This is a simple repl integration for the helix editor. It relies on on tmux to show the repl in a separate pane and on jupyter (jupyter-console and whatever jupyter kernels you might like) to run the actual repl.
This document is a simple resource to create sphinx documentation for projects with python(or not). This isn't exhaustive, but it'll help us get started without much hassle, (which, is typical for sphinx generated documenting)
For better results, ensure that you're inside a virtual env. This document assumes you're in a Pipenv based environment. Update it later to include other python package management tools (hopefully poetry)
The dplyr package in R makes data wrangling significantly easier.
The beauty of dplyr is that, by design, the options available are limited.
Specifically, a set of key verbs form the core of the package.
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).
dplyr is organised around six key verbs:
Because pointers can be ugh
To understand a pointer, let's review "regular" variables first. If you're familiar with a programming language without pointers like JavaScript, this is what you think when you hear "variable".
When declaring a variable by identifier (or name), the variable is synonymous with its value.