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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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