Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python’s design philosophy emphasizes code readability with its notable use of significant white-space. Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming.
Let’s run Jupyter notebooks in a Visual Studio Code development container, so we keep our host system clean and our development setup replicable. We’re building a scraper, so let’s add support for Puppeteer (pyppeteer) as well!
To make a setup more resilient we could allow for actions to be retried when they fail. We should not “hammer” our underlaying systems, so it is wise to wait a bit before retrying (exponential backoff). Let’s see how something like this could be done in Python. Note: this only works if actions are idempotent and you can afford to wait.
At Wehkamp we use Redis a lot. It is fast, available and implemented as a managed AWS service called ElastiCache. Sometimes we need to extract data from Redis, and usually I use the redis-cli to interact from the command-line. But what if you need to get the values of 400k+ keys? What would you do? Is there an effective way to query multiple key/values from Redis?
Let’s see if we can use PIL to crop model images and resize them to a 2:3 ratio using Python Image Library (PIL). When all images on an overview are the same ratio, the overview looks way nicer. And… let’s try to make the model on the image, the center of the image.
When working with images in a Python notebook I like to visualize them on a grid. Just calling display is not enough as it renders the images underneath each other. Let’s use Matplotlib to generate a single image with an image grid on it.
I imagine your first thought is: why? Well, at Wehkamp we do a lot of cross platform development, but sometimes we end up with shell scripts that do stuff with Docker and Python. Usually that’s not a problem for Mac, but for Windows it’s a different thing. I have a MacBook Pro, but I’m a .NET developer, that’s why I prefer Windows, so I run Bootcamp. This article will show how to do Python development in the Windows Subsystem for Linux (WSL) using Visual Studio Code and Docker.
Last week I was working on a Databricks script that needed to produce a Slack message as its final outcome. I lifted some code that used a Slack client that was PIP-installed. Unfortunately, I could not use the package on my cluster. Fortunately, the Slack API is so simple, that you don’t really need a package to post a simple message to a channel. In this blog I’ll show you the simplest way of producing awesome messages in Slack.
As an engineer, I love to parametrise my applications. That’s why I love the widget-feature of Databricks notebooks, which allows me to do this with a nice UI. In this blog I’ll explore how to build a True/False widget and a list widget. I also show how to validate the values of required fields.
At Wehkamp we use AWS Lambda to classify images on S3. The Lambda is triggered when a new image is uploaded to the S3 bucket. Currently we have over 6.400.000 images in the bucket. Now we would like to run the Lambda for all images of the bucket. In this blog I’ll show how we did this with a Python 3.6 script.
At Wehkamp we’ve been using machine learning for a while now. We’re training models in Databricks (Spark) and Keras. This produces a Keras file that we use to make the actual predictions. Training is one thing, but getting them to production is quite another!
The main problem we’ve faced was that it was too big to actually fit into a lambda. This blogs shows how we’ve dealt with that problem.