qu | a Ruby library for queuing and processing background jobs | Job Scheduling library

I'm trying to make my bot mention my server staff in a specific text channel when someone enters in the voice support waiting room.

Here's the script I use:

I am calculating the structure similarity profile between 2 moles using rdkit. When I am running the program in google colab (rdkit=2020.09.2 python=3.7) the program is working fine.

I am getting an error when I am running on my PC (rdkit=2021.03.2 python=3.8.5). The error is a bit strange. The dataframe contains 500 rows and the code is working only for the first 10 rows (0-9) and for later rows I am getting an error

I would like to create a loop in which the index is given by the column names of a dataframe. The idea is to select one column at a time and create a map based on the data in that column. I need i being the column name, as it identifies the name of the variable and I'll use that as part of the title of the map. However, I do not seem to be able to associate my index i to the name of the column. My code goes as follows:

I am having lookups.tf file with the below content

I have a dataframe that looks like so:

Let's say I have a data frame with spatial columns (x,y), a time column (time), and a data column (value). How can I correctly coerce this to a stars object using st_as_stars?

How can I convert the summary run on a data.frame into a data.frame itself? I need a data.frame as an output to knitr::kable in RMarkdown.

In particular I have this dataframe

I have a data.frame of galaxies and their distances (z):

Dear Community,

I am working with R and looking for trends in time series data of bilateral exports over a duration of 20 years. As the data is fluctuating a lot between the years (and in addition is not 100% reliable), I would prefer to use four-years-average data (instead of looking at every single year separately) in order to analyze how the main export partners have changed over time. I have the following dataset, called GrossExp3, covering the bilateral exports (in 1000 USD) of 15 reporter countries for all years between (1998 – 2019) to all available partner countries. It covers the following four variables: Year, ReporterName (= exporter) , PartnerName (= export destination), 'TradeValue in 1000 USD' ( = export value to the destination) The PartnerName column also includes an entry, called “All”, which is the total sum of all exports for each year by reporter.

Here is the summary of my data