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As usual while listening to python bytes 181 [1] 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 Interrogate # [2] It runs documentation coverage for your python project. It allows you to set the minimum amount of docstring coverage for your project and has some great setup instructions right in the readme. Install it # [3] Interrogate is on pypi so it is super simple to install with pip pip install interrogate run it # [4] This is the best part, its super easy to run right from the command line! Just call it, and give it a path to run. interrogate -v <path> 😲 I hav...

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. Example # [1] I’d say 50% of what I need is just a full-width horizontal bar to give some visual flair or separation. [2] Bars # [3] ― ⍽ ⎸ ⎹ ␣ ─ ━ │ ┃ Square Corners # [4] ┌ ┍ ┎ ┏ ┐ ┑ ┒ ┓ └ ┕ ┖ ┗ ┘ ┙ ┚ ┛ Round Corners # [5] ╭ ╮ ╯ ╰ ╱ ╲ ╳ Harpoons # [6] ⃑ ⃬ ⃭ ↼ ↽ ↾ ↿ ⇀ ⇁ ⇂ ⇃ ⇋ ⇌ ⥊ ⥋ ⥌ ⥍ ⥎ ⥏ ⥐ ⥑ ⥒ ⥓ ⥔ ⥕ ⥖ ⥗ ⥘ ⥙ ⥚ ⥛ ⥜ ⥝ ⥞ ⥟ ⥠ ⥡ ⥢ ⥣ ⥤ ⥥ ⥦ ⥧ ⥨ ⥩ ⥪ ⥫ ⥬ ⥭ ⥮ ⥯ Double Boxes # [7] ═ ║ ╒ ╓ ╔ ╕ ╖ ╗ ╘ ╙ ╚ ╛ ╜ ╝ ╞ ╟ ╠ ╡ ╢ ╣ ╤ ╥ ╦ ╧ ╨ ╩ ╪ ╫ ╬ Dashed Boxes # [8] ┄ ┅ ┆ ┇ ┈ ┉ ┊ ┋╌ ╍ ╎ ╏ Connectors # [9] ├ ┝ ┞ ┟ ┠ ┡ ┢ ┣ ┤ ┥ ┦ ┧ ┨ ┩ ┪ ┫ ┬ ┭ ┮ ┯ ┰ ┱ ┲ ┳ ┴ ┵ ┶ ┷ ┸ ┹ ┺ ┻ ┼ ┽ ┾ ┿ ╀ ╁ ╂ ╃ ╄ ╅ ╆ ╇ ╈ ╉ ╊ ╋ Others # [10] ☐ ☑ ☒ ⫍ ⫎ ⮹ ⮽...

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 [1] If you are completely unsure what kedro is be sure to check out my what is kedro post Docs # [2] a w...

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. Must Haves # [1] - check that inputs exist or are of a type to skip (sql) Good to haves - check that all input and output databases are accessible with good credentials - check for s3 bucket access - check for spark install Implementation # [2] @hook_spec def before_pipeline_run(run_params, pipeline, catalog): run params # [3] { "run_id": str "project_path": str, "env": str, "kedro_version": str, "tags": Optional[List[str]], "from_nodes": Optional[List[str]], "to_nodes": Optional[List[str]], "node_names": Optional[List[str]], "from_inputs": Optional[List[str]], "load_versions": Optional[List[str]], "pipeline_name": str, "extra_params": Optional[Dict[str, Any]] } References: [1]: #must-haves [2]: #implementation [3]: #run-params

git remote -v git remote add gitlab <url> git push gitlab main

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. [1] [2] [3] [4] # [5] 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. When working on larger pipeline projects, it is advisable to break your project down into different sub-modules which requires knowledge of building python libraries, and knowing how to import each module correctly. While this is not too difficult, in some cases, it can trip up even the most se...

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. [1] # [2] 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. [3] # [4] 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 ...

Inspired by Chris Achard My ideas # [1] Python # [2] - List comps - Classes - Inheritance - Background - Click - Lambdas Kedro # [3] - Cataloging - Custom datasets - Reusable pipelines - find-kedro Learn kedro in 5 days # [4] Email course inspired by learn d3 in 5 days Mail # [5] - Share your knowledge - Practice - Practice in public - Make practice easy - Share your notes - Digital Gardening - Own your content - Build your audience - Be nice - Have empathy - Learn your way - Continuous learning References: [1]: #my-ideas [2]: #python [3]: #kedro [4]: #learn-kedro-in-5-days [5]: #mail

This past week I had a really weird bug in my kedro [1] 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. [2] # [3] 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 [1] framework a bit less. I have dynamically created lists of pipeline nodes many times in the past, but typically I take data from kedro [1] input and use it in the lambda. I prefer the simplicity of using lambdas over functools.partial. It typically looks something like this. #...

Is it possible to deep watch a single python function for changes? Shallow Watch # [1] 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. import inspect import hashlib def test(): return "hello" [ins] In [17]: m.update(inspect.getsource(test).encode()) [ins] In [18]: m Out[18]: <sha256 HASH object @ 0x7f7b7b70fde0> [ins] In [19]: m.hexdigest() Out[19]: '1f2ff4c69eb69b545469686edd6f849136e104cd535785891586d90620328757' [i...