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/4235#issuecomment-663157223,https://api.github.com/repos/pydata/xarray/issues/4235,663157223,MDEyOklzc3VlQ29tbWVudDY2MzE1NzIyMw==,8419157,2020-07-23T18:15:33Z,2020-07-23T18:18:24Z,NONE,"To give a bit more context, an example code simulating an experimental measurement. ``` import numpy as np import xarray as xr from scipy.stats import norm from matplotlib import pyplot as plt # nominal x x = xr.DataArray(np.arange(-10, 10, 0.1), dims='index', name='x') # Shift loc loc = xr.DataArray(np.arange(-0.5, 1, 0.5), dims='loc', name='loc') # Number of experiments exp = xr.DataArray(range(3), dims='exp', name='exp') # Add noise to x per experiment noise = xr.DataArray(np.random.rand(len(x), len(loc)), coords={'loc': loc}, dims=['index', 'loc']) x = x + noise * 0.5 # Measure y = xr.apply_ufunc( norm.pdf, x, x['loc'], 1, input_core_dims=[['index'], [], []], output_core_dims=[['index']], vectorize=True ) # Name x.name = 'x' y.name = 'y' # Merge data = xr.merge([x, y]) ``` I wish to be able to use this ``` # Plot y against x data.plot.line(x='x', y='y', hue='loc') ``` However, the closest I could get is with only `y` ``` y.plot.line(x='index', hue='loc') ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,659142025 https://github.com/pydata/xarray/issues/3985#issuecomment-650800165,https://api.github.com/repos/pydata/xarray/issues/3985,650800165,MDEyOklzc3VlQ29tbWVudDY1MDgwMDE2NQ==,8419157,2020-06-28T17:53:49Z,2020-06-28T17:53:49Z,NONE,"Sorry for the late reply. I have been using this function in my projects and as such it is minimum functional. However, I will try to investigate a simpler example that replicate the issue. Lastly, perhaps you have a better idea for groupby over multi-dimension without stacking the dimensions?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,603309899 https://github.com/pydata/xarray/issues/3985#issuecomment-616650521,https://api.github.com/repos/pydata/xarray/issues/3985,616650521,MDEyOklzc3VlQ29tbWVudDYxNjY1MDUyMQ==,8419157,2020-04-20T16:05:02Z,2020-04-20T16:05:02Z,NONE,Using `git bisect` I am able to narrow down to this PR #3807 that introduced the regression.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,603309899 https://github.com/pydata/xarray/issues/3763#issuecomment-583874997,https://api.github.com/repos/pydata/xarray/issues/3763,583874997,MDEyOklzc3VlQ29tbWVudDU4Mzg3NDk5Nw==,8419157,2020-02-09T18:07:00Z,2020-02-09T18:07:00Z,NONE,"I suggest that in order to convince xarrsy developers to help you is to provide an example data and show what you have tried with your string encoding solution and describe applications for the method. You should check out pandas which xarrsy extends and is more widely used then xarray. Hopefully someone have a similar problem as you with pandas and you can write here how to apply their solutions. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,562075354 https://github.com/pydata/xarray/issues/3763#issuecomment-583840504,https://api.github.com/repos/pydata/xarray/issues/3763,583840504,MDEyOklzc3VlQ29tbWVudDU4Mzg0MDUwNA==,8419157,2020-02-09T12:39:08Z,2020-02-09T12:39:08Z,NONE,"Sounds like a technique in data science, [encoding strings](https://towardsdatascience.com/categorical-encoding-using-label-encoding-and-one-hot-encoder-911ef77fb5bd), which is actually number of different techniques.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,562075354 https://github.com/pydata/xarray/issues/3715#issuecomment-577589279,https://api.github.com/repos/pydata/xarray/issues/3715,577589279,MDEyOklzc3VlQ29tbWVudDU3NzU4OTI3OQ==,8419157,2020-01-23T09:04:19Z,2020-01-23T09:04:19Z,NONE,I could raise an issue on scipy's end and see what they says.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,553672182 https://github.com/pydata/xarray/pull/3115#issuecomment-526635815,https://api.github.com/repos/pydata/xarray/issues/3115,526635815,MDEyOklzc3VlQ29tbWVudDUyNjYzNTgxNQ==,8419157,2019-08-30T15:02:45Z,2019-08-30T15:02:45Z,NONE,Would it be possible to add exclude_dims? I have a use case where I wanted to apply a function (for example fitting) along a dimension. I programmatically create a list of dimensions to stack to create 2D dataset. Then I loop over the stacked dimension and apply the function to last dimension.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,467767771 https://github.com/pydata/xarray/issues/2029#issuecomment-451731920,https://api.github.com/repos/pydata/xarray/issues/2029,451731920,MDEyOklzc3VlQ29tbWVudDQ1MTczMTkyMA==,8419157,2019-01-06T10:46:39Z,2019-01-06T10:46:39Z,NONE,"I assumed that using a `with` statement I have loaded the data into memory and closing the file. ``` with xr.open_dataset(path) as ds: data = ds ``` However this does not load into memory entirely and so lazy loading is still in effect. So using `.load` does what I wanted ``` with xr.open_dataset(path) as ds: data = ds.load() ```","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,309949357