Posts tagged: data

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...

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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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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

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...

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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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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...

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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See all of my kedro related posts in [[ tag/kedro ]].

I am tweeting out most of these snippets as I add them, you can find them all here #kedrotips.

Below are some quick snippets/notes for when using kedro to build data pipelines. So far I am just compiling snippets. Eventually I will create several posts on kedro. These are mostly things that I use In my everyday with kedro. Some are a bit more essoteric. Some are helpful when writing production code, some are useful more usefule for exploration.

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Good for method chaining, i.e. adding more methods or filters without assigning a new variable.

# is skus.query('AVAILABILITY == " AVAILABLE"') # is not skus.query('AVAILABILITY != " AVAILABLE"')

masking #

general purpose, this is probably the most common method you see in training/examples

# is skus[skus['AVAILABILITY'] == 'AVAILABLE'] # is not skus[~skus['AVAILABILITY'] == 'AVAILABLE']

isin #

capable of including multiple strings to include

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If you are a regular listener of TalkPython or PythonBytes you have hear Michael Kennedy talk about Named Tuples many times, but what are they and how do they fit into my data science workflow.

As you graduate your scripts into modules and libraries you might start to notice that you need to pass a lot of data around to all of the functions that you have created. For example if you are running some analysis utilizing sales, inventory, and pricing data. You may need to calculate total revenue, inventory on hand. You may need to pass these data sets into various models to drive production or pricing based on predicted volumes.

Here we setup functions that can load data from the sales database. Assume that we also have similar...

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This post is intended as an extension/update from background tasks in python. I started using background the week that Kenneth Reitz released it. It takes away so much boilerplate from running background tasks that I use it in more places than I probably should. After taking a look at that post today, I wanted to put a better data science example in here to help folks get started.

This post is intended as an extension/update from background tasks in python. I started using background the week that Kenneth Reitz released it. It takes away so much boilerplate from running background tasks that I use it in more places than I probably should. After taking a look at that post today, I wanted to put a better data science example in here to help folks get started.

I use it in more places than I probably should

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I commonly have a need to paste the first few lines of a dataset into a markdown file. I use two handy packages to do this, tabulate and pyperclip. Lets say I have a Pandas DataFrame in memory as df already. All I would need to do to convert the first 5 rows to markdown and copy it to the clipboard is the following.

from tabulate import tabulate import pyperclip md = tabulate.tabulate(df.head(), df.columns, tablefmt='pipe') pyperclip.copy(md)

This is a super handy snippet that I use a lot. Folks really appreciate it when they can see a sample of the data without opening the entire file.

I have tried most of the different methods in the past and found that copying and pasting the threadpoolexecutor example or the processpoolexecutor example from the standard library documentation to be the most reliable. Since this is often something that I stuff in the back of a utility module of a library it is not something that I write often enough to be familiar with, which makes it both hard to write and hard to read and debug. If you are looking for a good overview of the difference concurrency Raymond Hettinger has a great talk about the difference between the various different methods, when to use them and why.

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