The Django shell provides an environment where developers can interact with the database via Django's ORM. While the shell is great for basic interactions, it quickly becomes laborious to work in, be it due to manual imports, having to scroll through shell history to repeat commands, or working with / viewing queries returning more than, say, 20 records. These issues, and more, can be remedied by interacting with the ORM in Jupyter notebooks, using Pandas.

In Part 1 of this post, I described the trials, tribulations and ultimate success of running Ansible to provision a cloud server, which we do often here at Imaginary Landscape ...

Here at Imaginary Landscape, we are frequently tasked with spinning up new cloud servers for our clients.  Once a new server is online, we usually follow up by provisioning ...

The Introduction

This is the third and final part of a multi-part tutorial covering a simple(ish) setup of a continuous integration/deployment pipeline using Drone.io:0.5. Since ...

Like eating Brussels sprouts, everyone knows that keeping software current is good for you. But getting clients to actually upgrade regularly is hard. Why is that?

Presenting a new open source plugin: The Django Version Viewer!

Imaginary Landscape is pleased to present a helpful new plugin for the Django Web Framework - The Django Version Viewer!

If ...

The Introduction

This is the second part of a multi-part tutorial covering a simple(ish) setup of a continuous integration/deployment pipeline using Drone.io. Since Part 1, I’ve added ...

The Introduction

This is the first part of a multi-part tutorial covering a simple(ish) setup of a continuous integration/deployment pipeline using Drone.io. In Part 1 we will set ...

The recent buzz surrounding Functional Programming (FP for short) is well deserved. Languages like Haskell and Clojure in use by big name companies such as Barclays Captial, Atlassian, and ...