html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4610#issuecomment-1433686861,https://api.github.com/repos/pydata/xarray/issues/4610,1433686861,IC_kwDOAMm_X85VdFNN,14371165,2023-02-16T20:39:54Z,2023-02-16T20:39:54Z,MEMBER,"Nice, I was looking at the real example too, `Temp_url = 'http://apdrc.soest.hawaii.edu:80/dods/public_data/WOA/WOA13/5_deg/annual/temp' etc..`, and it was triggering a load in set_dims:
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,750985364
https://github.com/pydata/xarray/issues/4610#issuecomment-1433670641,https://api.github.com/repos/pydata/xarray/issues/4610,1433670641,IC_kwDOAMm_X85VdBPx,14371165,2023-02-16T20:24:51Z,2023-02-16T20:25:36Z,MEMBER,"> * Absolute speed of xhistogram appears to be 3-4x higher, and that's using `numpy_groupies` in flox. Possibly flox could be faster if using numba but not sure yet.
Could you show the example that's this slow, @TomNicholas ? So I can play around with it too.
One thing I noticed in your notebook is that you haven't used `chunks={}` on the open_dataset. Which seems to trigger data loading on strange places in xarray (places that calls self.data), but I'm not sure this is your actual problem.","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,750985364
https://github.com/pydata/xarray/issues/4610#issuecomment-846418243,https://api.github.com/repos/pydata/xarray/issues/4610,846418243,MDEyOklzc3VlQ29tbWVudDg0NjQxODI0Mw==,14371165,2021-05-22T14:46:13Z,2021-05-22T14:46:13Z,MEMBER,"> but `pyplot.hist` does both the bincounting and the plotting, so it might not be simple to do that.
Should be fine I think. Matplolib explains how to use `np.histogram`-like results in the weights-parameter: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html
```python
counts, bins = np.histogram(data)
plt.hist(bins[:-1], bins, weights=counts)
```
Some reading if wanting to do the plot by hand:
https://stackoverflow.com/questions/5328556/histogram-matplotlib
https://stackoverflow.com/questions/33203645/how-to-plot-a-histogram-using-matplotlib-in-python-with-a-list-of-data","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,750985364