Hi Pangeo members,

This is my first time posting question after getting many useful help in the forum. I want to take this chance to quickly thanks all the useful discussions and helpful people on this platform.

So my question is “Is there a more “xarray-way” to reshape a time series into a 2D matrix with dimension1 = year and dimension2=dayofyear”. My ultimate goal is to visualize the time series not in a line plot but a 2D mesh plot that have the year in x-axis and dayofyear in y-axis.

The “non-xarray-way” that works is to inefficiently loop through all time label and find the corresponding year and dayofyear to put in the 2D array below.

```
da_heatmap = xr.DataArray(coords={'dayofyear':range(1,366+1), 'year':range(syear,fyear+1)}, dims=['dayofyear','year'])
```

I feel like groupby is doing this kind of operation under the hood. However, I am having a hard time to utilize the groupby method since it is usually bundled with a reduction operation.

I know that stack and unstack is also a possible method to achieve my goal. However, to unstack my time series I will need to create a multi-index coordinate that has the corresponding year and dayofyear value. I am currently failed to find a efficient way to do so. Instead, I have to again loop through all time label to find the same year and dayofyear value.

I hope this question is not too-trivial since I cannot seem to find any discussion on this topic so far through google or search in the forum (high chance that this is too-trivial or no one is doing this operation…). Or maybe there is just that I not using the right keyword so far. Thank you!