CLImate | Tools for making Python command-line apps

I'm aiming to reproduce an animated figure by Ed Hawkins on climate change in R with gganimate. The figure is called climate spiral. While a static ggplot figure shows the correct order of lines by year (the most recent data on top), the animated plot with transition_reveal() results in a wrong order of the lines.

Here is a reproducible example code with synthetic data:

I'm using the excellent package ggrepel() to position text labels on bars with a bit of jitter. The only trouble is that they are appearing in reverse order. Here's some reproducible code:

I am trying to make an interaction plot in sjPlot showing percent probabiliites of my outcome under two conditions of my predictive variable. Everything works perfectly, except the show.values = T and sort.est = T arguments, which don't seem to do anything. Is there a way to get this to work? Or, if not, how can I extract the dataframe sjPlot is using to create this figure? Looking for some way to either label or tabulate the displayed probability values. Thank you!

Here is some example data and what I have so far:

I'm using Scrapy and I'm having some problems while loop through a link.

I'm scraping the majority of information from one single page except one which points to another page.

There are 10 articles on each page. For each article I have to get the abstract which is on a second page. The correspondence between articles and abstracts is 1:1.

Here the divsection I'm using to scrape the data:

I am looking to do something similar to what I did in R (below) in SQL:

I am working with NASA-NEX-GDDP CMIP6 data. I currently have working code that individually opens and slices each file, however it takes days to download one variable for all model outputs and scenarios. My goal is to have all temperature and precipitation data for all models outputs and scenarios then apply climate indicators and make an ensemble with xclim.

I have a table of the following format with just one column. There are around 700 entries in total, here are 5 samples:

I have a number of coordinates (roughly 20000) for which I need to extract data from a number of NetCDF files each comes roughly with 30000 timesteps (future climate scenarios). Using the solution here is not efficient and the reason is the time spent at each i,j to convert "dsloc" to "dataframe" (look at the code below). ** an example NetCDF file could be download from here **

I have a tsv file with the following values :