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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...

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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

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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...

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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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Here is one useful thing that you can do with GitHub actions no matter what language you use, send email. You might want to know right away when your ci passes. You might want to give your team a nice pat on the back when a new release is deployed. There might be subscribers wanting to see the latest release notes in their inbox as soon as the latest version is deployed. Whatever it is, its pretty easy to do with an action right out of the actions marketplace.

Here is a silly example that sends an email to yourself anytime someone stars your repo.

GitHub actions are an amazing tool that allows us to run code based on triggers inside of our repo. Their is a large and growing community of actions inside the marketplace to use with very little effort. Best of all they are free for public repositories, and private repos have a very generous free tier.

I have been diving deep into Github actions for about a month now and they are wicked good! They allow you to run any sort of arbitrary code based on events in your repo, webhooks, or schedules. They are very reasonably priced. The interface that GitHub hs developed for them is top-notch! It’s so good I have done 90% of my editing of them right from github.com.

some interaction to your repository triggers code to run.

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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. Similar to python YAML is whitespace driven by whitespace rather than brackets tags. The argument for using YAML for configuration files such as actions is that it is more human-readable and editable. It’s much easier to see the whitespace layout than it is to get closing brackets correct. For actions, I believe this is mostly true. I don’t see any use case to get past 3-5 indents, which is completely manageable.

Can I just say that I learned more than I realized about YAML by writing this article

In YAML or JSON, the most basic containers for data are arrays, a 1D list of things, and objects, for key-value pairs.

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Today I learned how to diff between two branches.

git diff feature..main

Sometimes we get a little git add . && git commit -m "WIP" happy and mistakenly commit something that we just can’t figure out. This is a good way to figure out what the heck has changed on the current branch compared to any other branch.

Let’s create a new directory, initialize git and toss some content into a readme.

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This is a quickstart to getting a new kedro pipeline up and running. After this article you should be able to understand how to get started with kedro. You can learn more about this Hello World Example in the docs

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You dont have to start out as a git wizard with the cleanest possible commit history. At first dont let yourself get too wrapped up in it, the most important part is that you make commits. You will find needs for more advanced stuff later.

git add . git commit -m "FEAT added new function to calculate revenue by product family" git push

Get comfortable with this, then learn how to branch, rebase, stash, etc…

Get the job done. Keep it in small bite size pieces. Make readable function definitions and variable names. You will thank yourself for naming things well later. Readability counts more than performance in most cases of data science. If it gets the job done try not to over worry about things like performance. A few extra seconds to clean a dataset or build a model is not worth hours of your time. As you go you will have cases that performance is more critical and you will learn what to do from the start to avoid...

I am working through Wes Bos’s beginnerjavascript.com/ I just hit module 18 on hoisting. It’s something that I always knew was there, Its not something I typically see used or use myself.

Do you have any use cases that you use hoising? Why? It seems like a really cool feature in any language that uses it, but I dont really notice it in use.

There are many articles that cover this in far more depth, but its the idea that variable declarations and functions are defined before they are executed. This means that it doesnt matter if you call a function before or after it is defined.

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What is Kedro

This is my original what-is-kedro article. There is a brand new one

Kedro is an open source data pipeline framework. It provides guardrails to set your project up right from the start without needing to know deeply how to setup your own python library for data pipelining. It includes really great ways to manipulate catalogs and pipelines. This article will cover the 10K view of kedro, future articles will dive deper into each one.

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Getting a custom scrollbar on your site makes it stand out a bit compared to the very plain stock one that are on most sites. This is how I set mine up on my gatsby site.

Inspired by Wes Bos’s new uses.tech I wanted a custom scrollbar on my personal site. I had tried to do it in the past, but gave up after it was not working.

Since uses.tech is open source I jumped on github, searched for scroll and found this Feb 21, 2020 2 min read

“Don’t waste your time learning everything.”

Inspired by this linkedIn post

I felt that this comment was very powerful. Here are my 2 cents.

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Moving into 2020 I have been really leaning on using purple as my theme color everywhere more and more. Its time for an update to my personal site, not just because it feels plain, not just because the cover art I am using for dev.to doesn’t fit my current card layout, but because I feel inspired and I want to.

This is what we are working with. It has been my card design for at least a year now. Its not bad but, its a bit play, doesnt fit my new cover art style, and that date is not working over top of the cover art text.

I have been really into using a deep purple lately. It is a neutral color that does not get enough respect, i.e. it’s not used as frequently and kinda stands out when used.

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It was so easy to get a professional looking navbar with just 3 lines of code. This package seriously is so usable on mobile it is ridiculous. I found this package from day-4 of the 100 days of gatsby challenge. It is by the wonderful man who brought us gatsbyjs Kyle Mathews, so you know its gotta be good.

installation is easy

npm i react-headroom

Import Headroom #

There was no instructions...

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