A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
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:
(This post was motivated by a talk by @jnolis at CascadiaRConf 2021)
Recent versions of Shiny have an undocumented feature for handling POST requests that are not associated with any specific Shiny session. (Note that this functionality is missing our normal level of polish; it's a fairly low-level hook that I needed to make some things possible, but doesn't make anything easy.)
In a nutshell, it works by replacing your traditional ui
object with a function(req)
, and then marking that function with an attribute indicating that it knows how to handle both GET
and POST
:
library(shiny)