git.

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Follow along the Journey as I build out kedro.dev.

I have really enjoyed my own personal journey as I have started to build all of my data pipeline projects with the kedro framework. I want to start building a place to share resources with the community. I want to see this community grow and flourish. They say in front end web development if you are not using a framework you end up building one. That’s exactly what I was doing before I started using kedro. I want to build out a set of resources that this community can learn from and start to use the framework at their own pace without needing to develop their own from scratch.

Looking into the front end frameworks to see how they welcome their community. Much of my inspiration is from them, bringing lessons learned to data.

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kedro-static-viz is out with support for the newly released hooks feature. This means that you can have kedro-static-viz automatically deploy a full gatsby site before_pipeline_run keeping your visualization always up to date.

Even though it is a static site there is no functionality lost. The only thing that’s missing is the flask server. With kedro-static-viz you can deploy your visualization to a number of static hosting providers such as GitHub pages free of charge with wicked fast performance

Even though it’s built on gatsbyjs the full site builds in under 2s even on slower hardware. This is because the site is already pre-rendered and stripped of any excess. It’s zipped up right into the python package and is typically used with the cli, but now can be used with python, or as a hook as well.

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There are two main ways to create kedro hooks, with modules and classes. Each one still uses the same verbiage as the function/method names.

Class hooks seem a bit special as they give you a way to configure them so that they are a bit more generally useful.

What is Kedro

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This post is a 🧠 branstorming work in progress. I will likely use it as a storage location/brain dump of hook ideas.

If you are completely unsure what kedro is be sure to check out my what is kedro post

hooks are executed in reverse order of the hooks list.

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I want to incorporate some of the wonderful comments, \U0001F495, \U0001F984, and \U0001F516’s that I have been getting on dev.to on my website. I have dabbled once or twice with no avail this time I am taking notes on my journey, so follow along and let’s get there together. By the end of this post, I will have a way to get comments from posts on the client-side thanks to the wonderfully open dev.to API.

I want to incorporate some of the wonderful comments, 💕, 🦄, and 🔖’s that I have been getting on dev.to on my website. I have dabbled once or twice with no avail this time I am taking notes on my journey, so follow along and let’s get there together. By the end of this post, I will have a way to get comments from posts on the client-side thanks to the wonderfully open dev.to API.

dev.to has an open API that allows us to easily get comments as HTML. They have their API hosted at https://docs.forem.com/api/#tag/comments, let’s take a look at it.

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GitHub’s actions are a new GitHub feature that will trigger GitHub to spin up a virtual machine and run some tasks with some special access to your repo. It can interact with comments/issues, it can clone your repo, You can explicitly pass in secrets so that it can commit back to the repo or deploy to another service. The environment may be a Linux, windows, or even a mac machine. I believe this is wildly incredible for the open-source community, putting these tools in the same place that we are already collaborating is so convenient.

GitHub actions can give you confidence that your site is up and running, with the latest JavaScript packages, does not have broken links, and can even take screenshots of what your website looks like on different screen sizes and operating systems.

srt32/uptime is an action that you can run on any public website. I run this one several times every day and it gives me confidence that my various sites are still up and running. It ensures that my build didn’t break something, nothing is wrong with my hosting provider, or my DNS.

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stack overflow link

Kedro provides an efficient way to build out data catalogs with their yaml api. It allows you to be very declaritive about loading and saving your data. For the most part you just need to tell Kedro what connector to use and its filepath. When running Kedro takes care of all of the read/write, you just reference the catalog key.

Under the hood there is an AbstractDataSet that each connector inherits from. It sets up a lot of the behind the scenes structure for us so that we dont have to. For the most part kedro has connectors for about anything that you want to load, csv, parquet, sql, json, from about anywhere, http, s3, localfile system are just some of the examples.

Here is a DataSet implementation from their docs. Here you can see the barebones example straight from the docs. Parameters from the yaml catalog will get passed in

As usual while listening to python bytes 181 I heard of a tool that I had to try out right away!

This thing is 🔥 hot off the press folks, we’re talking the first release only 3 weeks ago. Its something that the python community needed years ago, and it belongs in your CI today. I had tried several tools that tried to do docstring coverage in the past but they were a bit cumbersome and were quickly forgotten about. Not interrogate, its dead simple!

Nothing I have tried has come close to being this good

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When creating cli’s I often want some nice full-width character. I find it tough to find them, and when I do half the time it is an image or something that cannot be copied 👿.

I rarely get very complex with my semi-manual ASCII art. I can do 98% of what I need with bars and corners. Using some simple full-width characters can really give your cli a nice clean look.

I’d say 50% of what I need is just a full-width horizontal bar to give some visual flair or separation.

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Kedro Hooks Intro - kedro hooks are an exciting upcoming feature of kedro 0.16.0. They allow you to hook into catalog_created,pipeline_run, and node_run(nouns). With a before, or after (adjective). This really reminds me of reacts lifecycle hooks, that let you hook into various state of react web components. This is going to make kedro so extendable by the community. I am super pumped to see what the community is able to do with this ability.

kedro hooks are an exciting upcoming feature of kedro 0.16.0. They allow you to hook into catalog_created,pipeline_run, and node_run(nouns). With a before, or after (adjective). This really reminds me of reacts lifecycle hooks, that let you hook into various state of react web components. This is going to make kedro so extendable by the community. I am super pumped to see what the community is able to do with this ability.

What is Kedro

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This is a very rough idea for a kedro package to prevent time lost to get partway through a pipeline run only to realize that you dont have access to data or resources.

find-kedro is a small library to enhance your kedro experience. It looks through your modules to find kedro pipelines, nodes, and iterables (lists, sets, tuples) of nodes. It then assembles them into a dictionary of pipelines, each module will create a separate pipeline, and __default__ being a combination of all pipelines. This format is compatible with the kedro _create_pipelines format.

kedro is a ✨ fantastic project that allows for super-fast prototyping of data pipelines, while yielding production-ready pipelines. find-kedro enhances this experience by adding a pytest like node/pipeline discovery eliminating the need to bubble up pipelines through modules.

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Often when reading through javascript examples you will find some arrow functions use parentheses () while others use braces {}. This key difference is that parentheses will implicitly return the last statement while braces require an explicit return statement. It is important to understand the difference between them because it is likely that you will find code examples of both and trying to edit code written differently than you’re used to may have unintended consequences.

Arrow functions are one-liner functions in javascript that have two main syntactical ways to create the code block. with parentheses and braces. Let’s take a look at both ways of creating arrow functions so that when we come accross them in the wild it will all make sense.

Here is an example of an arrow function that will implicitly return the last statement without the return keyword. I believe that these are a bit more restricted in that you cannot set variables inside them. They are a little bit more concise and great for one-liners.

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Inspired by Chris Achard

Email course inspired by learn d3 in 5 days

This past week I had a really weird bug in my kedro pipeline. For some reason data running through my pipeline was coming out completely made no sense, but if I manually request raw data outside of the pipeline it matched expectations.

NOTE While this story is about a kedro pipeline, it can be applied anywhere closures are put into an iterable.

After a few days of looking at it off and on, I pinpointed that it was all the way down in the raw layer. Right as data is coming off of the database. For this I already had existing sql files stored and a read_sql function to get the data so I opted to just set up the pipeline to utilize the existing code as much as possible, leaning on the kedro framework a bit less.

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Is it possible to deep watch a single python function for changes?

keeping track of a python functions hash is quite simple. There is a__hash__ method attached to every python function. Calling it will return a hash of the function. If the function changes the hash will change.

[ins] In [1]: def test(): ...: return "hello" [ins] In [2]: test.__hash__() Out[2]: 8760526380347 [ins] In [3]: test.__hash__() Out[3]: 8760526380347 [ins] In [4]: def test(): ...: return "hello world" [ins] In [5]: test.__hash__() Out[5]: 8760525617988 [ins] In [6]: def test(): ...: return "hello" [ins] In [7]: test.__hash__() Out[7]: 8760526380491

Using hashlib provides a consistent hash.

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If you are developing python packages and using GitHub here are four actions that you can use today to automate your release workflow. Since python tools generally have such a simple cli I have opted to use the cli for most of these, that way I know exactly what is happening and have more control over it if I need.

If you are developing python packages and using GitHub here are four actions that you can use today to automate your release workflow. Since python tools generally have such a simple cli I have opted to use the cli for most of these, that way I know exactly what is happening and have more control over it if I need.

flake8 is pythons quintessential linting tool to ensure that your code is up to the standards that you have set for the project, and to help prevent hidden bugs. I am a heavy user of black and isort as well, but for ci flake8 is typically considered the gold standard. black and isort will help you automate many fixes suggested by flake8.

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So often I see a variables type() inside of its name and it hurts me a little inside. Tell me I’m right or prove me wrong below.

Pandas DataFrames are probably the worst offender that I see

# bad sales_df = get_sales() # good sales = get_sales()

Sometimes vanilla structures too!

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