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/4374#issuecomment-680138594,https://api.github.com/repos/pydata/xarray/issues/4374,680138594,MDEyOklzc3VlQ29tbWVudDY4MDEzODU5NA==,1478822,2020-08-25T16:39:26Z,2020-08-25T16:39:26Z,NONE,"I stand corrected. I missed that flag in the open_dataset() method.
I appreciate the edification.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,685613931
https://github.com/pydata/xarray/issues/1334#issuecomment-289790484,https://api.github.com/repos/pydata/xarray/issues/1334,289790484,MDEyOklzc3VlQ29tbWVudDI4OTc5MDQ4NA==,1478822,2017-03-28T14:36:05Z,2017-03-28T14:36:05Z,NONE,"This is true. I find that the Pandas creators assume too many things about the user. Not wanting to imply dumbing down, but users in science come to a module with a specific problem in mind, not to learn the module from scratch. Most of us are quick learners that can handle a steep learning curve if the docs/examples are rich in relevant info (intensive), which need not be long and drawn out. It would have been a better assumption to think that users such as myself are familiar with netCDF4, HDF4, Numpy, rather than the younger modules such as dask, xarray, and pandas, and draw associations from there.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,217485308
https://github.com/pydata/xarray/issues/1334#issuecomment-289746180,https://api.github.com/repos/pydata/xarray/issues/1334,289746180,MDEyOklzc3VlQ29tbWVudDI4OTc0NjE4MA==,1478822,2017-03-28T11:54:28Z,2017-03-28T11:54:28Z,NONE,"Ok. Concrete examples goes a far way. Maybe including this example into the docs might clarify how a user might access the data. I will use xr in the later stages of my work to analyse the data.
Appreciate the clarification :-) ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,217485308
https://github.com/pydata/xarray/issues/1334#issuecomment-289710102,https://api.github.com/repos/pydata/xarray/issues/1334,289710102,MDEyOklzc3VlQ29tbWVudDI4OTcxMDEwMg==,1478822,2017-03-28T09:10:42Z,2017-03-28T09:12:05Z,NONE,"apologies for these rookie questions. What I want is to access the array [0,1,2,3....] as integers, for example. Hope this is a bit clearer. I'm just checking if xarray can be use within my processing chain. Perhaps xr is it something more to plot data, and the objects within xr are only capable of manipulation only within a limited framework.
You can close this issue. I can ask this on stackoverflow as this is not truly a bug or the like. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,217485308
https://github.com/pydata/xarray/issues/1334#issuecomment-289707126,https://api.github.com/repos/pydata/xarray/issues/1334,289707126,MDEyOklzc3VlQ29tbWVudDI4OTcwNzEyNg==,1478822,2017-03-28T08:59:00Z,2017-03-28T08:59:00Z,NONE,"Aha! I see, but how do I access the data as an integer? I apologise, I'm new and it is not clear from the link how to access this as standard types:
hour.time
array(1167620400000000000, dtype='datetime64[ns]')
Coordinates:
time datetime64[ns] 2007-01-01T03:00:00
Attributes:
standard_name: time
long_name: time
axis: T
hour.time give the same as above.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,217485308