xarray | N-D labeled arrays and datasets in Python | Dataset library

I have a "dataarray" which I am trying to reproject it using rioxarray. However after reprojection when I use xarray.to_netcdf, the saved file is a dataset where the "spatial_ref" coordinate converted to a variable. I am not sure if that is xarray or rioxarray.reprojection causes this behavior. Here are some codes that show the issue:

Is there a simple way of getting an array of xyz values (i.e. an array of 3 cols and nrows = number of pixels) from an xarray dataset? Something like what we get from the rasterToPoints function in R.

I'm opening a netcdf file with values for a certain variable (chl). I'm not able to add images here directly, but here is a screenshot of the output:

Xarray dataset structure

I need to end with an array that have this structure:

Assume I have two variables in a dataset in xarray format as follow:

Physical Background

I'm working on a function that calculates some metrics for each vertical profile in an up to four dimensional temperature field (time, longitude, latitude, pressure as height measure). I have a working function that takes the pressure and temperature at a single location and returns the metrics (tropopause information). I want to wrap it with a function that applies it to every vertical profile in the data passed.

Technical Description of the Problem

I want my function to apply another function to every 1D array corresponding to the last dimension in my N-dimensional array, where N <= 4. So I need an efficient loop over all dimensions but the last one without knowing the number of dimensions beforehand.

Why I Open a New Question

I am aware of several questions (e.g., iterating over some dimensions of a ndarray, Iterating over the last dimensions of a numpy array, Iterating over 3D numpy using one dimension as iterator remaining dimensions in the loop, Iterating over a numpy matrix with unknown dimension) asking how to iterate over a specific dimension or how to iterate over an array with unknown dimensions. The combination of these two problems is new as far as I know. Using numpy.nditer for example I haven't found out how to exclude just the last dimension regardless of the number of dimensions left.

EDIT

I tried to do a minimal, reproducible example:

My dataset has 3 dimensions in the order (time, y, x) and I use apply_ufunc to apply a computation along the time dimension. This rearranges the order of the dimensions as (y, x, time). I need to restructure the xarray so its in the (time, y, x) order as the original dataset. How would I go along doing this?

Here is a visual description of what's happening:

Before:

Then I apply my function:

I'm trying to reorganise my data (the overarching goal is to convert a ASCII file to netCDF). One of the steps to get there is to take the data and stack the columns. My original data look like this:

I was wondering if it's possible to plot all the steps from this single netcdf file into a separate plots.

Step 113 means that the current accessed data is for the date of October 22,2019. The Step 0 is July 1,2019. There are 135 time steps overall. Which means I need to produce 135 maps for each and single day.

I am trying to rename a variable in some netcdf files. I am able to accomplish this using xr.Dataset.rename(). However, when I write that out to a netcdf file it has the old variable name that I was trying to replace. Here is the output from the original:

I have a 3-dimensional xarray DataArray of changes in surface temperature with coordinates of time, lat and lon. I am visualizing the data using Cartopy. You can find the 125 MB file here.

While producing plots of time-averages over different periods, I've found that I'm unable to produce orthographic projections when including certain time steps, such as the 132nd (index 131) time. Here is a plot of the time average from 0 to 130:

But this happens when I instead perform the time average from 0 to 131:

Here is the code I used to produce the plots: